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Abstract: Applying the precautionary principle to manufactured nanoparticles requires a full risk assessment throughout the life cycle of nanomaterial-containing products. We propose a pragmatic methodology for the systematic identification of knowledge gaps and of priority starting points for exposure assessment as one step of a risk assessment. Its feasibility and usefulness are demonstrated for the post-use phase of automobiles and paper products. These starting points include, for example, automobile recycling facilities or factories using large amounts of recovered paper. Our methodology is transferable to other life cycle phases and could also be applied to selected nanomaterials or to products still being developed. [Copyright &y& Elsevier]

This paper aims to contribute to both theory and practice by describing current problem-solving practices, proposed by various authors, as part of the quality management process of companies in the automotive industry. The paper, based on proposals from different authors, also includes a set of quality tools that can be used in the different steps of the problem-solving process. The results show that there is general agreement on best problem-solving practices, emphasizing root-cause analysis and the development and implementation of corrective actions, which are included in all the proposals analyzed. The analysis also shows that many quality tools can be used in the different steps of this process. In addition, the paper proposes an initial framework developed by the authors that can be used by organizations to improve their problem-solving practices. [ABSTRACT FROM AUTHOR]

Continuing technical vocational education and training (TVET) and professional development of the entire automotive sector towards implementing Industry 4.0 and 5.0 tools, methods and technologies has become an essential component of today's knowledge society. The principle of lifelong learning permeates all areas of professional and private life and has manifested itself as an essential component of today's knowledge society. Especially the automotive sector is challenged to successfully master the green and digital transformation. This needs engineers with expertise to actively shape the dual transformation. In this context, continuing education and training on a postgraduate level is indispensable. This paper illustrates continuing technical vocational education and training at Graz University of Technology, managed by the Life Long Learning organizational unit. As an example, a course concept is described in more detail on the basis of the university course Automotive Mechatronics. The aim of this paper is to present a successful TVET program and to show how postgraduate education over the lifespan can succeed sustainably for engineering professionals in the automotive sector. [ABSTRACT FROM AUTHOR]

The automotive industry has to deal with wide-reaching challenges and changes. A continuous gain of significant competitive advantages in a highly competitive market requires automotive manufacturers to produce new products with a large diversity of variants at ever-shorter time intervals. As a result, the frequency of series launches has increased and the need for a sophisticated vehicle project management and an efficient handling of vehicle changes and model updates along the product process have gained significant importance. However, current project management practices and frameworks utilized by automotive manufacturers do not effectively address this demand, e.g., the goals of increasing operational efficiency and productivity as well as establishing continuous improvement are not sufficiently achieved. While these goals are traditionally common-place in certain fields of the automotive manufacturing, e.g., in production, logistics, or warehousing, their dedicated consideration in the vehicle project management or one of its neighboring areas like the vehicle change management has not been examined thoroughly in the required extend. Understanding the determinants of the needed change in the vehicle project management and its related fields is a central objective of the research presented in this paper. In this context, this paper proposes a novel approach on how to manage the transition and why this is a challenge to common project management practices utilized traditionally in the automotive industry. [ABSTRACT FROM AUTHOR]

This paper proposes a novel curve called the α - curve , which is specified by a variable shape parameter α and can be applied in curve reconstruction involving curve fairing, fitting, and segmentation. In contrast to classical log-aesthetic curves in which α is constant, this study contributes a novel formulation of α that varies monotonically within a specified segment. By introducing the variable α , the logarithmic curvature graph (LCG) becomes piecewise linear and parabolic, or any other function if necessary. Consequently, in comparison to conventional log-aesthetic curves that have limited field usage based on their rigidity, α -curves have various LCGs and flexible representations, thereby opening up many practical applications. Experimental results and comparative studies on curves created by CAD experts in the automotive industry demonstrate the theoretical and practical validity of α -curves. [Display omitted] • α -Curves are proposed by extending the mathematical framework of log-aesthetic curves • An α -curve is specified by a variable shape parameter • α -Curves have various log-curvature graphs (LCG) and flexible representations • α -Curves are applied to a new fairing, avoiding conventional issues of side effects [ABSTRACT FROM AUTHOR]

This paper deals with the issue of safety lead time (SLT) calculations in production-inventory systems in presence of both demand and replenishment lead time variability. We provide some formulations of the SLT and numerically show their performance as compared to a benchmark in the literature. Thus the main objective of this paper is to provide analytical formulations to calculate the SLT that contemplate demand variability. To this end, a literature review was done to analyze the approaches and justifications of the different revised research works to identify reference formulations according to the objectives of this work. A supply chain from the automotive sector was used as the study frame and to validate the proposed formulations. This supply chain involved two companies: a car manufacturer and a first-tier supplier. In order to compare the proposed formulations with one another, and with that currently used by the first-tier supplier and is the study object, three parameters were used: safety stock, the number of times stockout occurs and the mean stock. They allowed the final selection of the most suitable SLT formulation for each case study. [ABSTRACT FROM AUTHOR]

Vision-based driving assistance systems has made a paradigm shift in the automotive industry, especially in auto electronics, towards vehicular technology in smart cities. This technology concentrates on safeguarding the members inside and outside the car, such as pedestrians or other vehicles on the road. This paper proposes a methodology that employs a single image/frame-based rain filter for video processing. In this paper, an innovative method of frame filtering is statistically modelled to provide a clear vision on the road during rainy weather. The frame filtering displays an improvement by considering a reference frame as the input frame and it is further exploited to confiscate the contextual and atmospheric particles using the method of frame difference. Moreover, this article suggests a hybrid algorithm that accomplishes filtering utilizing L0-gradient image smoothing and weights least square smoothing along with adaptive histogram equalization to preserve and enhance the image under intense rainy conditions. With a single frame-based filtering methodology, the proposed algorithm performs well with rainy images but loses information during video processing. The proposed method displays an improvement by 36.29 % and 3.48 % in Structural Similarity Index (SSIM) and 57.15 % and 14.47 % in Gradient Magnitude Similarity Deviation (GMSD) as compared to guided filter and bilateral filter methods. [ABSTRACT FROM AUTHOR]

Automated Fibre Placement (AFP) is receiving increasing attention from academia and industry. This is due to its widespread application in major sectors such as the automotive, marine, aerospace, renewables, and rail industry. AFP defects pose a significant barrier to efficient part throughput due to their lengthy processing time in inspection and rework. While there is a plethora of research on the causes and effects of various defect types, the subsequent reworking of these defects has not been thoroughly investigated. Although some papers have examined manual rework, their scope is limited. Insights from manual hand layup and similar methods can be applied to this context. This article presents the first comprehensive review of the academic literature in the field of defect rework, alongside an analysis of rework process experiments conducted by the authors to provide insight into current industry best practices. Following this, the article focuses on analysing the rework process in AFP and proposes guidelines and best practices. It emphasises the need for further research in this area, along with the findings of other researchers, to demystify the rework activity and inspire new investigations. Such research can offer valuable insights into enhancing reworking techniques and exploring automation possibilities. Automation can assist in manual reworking or even eliminate the need for it entirely, thereby reducing manufacturing time. The suggested directions for future work include developing automated rework processes to achieve near-full automation of AFP. [ABSTRACT FROM AUTHOR]

Classic automobiles are a crucial component of the automotive industry, representing specific eras' historical, and technological accomplishments. However, to be regarded as masterpieces, these must be conserved in mint condition or restored following strict guidelines applied by expert services. Therefore, all data about restoration processes, and other relevant information about these vehicles, must be rigorously documented to guarantee its auditability and immutability. This paper reports on our ongoing research to properly provide such features to the classic car ecosystem. We are developing a blockchain-based solution that facilitates the adequate recording of classic car information, restoration procedures applied, and all associated documentation by ensuring this data is immutable and trustworthy, while also promoting collaboration between interested entities. Our proposed solution aims to be advantageous for all automotive entities involved. Additionally, it will be feasible to assess the impact of our system and its key technology, blockchain, applied to this particular domain. [ABSTRACT FROM AUTHOR]

Effective decision-making in a temporal context is challenged by the enduring impacts of delays, where decisions made in a certain period are influenced by choices made in preceding periods. This paper delves into the analysis of decision-making in a dynamic game system characterized by delay impact on decisions. Notably, it pioneers the application of a game approach to detect the intricate relationship between car and petrol supply chains operating under conditions of dynamism and uncertainty. The marginal profit method is applied to deal with the dynamic game model. Furthermore, this article investigates the improvement of green car levels, on member profits. In the car supply chain, the car manufacturer sells two distinct product types: green and regular. In this paper, green cars refer to cars with low petrol consumption, and as gas consumption decreases, the green level of the car ascends. This study marks the first instance where optimal production capacity allocation is examined within the framework of delay and uncertainty. Considering the government's initiative to curtail petrol consumption in automobiles, the effects of the government's supporting role are also analyzed in this paper. As a result, the greenness level witnessed a 4.1-fold increase, leading to an 81.7% surge in the demand for green cars, consequently boosting the profit of the entire car supply chain. Several numerical experiments of a real case were examined to evaluate the performance of the proposed models. Finally, some sensitivity analyses were applied to the principal parameters of the model to extract managerial insights. [Display omitted] • Modeling stochastic-dynamic game under delay impact for green/regular cars. • Analyzing interactions of a car and a petrol supply chains on each other. • Proposing a model for allocating optimal capacity for green/regular cars. • Studying impact of government intervention on improving green level of cars. [ABSTRACT FROM AUTHOR]

This paper reviews the challenges faced by the global automotive industry as it transitions towards decarbonization. The article presents a comprehensive survey of hybrid, battery electric, and fuel cell electric vehicles, offering insights into their merits, drawbacks, and the potential implications they might have on consumers. It also highlights advancements in electrochemical storage devices, improvements needed to make them competitive with conventional fuels, and the role of government policies in driving adoption. Factors that influence the adoption of low- and ultra-low carbon technologies are also examined from a market perspective. The paper concludes by calling for further research, development, and collaboration to overcome challenges and fully realize the potential of these technologies. Ultimately, the adoption of low-carbon technologies is influenced by government regulations, consumer demand, technological progress, and economic considerations within the automotive industry. [Display omitted] • Automotive shift: Decarbonizing via hybrid, electric, fuel cell vehicles. • Policies, demand, tech drive low carbon auto adoption. • Battery concerns: materials, supply. solutions: recycling, renewables. • Collaboration, R&D vital for low-carbon tech success. [ABSTRACT FROM AUTHOR]

The software development complexity of automotive systems has significantly increased during the last decade due to the latest Advanced Driving Assistance System (ADAS) functionalities. To effectively address this complexity, domain specific modeling languages (DSMLs) like AUTOSAR or an open-source system performance model for AUTOSAR-aligned systems, APP4MC, have become a common trend in the automotive industry. DSMLs allow for easily capturing the functional and non-functional requirements of the system without needing to master low level details of the programming model or the processor architecture. Unfortunately, current DSMLs do not support the parallel programming models, like OpenMP and CUDA, that are used to exploit parallel heterogeneous architectures featuring acceleration devices such as GPUs and FPGAs required. These architectures are however essential to cope with the performance needs of ADAS. This exposes a gap between the DSMLs used by automotive designers to enhance software productivity and leverage verification and validation processes, and the parallel processor architectures used in this domain. This paper presents a complete framework to safely exploit the inherent parallelism exposed by the AMALTHEA system description, supported in APP4MC, by: (1) automatically transforming the high-level design into the OpenMP parallel programming model targeting both host and accelerator parallelism, and (2) using compiler analysis techniques to prove the correctness of the model transformed to OpenMP code. The paper contributes also with (3) an analysis of the parallel execution model allowed by the AMALTHEA DSML and that of OpenMP, and (4) a performance plus productivity evaluation of the proposed framework on real automotive systems executed on an embedded GPU-based processor architecture. [ABSTRACT FROM AUTHOR]

Highlights • This approach utilises multidimensional process mining for internal logistics. • The event data is used for automatically mapping physical logistics activities. • We provide a discovery, performance and conformance analysis for each process cube. • The analysis results can be used to continuously quantify different types of waste. • A case study in the automotive industry evaluates the benefits of the approach. Abstract Pen and paper-based value stream mapping (VSM) is the established tool for recording processes, identifying waste and deriving recommendations for action. However, today, its application in manufacturing industry requires a high level of effort and is challenging due to product and process complexity, as well as dynamics. To overcome these shortcomings, we are developing a methodology to apply process mining (PM) to internal logistics for a mixed-model assembly line. The methodology combines multidimensional process mining (MDPM) techniques with proven principles of lean production and VSM. Firstly, internal logistics is modelled using existing event data by automatically mapping physical logistics activities (e.g. transport, store). Secondly, to enable PM, the event data is transformed into enriched event logs. Thirdly, the MDPM approach contains (1) a discovery analysis, (2) a performance analysis and (3) a conformance analysis including a reference process classification for each individual part and process. Finally, a waste analysis and strategy for practitioners is designed to identify and prioritise wasteful parts and processes. The methodology has been applied and evaluated in a case study at a German automotive manufacturer. In the case study, we analysed 7500 parts and 15 reference processes. An analysis for each individual part and process has not been available yet. We could both identify part-specific root causes (e.g. a long lead time) and process-specific root causes (e.g. a low trace fitness). The main contribution of this paper is to provide an MDPM methodology for practitioners to enable a continuous recording, evaluation and waste analysis of each individual part and process within internal logistics. [ABSTRACT FROM AUTHOR]

Abstract Environmental issues and cleaner production are getting increasing attentions currently, making the clean production and sustainable consumption with low emissions significant. Traditional energy vehicles are increasingly unsuitable for the development of current society. It's a trend in the current vehicle market to accelerate the substitution of new energy vehicles for traditional energy vehicles. This paper makes a theoretical study on the development of new energy vehicle market supported by the government. First, this paper establishes a Cournot duopoly model which includes domestic and imported new energy vehicle manufacturers. Through the analysis of this model, this paper argues that government support policies such as subsidies and tariffs can effectively expand the market share of domestic new energy vehicles with less technology. Yet this approach is unsustainable, because domestic new energy vehicles with less technology can only survive in the market by shortening the gap with imported brands. Secondly, this paper establishes a Stackelberg model with product price as the decision variable and find that when there is a technical gap, the first mover advantage in the market will no longer exist. Finally, this paper compares two kinds of subsidies for domestic new energy vehicles and shows that the two subsidies are not different from subsidizing consumers or enterprises in expanding market share, and the consumer can obtain higher social welfare level as the subsidy object. Highlights • New energy vehicles boom with obvious substitution effect on traditional ones. • Both subsidies and tariffs can expand the production of domestic new-energy cars. • First move of domestic brands into the market can narrow the price difference. • First mover advantage no longer exists in the Stackelberg game. • Consumer subsidy is superior to enterprise subsidy in new energy auto industry. [ABSTRACT FROM AUTHOR]

Abstract The usage of modern materials in the automotive industry for the body in white construction leads to increasing requirements for the manufacturing process. Due to the limited formability of high-strength aluminium alloys and steels, joining by forming of these dissimilar materials is still a big challenge, which requires suitable and innovative manufacturing technologies. In sheet forming processes the Tailor Heat Treated Blank (THTB) technology allows enhancing the formability of high-strength aluminium alloys. Therefore, the applicability of the THTB technology for the shear-clinching technology is investigated in this paper. Up till now, this innovative joining by forming technology is only qualified to join ductile aluminium alloys and high-strength steels, but the combination of high-strength aluminium and high-strength steel has not been realized yet. In this research work, the limits of the shear-clinching process have been extended to successfully join the material combinations of AA7075 on the punch side and HCT780X, as well as press hardened 22MnB5 on the die side by using aluminium specimens with different heat treatment layouts. Further, this paper shows the possibility to adjust the material flow of the shear-clinching process by local and short-term heat treatment. [ABSTRACT FROM AUTHOR]

Abstract This paper aims to better manage the reverse supply chain of the automotive industry in the context of green, circular, and sustainable development by predicting the number of end-of-life vehicles to be recycled through the establishment of a multi-factor model. The prediction of the number of end-of-life vehicles to be recycled in this paper will support the end-of-life vehicle recycling industry in terms of recycling management and investment decision-making and provide a reference for the formulation and implementation of policies relating to end-of-life vehicles. To solve the problems posed by nonlinear characteristics and uncertainty in the number of end-of-life vehicles recycled, and deal with the multiple factors influencing the recycling number, this paper presents a combined prediction model consisting of a grey model, exponential smoothing and an artificial neural network optimized by the particle swarm optimization (PSO) algorithm. Using Shanghai's end-of-life vehicle reverse logistics industry as an example, this study selects historical data about end-of-life vehicles recycled in Shanghai during the 2005–2016 period, identifies multiple influential factors, and validates the effectiveness and feasibility of the prediction model through empirical research. This paper proposes an effective prediction model for end-of-life vehicle industry managers, researchers, and regulators dealing with the industry's common challenges. Highlights • Hybrid prediction model based on grey model and artificial neural network predicts end-of-life vehicles recycling volume. • Hybrid prediction model improves prediction performance on data featuring randomness and small sample size. • Hybrid prediction model improves prediction performance of end-of-life vehicles recycling volume affected by multi-factors. • Artificial neural network prediction model using particle swarm optimization algorithm has better prediction performance. • Model proposed in this paper is tested by application to real-life data from the reverse logistics industry in Shanghai. [ABSTRACT FROM AUTHOR]

Predictive modeling is revolutionised by the use of artificial intelligence (AI) in this paper. AdaBoost regression, a potent algorithm for machine learning, is utilised. It excels at handling complex relationships between input and output variables and making accurate predictions. AdaBoost regression is preferred due to its reliability and ability to identify informative patterns. It achieves high precision and performance despite using a small number of regressors. AdaBoost regression proves to be exceptionally effective in the optimization of process parameters. By utilizing historical data and training the model, the optimal settings for improving process outcomes are determined. Improvements in yield, increased conversion rates, and resource optimization are a few of the valuable insights and recommendations. AdaBoost regression handles multiple input variables, including blend composition, speed, temperature, and duration, allowing for extensive modeling and analysis. Variables such as viscosity, oxidation stability, flash point, and density are included in the predictions. AdaBoost regression is a reliable tool that is well-known in a variety of industries, including finance, healthcare, and manufacturing. This paper emphasises its high accuracy and dependability for making informed decisions, optimizing operations, and attaining superior performance. In conclusion, this paper demonstrates the transformative power of AI in predictive modeling through AdaBoost regression. It plays a crucial role in optimizing process parameters and driving success across a variety of applications due to its capacity to handle complex relationships and provide accurate predictions. [ABSTRACT FROM AUTHOR]

Recently, Indian manufacturing industries have undergone a significant transformation emphasising customer-centric objectives to meet the dynamic market's demands. To achieve this, existing industries must upgrade their existing infrastructure to a smart environment driven by automatic data exchange, transparency, and interconnected digital systems. These are the key enabling elements of Industry 4.0. As a result, many Indian industries are developing various sophisticated implementation strategies for adopting it. However, few research works and strategies contribute to its effective and successful implementation. This paper proposes two types of implementation strategies for implementing Industry 4.0 enabling technologies based on the requirements and adaptability of India's commercial vehicle manufacturing industry. The first strategy identifies various aspects of implementing Industry 4.0 enabling framework elements and categorizes it for developing implementation strategies based on India's commercial vehicle manufacturing industry. The significance of each was determined using fuzzy-AHP analysis and ranked accordingly based on the linguistic responses of the industry experts. The most significant framework element is found to be 'Decision Making and Management Skills'. The second strategy emphasizes long-term sustainable implementation objectives. The framework elements are reconstructed and developed into a novel, customized sustainable framework based on six dimensions of sustainability. Using the Fuzzy-DEMATEL method, the causal relationships among these elements are investigated to determine a sustainable implementation strategy. 'Central Database Server' is determined to be the most significant sustainable framework element, while the maximum number of casual relationships is obtained from the group, 'Skill upgradation of the workforce'. This paper proposes a novel research direction for implementing Industry 4.0 technologies into the existing industry. The findings provide several insights and strategies for technological upgradation in other industries in this geographical region. The findings of the work enhance the implementation process of I4.0 technologies, transitioning it from a conventional approach to a futuristic sustainable implementation that is economical and effective. • Industry 4.0 enabling framework elements for Indian manufacturing industry were identified and categorised. • Significance of each framework element are determined using Fuzzy AHP. • A customised sustainable framework of I4.0 enabling elements is presented. • Fuzzy DEMATEL approach used to examine their causal relationships and significance based on long-term sustainability. • Sustainable implementation strategies and implications are discussed. [ABSTRACT FROM AUTHOR]

• An LD closest to an incomplete FLE is computed by optimization models. • The PISs of HFLTSs in an FLPR are computed. • Consistency and consensus of an FLPR are considered. In group decision making, flexible linguistic preference relations (FLPRs) are very useful with the pairwise comparisons taking the form of flexible linguistic expressions (FLEs). Due to the fact that different decision makers have different understandings of words, this paper investigates the personalized individual semantics (PISs) of the linguistic information in FLPRs. Two optimization models are constructed to compute a linguistic distribution which is closest to an incomplete FLE. The FLPRs are transformed into fuzzy preference relations by using optimization models which maximize consistency and consensus of the fuzzy preference relations. The PISs of linguistic terms and subsets of the linguistic term set are obtained in this process. A group decision making model based on FLPRs is presented and a green supplier selection problem in automotive industry is solved by using the proposed model. The comparative analysis is presented to show the feasibility of the group decision making model. [ABSTRACT FROM AUTHOR]

In this paper, we examine a potential causal link between vehicle emission standards and local automotive industry performance, using a 42-country panel data set covering a 19-year period between 2000 and 2018. Our panel logit analysis shows that having a sizeable local automotive industry decreases the chance of adopting stringent vehicle emission standards, while the stock of automotive FDI increases the chance. This finding suggests that the primary motivations for vehicle emission standards may not be purely environmental. Our follow-up Granger causality test demonstrates that such non-environmental motivations have in fact translated into an actual industry-promotion outcome. Stricter emission standards in OECD member countries tend to promote local automotive output and international vehicle export with a 3-year time lag, while this causal link is limited to up-market exports for non-OECD panels. Automotive FDI hosts with large domestic markets and/or local-brand automakers also present a similar causal link, but in this case significant long-term industry-promotion effects appear only after a short-term negative shock. These results suggest that a regulation-induced short-term efficiency loss may be partly offset by a long-term productivity gain through advance/timely local adoption of cleaner technologies. • Vehicle emission standards involve non-environmental motivations, as well. • Local production volume and FDI stock affect the stringency of the standards. • Regulation-induced efficiency loss can be partly offset by long-term productivity gain. • Stricter standards tend to have pro-export effects with a 3-year lag. • The causal link depends on domestic market size and local-brand automakers. [ABSTRACT FROM AUTHOR]

• The fusion-bonded Single Lap Joints under four-point bending load were analysed experimentally and numerically. • The effect of the geometry of the overlap terminus was evaluated, with a special interest in the overall joint strength and failure mechanisms. • The effects of notch geometry on the joint performance were considerable. • The failure always started at tensile edge of the overlap, irrespective of the geometry of the overlap termination. The assembly of thermoplastic composites is one of the most important issues for their reliable use in the aerospace and automotive industries. Assembly through co-curing is a process that enables simultaneous bonding and curing of adherends and is a preferable option for these industries. This paper presents a study of fusion-bonded, co-cured single lap joints with thermoplastic adherends that were subjected to four-point bending. Processing of the joint created a 45° notch at one edge of the overlap. The effect of this notch on the strength of the joint was assessed in tension and compression by flipping the joint. Compared to samples without such notches, experiments indicated that the tensile stresses in the vicinity of the notch decreased the maximum load and applied displacements at failure initiation by about 30 % and 33 %, respectively. In order to better understand this behavior, the joints were also analyzed using finite element simulations. The initial failure was predicted using the Maximum Stress Theory. Both experimental and numerical results were consistent up to the initial failure. The analysis did not account for the subsequently observed interlaminar delamination in the adherends. This type of failure would most likely be approached using measured interlaminar traction-separation relations in a cohesive zone modelling approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel failure mode and effect analysis (FMEA) framework as a formal design method to ensure safety and reliability. FMEA is used to identify potential failure modes (FMs), and it is crucial to determine the weights of risk factors and prioritize FMs. In this work, we propose a comprehensive framework that integrates the Dempster-Shafer theory, best-worst method (BWM), stochastic multi-objective acceptability analysis (SMAA), and measurement of alternatives and ranking according to compromise solution (MARCOS) to address this problem. To capture the uncertainty caused by the loss of information, the Dempster-Shafer theory is applied for dealing with the uncertainty about risk factors and FMs in terms of linguistic information. Based on the comprehensive evidential preference interval vectors of risk factors constructed by Dempster-Shafer theory, an evidential BWM combined with SMAA is proposed to determine the optimal set of risk factor weights. Meanwhile, based on the constructed interval belief interval decision matrix of FMs to risk factors constructed by Dempster-Shafer theory, an evidential SMAA-MARCOS method is proposed for determining the risk priority of FMs. Further, we conduct a case study to evaluate the risk of equipment in an automobile manufacturing enterprise. A sensitivity and comparative analysis are also conducted to demonstrate the effectiveness and superiority of the proposed framework. • An evidential SMAA method is proposed to identify the best and worst risk factors. • An evidential BWM is proposed to determine the weights of risk factors. • An evidential SMAA-MARCOS method is proposed to rank FMs. • The FMEA framework using evidential BWM and SMAA-MARCOS method is proposed. [ABSTRACT FROM AUTHOR]

Green logistics is an important aspect of production system in today’s world. It combines various efforts to quantify and control the environmental effect of production activities in an enterprise. This research is intended to emphasize upon the ways by which organizations can accomplish positive ecological effects simply by modifying their logistics practices. This paper presents a case study of an Indian automobile company by analyzing alternatives for green practices along with organizational objectives. It is imperative to focus on the green practices in context of Indian automobile sector as the volume of vehicles in India has increased considerably during last few decades. Accordingly, this paper attempts to improve the performance for sustainable adaptation of green practices of an Indian company by selecting the best alternative obtained from the use of Analytical Hierarchy Process to analyze alternatives for green practices in the assembly and packaging processes. Alternatives have been evaluated on the basis of three criteria i.e. green efficiency, factor of safety and ease of operation. The study revealed that clinch joint is the most appropriate alternative for assembly operation while carbon positive packaging material is the best alternative for packaging. [ABSTRACT FROM AUTHOR]

Toothing of bevel gear is a crucial operation for the automobile industry. To achieve the required performance characteristics of the gear it is essential to optimize the toothing process. Herewith, the task of understanding the proceeding the heat processes during cutting is important to forecast of the tool wear, accuracy, and undulation of the processed surface. In the article the results of numerical simulation of thermal processes appearing during processing of bevel gear are considered. This paper presents the results of numerical modeling of thermal processes occurring in a tool, workpiece, and chips during bevel gear cutting. Also the influence of the design parameters of the cutting tool on the value of maximum temperatures and heat flows occurring in the process of cutting was investigated. The approaches which were used in that paper can also be applied to other cutting processes with the removal of a complex U and / or L shaped chips (gear shaping, broach etc.) [ABSTRACT FROM AUTHOR]

In the context of industry 4.0 and digital transformation, the predictive maintenance (PdM) approach plays an important role in the efficiency of maintenance, as breakdowns and over-maintenance. Remaining Useful Life (RUL) is the main part of the prognostic aspect of maintenance. RUL is also one key piece of information that feeds the PdM and needs to be dynamically readjusted in a global approach and procedure. In this paper, a new methodology for the prognostic aspect of PdM, and a dynamic RUL estimation method have been proposed. In this way, general methodologies and processes of prognostics have been presented that outline a more coherent vision toward RUL estimation. Within the dynamic RUL method, it is proposed to use from Prophet prediction model in a dynamic algorithm to better estimation of RUL based on the Health Indicator (HI) trends updates. The applicability and efficiency of the proposed procedure and method have been applied and validated in the conveyor chains of an automotive company which their failure results in production stoppages and significant damages. The performance of the prediction method has been presented with a comparison to deep learning and statistical prediction methods. Following the dynamic RUL estimation, a maintenance strategy has been proposed for the studied case to improve maintenance planning. [ABSTRACT FROM AUTHOR]

Aviation is among one of the fastest-growing sources of greenhouse gas emissions (GHG). Thus, transitioning into a more low-carbon approach for future sustainable growth in the airline industry is necessary. Inspired by European Union's GHG emissions trading system that allows automobile makers to "pool" their carbon emissions, we study three emission pooling scenarios for the airline industry. Under the EU emissions trading system (EU ETS), passenger and cargo airlines such as Lufthansa and FedEx can either (1) pay a premium for each ton of carbon dioxide equivalent (CO 2 eq) they fall short of free allowances (Pay Premium Strategy) or (2) buy allowances from other industries in the EU ETS market (Market-Based Strategy) (3) partnership with other airlines to "pool" their carbon emissions (Cooperative Strategy). These three approaches correspond to different market structures. The latest strategy has a co-opetition relationship between airlines: traditional airlines operating older aircraft purchase emission allowances from airlines operating newer aircraft. The purpose of this paper is to examine the conditions for airlines' paying penalties, purchasing allowances from the ETS market, or cooperation. We demonstrate that the EU ETS will always be detrimental to airlines using older aircraft. A hefty premium may effectively induce airlines to pursue a low-carbon transition plan. Our study shows that the ETS is not a silver bullet; it only works when the premium mechanism is designed properly and a fair market for emission allowances is available. • This study investigates four emission pooling models for the airline industry. • The purpose of this paper is to examine the conditions for airline cooperation. • This study's findings can be insightful for both traditional airlines and sustainable airlines. [ABSTRACT FROM AUTHOR]

Autonomous driving has been among the most popular and challenging topics in the past few years. Among all modules in autonomous driving, High-definition (HD) map has drawn lots of attention in recent years due to its high precision and informative level in localization. Since localization is a significant module for automated vehicles to navigate an unknown environment, it has immediately become one of the most critical components of autonomous driving. Big organizations like HERE, NVIDIA, and TomTom have created HD maps for different scenes and purposes for autonomous driving. However, such HD maps are not open-source and are only available for internal research or automotive companies. Even though researchers have proposed various methods to create HD maps using different types of sensor data, there are few papers that review and summarize those methods. New researchers do not have a clear insight into the current state of HD map creation methods to work on their HD map research. Due to the reason above, reviewing, classifying, comparing, and summarizing the state-of-the-art techniques for HD map creation is necessary. This paper reviews recent HD map creation methods that leverage both 2D and 3D map generation. This review introduces the concept of HD maps and their usefulness in autonomous driving and gives a detailed overview of HD map creation methods. We will also discuss the limitations of the current HD map creation methods to motivate future research. Additionally, a chronological overview is created with the most recent HD map creation methods in this paper. [ABSTRACT FROM AUTHOR]

Composites or composite materials are engineered materials that consist of two or more constituent materials with wide discrepancies in their physical, chemical, and mechanical properties. The characteristic properties of these composite are as a result of the individual properties of their constituent parts and their respective volume fractions and arrangements in the material system. Depending on the intended application, composites can be designed to satisfy specific geometrical, structural, mechanical, chemical, and sometimes aesthetic requirements. Areas of application of these synthetic materials includes construction such as in buildings and bridges, automotive industry such as in car bodies, aeronautic, naval (e.g., ships and boats), and in the biomedical fields. Although metallic, polymeric and ceramic biomaterials have been in use for medical treatments such as tissue repairs and replacements for decades, composites are just coming to light. Therefore, the main purpose of this paper is to introduce composite materials and discuss their current and potential use in the biomedical field. [ABSTRACT FROM AUTHOR]

This paper investigates why material throughput remains high in the UK automotive industry when there are opportunities for material efficiency improvements. Informed by socio-technical studies of automobility, the paper emphasises the importance of recognising how decisions regarding material use are always shaped by more than simply cost considerations. Drawing on industry interviews, six interconnected socio-technical factors are identified that guide the vehicle design and manufacturing process. These are: (1) customer preferences; (2) market positioning; (3) techno-economic feasibility; (4) supply chain feasibility; (5) regulation and (6) organisational attributes. These factors can provide insights into the current operating context of the UK automotive industry and help explain why the average material intensity of vehicles and vehicle throughput are increasing. Overall, the paper shows that the efficiency of material use in the UK automotive industry is the outcome of complex and advanced design and manufacturing processes. Understanding these processes and the factors that guide them can potentially increase the likelihood of the automotive industry adopting material efficiency initiatives. [ABSTRACT FROM AUTHOR]

This paper presents a model based approach for defining automotive functional safety requirements and provides a solution to ensure functional safety through model-based diagnosis and fault tolerant control. This model-based approach is consistent with ISO 26262 – functional safety standard. In particular, this paper presents the necessary steps for defining and implementing functional safety requirements, including item and function definition, Hazard Analysis and Risk Assessment, as well as the design of a model-based diagnostic and fault tolerant control (FTC) system that can lead to a systematic solution to automotive functional safety problems. The methodology proposed in this paper is applied to the problem of torque functional safety of pedal-by-wire systems. [ABSTRACT FROM AUTHOR]

The paper evaluates mesoscopic simulation models applied to logistics planning tasks in the automotive industry. In terms of level of detail, mesoscopic simulation models fall between object based discrete-event simulation models and flow based continuous simulation models. Mesoscopic models represent logistics flow processes on an aggregated level through piecewise constant flow rates instead of modeling individual flow objects. The results are not obtained by counting individual objects but by using mathematical formulas to calculate the results as continuous quantities in every modeling time step. This leads to a fast model creation and computation. The authors expect that mesoscopic simulation models can help to support decisions on the operational, tactical and strategic level of planning. The paper describes a mesoscopic simulation model of the goods receiving of an assembly plant and compares the simulation results and computation time with a discrete-event model. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for electronic engine-control unit (ECU) inspection in manufacturing phase. Although various methodologies have been developed for the ECU verification at the development phase, the ECU verification in the manufacturing phase has rarely been brought into focus. ECUs in the manufacturing phase need the verification process in the unified way of the ECU software and hardware components by three major causes: 1) ECU software revision, 2) incomplete installation of software, and 3) quality variation of hardware. For the effective ECU verification in the manufacturing phase, we propose a simulation-based ECU testing methodology. The proposed framework configures virtual vehicle environment to simulate an ECU using a “virtual engine system (VES) model” that specifies the operations of every ECU function during a simulation. The proposed framework employs a reverse engineering approach to identify the operation state transition of an ECU function by reference vehicle states from vehicle experimental data. The main objective of this paper is to design a VES model-based inspection system that simulates an ECU without software modification in brief time for set-up and execution. The proposed framework has been implemented and successfully applied to a Korean automotive company. [ABSTRACT FROM AUTHOR]

This paper first analyzes the research significance and purpose of the paper, then uses the SWOT analysis method to list the SO strategy, the ST strategy, the WO strategy, the WT strategy, and the SO strategy of the corresponding SWOT in view of the present situation and problems of the reverse logistics of the automobile industry in China. Combined with China's automobile reverse logistics industry characteristics, pointed out that China's specific reverse logistics development strategy of the automotive industry. [ABSTRACT FROM AUTHOR]

In a context of growing complexity of supply chains and pressure from multiple stakeholders for more transparency, there is a trend in research and practice towards building methods for supply chain sustainability assessment. This trend represents a core issue in research but rarely takes a multiple stakeholder perspective. Based on a literature review, a qualitative and quantitative content analysis of sustainability reports, a survey, and interviews with various stakeholders, this paper suggests a set of sustainability aspects to exchange data to support the sustainability assessment of supply chains. The focus is set on the inter-related automotive and electronics industries which are representative sectors of this growing need for transparency and assessment and reflect the complexity of the issue. The outcome and main differences between companies and other stakeholders (e.g., Non-Governmental Organisations, researchers) is highlighted as well as the overall result of this work: a suggestion for 36 sustainability aspects to exchange sustainability data along the supply chain. By integrating empirical information, this paper emphasises the need to define precisely which sustainability aspects can be found in the different dimensions of sustainability and shows the importance of the governance dimension in sustainability research. It also aims at encouraging a standardised data exchange by taking into account company demographics and data exchange accessibility to allow comparability between companies and supply chains and support decision-makers. The results are meant for companies to improve and monitor the sustainability performance of their organisation and supply chain, improve communication through corporate sustainability reports, and provide more transparent information to customers and final consumers. The results also aim at supporting researchers in the field in developing indicators for a supply chain-wide sustainability assessment. [ABSTRACT FROM AUTHOR]

In this review paper, the integration of Machine Learning (ML) and Model Predictive Control (MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided into these three main subsystems: enhancing safety, improving comfort, and reducing fuel consumption and emissions. Due to the development of new technologies such as advancing autonomous and connected vehicles the complexity of these subsystems is increasing. The ACS is meant to encompass the vehicle dynamics, powertrain control, passenger comfort, and accessories. Since vehicle manufacturers must meet stringent performance and emission requirements, optimal control methods for ACS applications are seen as a promising technology. MPC is an optimal control method for closed-loop control applications that allows constraints to be enforced in real-time while an objective function is minimized. The application of MPC in the automotive industry has been shown in the past decade. An important challenge in the design and real-time implementation of MPC is having a accurate predictive model that also does not require excessive real-time computation. Using ML to provide an accurate model at decreased computational cost improves MPC performance of ACS and is the main focus of this paper. How MPC in ML-based ACS applications ensures stability while meeting constraint is also discussed. Method to combine MPC and ML for the ACS subsystems of vehicle dynamics and powertrain control are reviewed and an outlook on future ACS is discussed. [ABSTRACT FROM AUTHOR]

Waste electrical and electronic equipment and end of life vehicles are two of the main sources of solid waste (after municipal solid waste), in terms of both volume and growth rate. Although they have begun to be adequately regulated worldwide, the management of printed circuit boards embedded into them still presents many challenges. One of these challenges is related to the management of automotive electronic waste. The development of the automotive industry enabled the wide application of electronics within cars. This way, the similarity with electrical and electronic equipments have increased during the last decades, especially considering the presence of printed circuit boards. In spite of these increasing similarities, the treatment of waste printed circuit boards from both electrical and electronic equipments and end-of-life vehicles still follows quite different paths. The aim of this paper is to highlight the unsustainability of their different treatment. A comparison of current practices and a quantification of potential improvements arising from a combined management of printed circuit boards are described within the paper, in terms of both volume and profit. The results demonstrate, even if only theoretically, how a change in managing waste printed circuit boards could offer interesting business opportunities. [ABSTRACT FROM AUTHOR]

Currently, the direction of computer technology in the automotive industry is developing strongly. One of the newest areas is the driver assistance system. One of its main functions is the all-round visibility. It allows you to create a view of the car from above, and it is possible to display from different viewing angles using images from four cameras placed on all sides of the car. There are a large number of solutions in this area, but they are usually specific to different types of equipment. In this paper we will consider the Circular view algorithm on the embedded tda3xx Board. [ABSTRACT FROM AUTHOR]

The European automotive sector is deeply integrated in the EU regional value chain. Each EU member show differences their economic and environmental performance. While central European countries perform more advanced, higher-value and lower-emission tasks, eastern economies specialize in manufacturing and have a higher share of exports from an environmental perspective. However, the environmental effects resulting from automotive production, as well as the distinctions among European economies based on their development levels, remain unexplored. Similarly, the implications of participation in global value chains (GVCs) in this context have not been examined. Therefore, the aim of this paper is to analyze the Environmental Kuznets Curve and the Pollution Haven/Halo Hypothesis in the case of the EU28 automotive sector under a value-added approach. The multi-regional input-output methodology along with a panel data estimation was used to verify the hypotheses. The results verify an inverted-N shape curve for the Kuznets hypothesis. Nevertheless, the results show no evidence of compliance with the haven or halo hypotheses. Moreover, participation in GVCs implies higher environmental impacts. Based on these findings, some policy recommendations are proposed to enhance the sector's circularity and optimize material use throughout the production chain. • Higher automotive emissions intensity in Eastern Europe. • Increased growth in the European automotive sector implies rising emissions. • Increased investment in the automotive sector results in lower emissions. • Increased participation in GVCs implies an increase in emissions. [ABSTRACT FROM AUTHOR]

Cabin climatization is one of the largest auxiliary loads in an electric vehicle, and its performance significantly affects the driving range. Recirculating climatized air from the cabin has been shown to reduce energy consumption, but at the risk of fogging the windows and deteriorating the air quality. Therefore, many automobile manufacturers refrain from adopting it at low ambient temperatures. In this paper, an adaptive recirculation strategy that takes these issues into account is proposed and studied on an electric truck cabin while heating. Numerical simulations were performed using a coupled CFD-thermoregulation model, with the consideration of humidity and CO 2. The JOS-3 thermoregulation model was employed for estimations of skin temperatures and evaporation of vapor from the skin, and the Berkeley comfort model was used to evaluate the comfort metrics. Ten scenarios were considered at various vehicle speeds, temperatures, and relative humidity levels while evaluating them with and without the proposed return-air strategy. The controller adapted between humidity and CO 2 -critical conditions during run-time. The fresh-air mass flow requirements reduced with increasing difference between the setpoint and ambient vapor mass fractions under humidity critical conditions, and plateaued at 10 g/s where CO 2 was more critical. The proposed strategy provided energy savings ranging from 9% to 34% depending on the operating condition. • An adaptive return-air strategy for an electric vehicle cabin is developed. • CFD-thermoregulation models are used to evaluate cabin-passenger interactions. • A parameter for scenario independent return-air ratio (RAR) estimation is defined. • A good estimation of RAR can be obtained with the steady-state analytical solution. • The net energy savings increase linearly with the RAR employed up to 34%. [ABSTRACT FROM AUTHOR]

This paper addresses integrated production and distribution scheduling problem in which orders/jobs are undergone a single operation on any one of the identical machines in parallel and upon the completion of the production they are distributed to destined customers by limited number of vehicles. Customers located in dispersed regions place their orders with predetermined demand size and time windows. On production side, machines operate under discrete speed modes, low of which requires less energy cost or vice versa. On distribution side, energy consumed by a vehicle varies depending on the size of load on it. Therefore, objective is minimizing the sum of the weighted cost emanating from early and tardy deliveries plus production and distribution costs. Operational decisions for (i) production are to determine the allocation of jobs to the machines and sequence of jobs on any machine as well as speed mode of each machine for a particular job. As for (ii) distribution: We need to decide vehicle assignment to specific subset of consolidated jobs and the sequence of customer visitation for each vehicle. We develop a formulation for the problem at hand involving parallel machine scheduling and vehicle routing to obtain solutions to optimality. Not surprisingly, however, CPLEX only provides optimum solution for all instances with customer number up to and 6, for which reason we present two metaheuristics, namely Memetic Algorithm (MA) and Iterated Local Search (ILS) for practical sized instances. Computational results indicate that ILS yields better solutions in shorter times as compared to its counterpart. [ABSTRACT FROM AUTHOR]

• New Fermatean fuzzy cross entropy-based optimization weighting procedure is developed. • An integrated Fermatean fuzzy weighting procedure is developed for decision expert's weight. • A double normalized MARCOS approach with Fermatean fuzzy information is proposed. • A case study of warehouse location for the automotive manufacturing company is discussed. • The comparative study and sensitivity investigation are made to confirm the robustness of the obtained results. The automotive industry is one of the most competitive sectors, and it requires a well-structured logistics system to meet the industry' vital requirements such as just-in-time, lean and agile supply chain operations, productivity and sustainability. Well-located and well-designed warehouses can make reaching these aims for the automotive industry possible and more accessible. Hence, determining a location for a warehouse is a highly critical, tactical, and managerial resolution for the automotive industry, as there is a strong correlation between well-located warehouses and the well-structured logistics network in the automotive industry. Although the WSS is a significant decision-making problem, we observed four critical and severe gaps in the existing literature: (1) the authors preferred to apply traditional objective & subjective frames, and they overlooked existing highly complicated uncertainties. (2) The number of studies focusing on the WSS problem in the automotive industry is surprisingly scarce. (3) It is not sufficiently clear how these factors used in the previous studies were determined, which causes doubts about their reliability. (4) there is no satisfactory evidence of which approaches were used to identify the factors in the previous papers. By considering these gaps, we propose two approaches which can be accepted as a novelty of the paper. First is the extension of the Delphi techniques based on the Fermetean fuzzy sets (FFs) used for identifying the criteria. It also combines the two traditional approaches (i.e., literature review and professionals' evaluations to identify the criteria) with the FF-Delphi technique. The second is the Double Normalized MARCOS approach based on FFs (FF- DN MARCOS) implemented to identify the weights of the criteria and ranking performance of the alternatives. The proposed model was implemented to identify the best warehouse location for the automotive manufacturing company. The results show that the C1 "energy availability & cost" criterion is the most influential criterion and the C5 proximity to port and customs criterion is the second most crucial factor. Then we executed a comprehensive sensitivity analysis, and the results approved the suggested model's validity and robustness despite excessive modifications in the criteria weights. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are the most prominent power source for electric vehicles. The continues use at different environmental conditions demand accurate electrical and mechanical functionality. Most of the research paper published provide information to describe these conditions covering only one or a very few parameters. It leaves aside a holistic and comprehensive study to evaluate performance in lithium-ion battery packs. This review paper presents more than ten performance parameters with experiments and theory undertaken to understand the influence on the performance, integrity, and safety in lithium-ion battery packs. However, when the parameters are reviewed, it is concluded, that vibration and temperature critically affect the electrical and mechanical performance and are inherent to the operation conditions. Through the present work, it was found that limited literature exist that clearly define the influence of temperature and vibration. Therefore, comprehensive research still needs to evaluate the influence of the thermo-mechanical coupled loads on the battery performance. This review concluded that it is fundamental to perform the mentioned research to improve the battery pack performance and safety. In addition, it proposes an innovative technical solution to the automotive industry and can be a novel contribution to academia. [ABSTRACT FROM AUTHOR]

Due to factors such as increasingly stringent regulations and growing public awareness, companies are trying to reduce exposure to economic, environmental and socio-political risks in their supply chains. This paper addresses the problem of assessing such sustainability issues supply chain-wide. First, to clarify the status quo regarding current approaches, it reviews the literature and then groups the various approaches. Second, the paper provides a conceptual framework for supply chain sustainability assessment. Finally, an overview of available methods is provided, and three approaches to assessing and aggregating sustainability indicators are proposed. These approaches allow for the aggregation of the sustainability performance of individual companies into a supply chain-wide sustainability performance by means of quantitative and qualitative indicators. The results are based on a literature review of sustainability assessment in supply chains as well as on focus group workshops with experts from the European automotive and electronics industry. This paper contributes to the theory and practice of sustainability assessment in supply chains by providing a literature analysis, seven key industrial requirements, a conceptual framework and aggregation approaches. The framework and the methods integrate a comprehensive sustainability perspective emphasizing the understudied social dimension. They lay the groundwork for defining specific indicators and for an integration into a software solution for supply chain sustainability data exchange. This enables corporate decision makers to assess and trace the supply chain sustainability performance of products and organizations. [ABSTRACT FROM AUTHOR]

The automobile sector is rapidly increasing its market share worldwide in recent years. It is because of various business strategies by automobile manufacturer such as more focus on small cars and fuel-efficient cars having a low market price that are targeted to capture the maximum market. But due to increasing market share of the automobile sector there are several environmental issues are also arising such as carbon emission, global warming, etc. In such a scenario, automobile manufacturers are facing dual pressure one, to save the environment and another is to maintain the performance of the firm in the long run. The performance of the firm can be judged on the basis of financial, operational and marketing capability of the firm. This paper develops an approach towards the adoption of the green initiatives at the firm, and also tries to build a relationship between the performance of the firm and sustainable development through the adoption of green initiatives. The paper would discuss various green initiatives such as green marketing, green supply chain management, green innovation, etc. which firms are adopting. The study would focus on the facts and opportunities of green initiatives for the success of the firm as well as sustainable development. The paper is based on the exploratory research, and extensive literature survey has been done to bring the findings of the study. [ABSTRACT FROM AUTHOR]

Automobile industries have prime importance to vibration testing. Sine vibration testing is performed when we have been given with only one frequency at given time instant. Trend to perform random vibration testing has been increased in recent times. As random vibration takes into account all excited frequencies in defined spectrum at known interval of time, it gives real-time data of vibration severities. The vibration severity is expressed in terms of Power Spectral Density (PSD). The consideration in the paper is based on the fact that we will be dealing with only random vibration. Aim of paper is to perform random vibration analysis for starter motor mounted on an engine and also find its transmissibility value. [ABSTRACT FROM AUTHOR]

Recent advancements in 3D printing, sometimes referred to as additive manufacturing is considered a game changer relative to rapid prototyping. The industrial, military, commercial, aerospace as well as the automotive industry have made significant gains due to this technology. The 3D printing market is expected to reach $8.43 billion by 2020, at a Compound Annual Growth Rate (CAGR) of 14.37% from 2014 to 2020 1 . As in any new technology there are trade-offs and this work reports on an approach to optimize or save in power consumption for such systems. Because the 3D printing process is focused about producing three dimensional (3D) solid objects from a digital file, power consumption is an important consideration. This paper presents the prototype of a heated platform designed for use in higher-end 3D printers. The goal of this prototype was to improve the energy efficiency and reduce the power consumption of a 3D printer by employing an innovative and validated heating circuitry or platform without compromising print quality. For this investigation a 3D printer that has a surface component with dimensions of 10in X 10in is selected. As a baseline for comparison, the calculated power consumption under standard operating conditions is 50W where the source is 12 V DC. The paper herein expands on key design parameters, presents the preliminary design calculations, and describes comparable products researched and statistical improvements over those products. [ABSTRACT FROM AUTHOR]

There have been significant advances in technology and automated systems that will eventually see the use of autonomous cars as commonplace on our roads. Various systems are already available that provide the driver with different levels of decision support. This paper highlights the key human factors issues associated with the interaction between the user and an autonomous system, including assistive decision support and the delegation of authority to the automobile. The level of support offered to the driver can range from traditional automated assistance, to system generated guidance that offers advice for the driver to act upon, and even more direct action that is initiated by the system itself without driver intervention. In many of these instances the role of the driver is slowly moving towards a supervisory role within a complex system rather than one of direct control of the vehicle. Different paradigms of interaction are considered and focus is placed on the partnership that takes place between the driver and the vehicle. Drawing on the wealth of knowledge that exists within the aviation domain and research literature that examines technology partnerships within the cockpit, this paper considers important factors that will assist the automotive community to understand the underlying issues of the human and their interaction within complex systems. [ABSTRACT FROM AUTHOR]

Full cost accounting has been applied in many industrial settings that include the oil and gas, energy, chemical and waste management industries. Presently, it is not known how it can be applied in an automotive industry context. Therefore, the objective of this paper is to review existing full cost accounting methods and identify an appropriate approach for the automotive sector. This literature review of 4381 papers extracted ten full cost accounting methods with a diverse level of development and consistency in application. Based on a careful examination and critical analysis of each approach and existing automotive sustainability measures, the Sustainability Assessment Model developed by British Petroleum and Aberdeen University has been proposed as a well-developed and potentially practical tool for automotive applications. The Sustainability Assessment Model can be used by both academics and practitioners to translate a range of conflicting sustainability information into a monetary unit score. This is an effective way of communicating trade-offs and outcomes for complex and multi-disciplinary sustainable decisions in the automotive sector. It measures a broad range of economic, environmental, resource and social effects (internal and external), which is currently lacking in existing automotive systems. Its other strengths are the ability to provide both monetary and physical metrics for sustainability assessment, its flexibility and the ability to combine multiple sustainability dimensions. Furthermore, this paper provides helpful clues for researchers interested in exploring full cost accounting in the future by reviewing, analysing and synthesising the broad range of relevant sources from diverse fields in this topic area. [ABSTRACT FROM AUTHOR]

In the last two decades, the alignment of Information Technologies (IT) and the business needs are continuous requirements. These needs arise from continuous development of a specific solution for a specific problem, without ever worrying if, in the future, such solutions can be integrated to others. In order to line up the IT with the business needs in terms of development of information systems, the use of Enterprise Architecture (EA) approaches start to be explored. The existence of an EA allows the communication infrastructure, applications, security plans, data, network services and other domains to be somehow correlated. The support for EA's development also benefits the maintenance, since the out of date or inoperability can be avoided. This short paper describes the approach that is being followed to implement an EA on an agile and flexile automotive company, the Bosch Car Multimedia Portugal. In order to achieve this goal, an investigation is being conducted over what has already been done in the company to align with internal guidelines. Later on, a survey and analysis will be conducted on what is already implemented and correspondent business objectives alignment. From the current state of the existing architecture, a plan for EA improvements will be analyzed and proposed. [ABSTRACT FROM AUTHOR]

Vehicle software architectures have been evolving over the last twenty years to support data-driven functionalities. Several enterprises from different domains are currently focusing on improving their data architectures by re-defining the underlying data models to enable core support for analytics and artificial intelligence. Moreover, a common desire to add clear data provenance and explicit context impulses the field of semantics and knowledge graphs. Nevertheless, in the automotive industry, the scenario of connected vehicles implies extra complexity. Vehicle data has an enormous variety, making it essential to develop and adopt standards. This paper presents aspects of ongoing research at the BMW Research Department regarding a conceptual design for vehicle software architectures in the automotive industry. We discuss the principles of a modern data architecture with particular emphasis on the data-centric mindset. We also explore the current challenges and possible working points as the foundation to move from siloed data towards a so-called AI factory. [ABSTRACT FROM AUTHOR]