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The modularity of a product architecture with standard, variant, and optional modules can be measured by the characteristics of commonality and combinability. Positive and negative effects of a more communal or more combinable structure are summarized and visualized in an impact model. However, due to the megatrend of personalization, the solution space of a modular product architecture needs to be extended to include personalizable modules. What remains unclear till now is how personalization impacts the different life phases. Therefore, this article derives an impact model considering product personalization. First, the modularity property of personalizability is derived, in order to then specifically investigate the effects occurring in the different life phases. Therefore, a literature review is conducted. New effects are found, and the existing effects of commonality and combinability are examined for their validity for personalizability. The findings are then combined with the known effects of commonality and combinability to create a holistic impact model of modular product families. This new model takes personalizable modules into account and can support companies in defining the goals and focus of a modularization project. [ABSTRACT FROM AUTHOR]

Plastic packaging, in the form of films, brought several advantages to the commercialization of products given its lightness and durability. It provided better ergonomics, ease of transport, increased shelf life, and easy handling and use. Despite that, plastic packaging is facing enormous sustainability concerns associated with the traditional practice of linear economy, combined with commonplace irresponsible handling by citizens since it is almost exclusively designed for single-use and its end-of-life (EOL) management is not planned for. To mitigate that, the circularity of plastic packaging must be more clearly studied and evaluated through approaches such as micro-level circular economy (CE) indicators. This paper focuses on the selection of relevant CE micro-indicators specifically for the plastic packaging sector among the plethora of indicators available. Relations are also established between CE micro-indicators and CE guiding principles, as well as the most prevalent Design for X (DfX) approaches, providing new insights into how these different aspects of sustainability can be linked together. Results show three micro-level indicators as the most relevant for circularity calculation in packaging, namely those termed 'MCI', 'VRE', and 'CEIP', because their methodology and approach address most of the CE guiding principles and DfX approaches relevant for the packaging sector. Finally, guidelines and good practices to promote circularity adoption in the plastic packaging sector are highlighted. This work can guide companies aiming to adopt CE micro-indicators in their practical implementation, overcoming the significant knowledge barrier that currently exists. [ABSTRACT FROM AUTHOR]

Designing mechatronic products requires interdisciplinary skills and as products become more complex, the design of mechatronic systems plays a critical role. To minimize waste production and pollution, a shift toward a circular economy is necessary, with mechatronic products being particularly impacted by such policies. Repairing plays a key part in achieving a circular economy. Through repairability, the product lifespan can be extended, and combined with maintenance the rate of product replacement can be reduced. Within this context, the goal of this paper is to propose a design methodology (based on the EN 45554:2020 standard) for generating and implementing eco-design rules for disassembly and repair. The methodology has four phases, the first one is the identification of target components (those that are more likely to fail during the lifespan). The second phase encompasses the experimental disassembly analysis which can be manual or virtual. The third phase is the assessment of the disassemblability index which includes the analysis of parameters that affect the disassembly phase. The last phase is the implementation of the eco-design methodology for all the components that do not meet the minimum repairability requirements. A case study of electro-mechanical ovens is presented, targeting replaceable components. The results show that the use of this framework and the eco-design actions derived from it are successful in improving the repairability of the product and increasing the disassemblability index (30% on average) through a virtual analysis. A sensitivity analysis has been conducted to study the impact of parameter weight modification. This research contributes to advancing repairability and supporting the circular economy paradigm in mechatronic product design. [ABSTRACT FROM AUTHOR]

Remanufacturing is a process that returns end-of-life equipment to as-new conditions and offers numerous environmental and economic benefits. To fully capitalize on remanufacturing, its synergistic interactions with design must be fully realized and addressed during the design stage. Although this fact is widely recognized in the literature, most of the current studies focus primarily either on the design or remanufacturing aspects of design for remanufacturing (DfRem). In an effort to offer a more integrated DfRem approach than those reported in the literature, we propose a new combined design and remanufacturing optimization (reman co-design) framework that takes a holistic approach by leveraging the intricate interplay between design and remanufacturing. The aim of this formulation is to identify the optimal decisions that maximize the benefits of remanufacturing throughout the entire lifespan of a product. To showcase the utility of the new formulation, we are using a case study of a hydraulic manifold, (re)manufactured by John Deere. Using this industry example, we compare the results of reman co-design to the ones from a decoupled remanufacturing design approach. Results reveal that remanufacturing benefits are better realized and improved upon when using the developed reman co-design approach. [ABSTRACT FROM AUTHOR]

As additive manufacturing (AM) usage increases, designers who wish to maximize AM's potential must reconsider the traditional manufacturing (TM) axioms they may be more familiar with. While research has previously investigated the potential influences that can affect the designs produced in concept generation, little research has been done explicitly targeting the manufacturability of early-stage concepts and how previous experience and the presenting of priming content in manufacturing affect these concepts. The research in this paper addresses this gap in knowledge, specifically targeting differences in concept generation due to designer experience and presenting design for traditional manufacturing (DFTM) and design for additive manufacturing (DFAM) axioms. To understand how designers approach design creation early in the design process and investigate potential influential factors, participants in this study were asked to complete a design challenge centered on concept generation. Before this design challenge, a randomized subset of these participants received priming content on DFTM and DFAM considerations. These participants' final designs were evaluated for both traditional manufacturability and additive manufacturability and compared against the final designs produced by participants who did not receive the priming content. Results show that students with low manufacturing experience levels create designs that are more naturally suited for TM. Additionally, as designers' manufacturing experience levels increase, there is an increase in the number of designs more naturally suited for AM. This correlates with a higher self-reported use of DFAM axioms in the evaluation of these designs. These results suggest that students with high manufacturing experience levels rely on their previous experience when it comes to creating a design for either manufacturing process. Lastly, while the manufacturing priming content significantly influenced the traditional manufacturability of the designs, the priming content did not increase the number of self-reported design for manufacturing (DFM) axioms in the designs. [ABSTRACT FROM AUTHOR]

Generative design (GD) techniques have been proposed to generate numerous designs at early design stages for ideation and exploration purposes. Previous research on GD using deep neural networks required tedious iterations between the neural network and design optimization, as well as post-processing to generate functional designs. Additionally, design constraints such as volume fraction could not be enforced. In this paper, a two-stage non-iterative formulation is proposed to overcome these limitations. In the first stage, a conditional generative adversarial network (cGAN) is utilized to control design parameters. In the second stage, topology optimization (TO) is embedded into cGAN (cGAN + TO) to ensure that desired functionality is achieved. Tests on different combinations of loss terms and different parameter settings within topology optimization demonstrated the diversity of generated designs. Further study showed that cGAN + TO can be extended to different load and boundary conditions by modifying these parameters in the second stage of training without having to retrain the first stage. Results demonstrate that GD can be realized efficiently and robustly by cGAN+TO. [ABSTRACT FROM AUTHOR]

With the healthcare industry moving towards self‐medication, the number of self‐service pharmacies stocking over‐the‐counter pharmaceuticals (OTCP) is rising. The aesthetic attributes of OTCP packaging are critical to attract consumers' attention against competing products. Moreover, sustainable design aims at minimising the negative environmental impacts of packaging. Studies show that stakeholders' interests should be represented more in pharmaceutical packaging, specifically in the early stages of the design process. For this reason, OTCP packaging design is challenging, as sustainable packaging is typically seen as unappealing. Within this context, this paper presents a novel and comprehensive framework aimed at supporting designers to develop aesthetically pleasing and sustainable OTCP packaging, placing multiple users at its core. Studies with OTCP packaging stakeholders were first conducted to identify the framework requirements together with the necessary OTCP packaging attributes. A framework architecture was developed and subsequently implemented in a proof‐of‐concept computer‐based tool. The framework and its implementation were evaluated with the OTCP packaging development stakeholders. Results provide a degree of evidence that the framework contributes significantly to guide OTCP packaging designers in taking the right decisions and can also provide the first steps towards considering aesthetics and sustainability in the packaging design in other sectors, namely, food and beverage. [ABSTRACT FROM AUTHOR]

Abstract Conceptual design is the crucial stage of selecting and determining product composition and configuration, which greatly affects product performance and cost. In conceptual design stage, designers have the maximum design freedom in order to put forward to the optimal design solution in terms of assembly, manufacturing and cost. However, the above product aspects are analyzed independently in current industry, and a guided decision-making method based on multi-objective criteria is missing. Therefore, it is necessary to introduce a multi-objective analysis method to obtain an optimal conceptual scheme considering various aspects of product. Due to the complexity of product conceptual solution generation, this study divides the multi-objective analysis process into multi-objective solution optimization and multi-objective solution selection. The non-dominated solution set can be obtained from all the potential solutions in the former step. Then, the optimal conceptual solution can be obtained from the non-dominated solutions set in the latter step. The two-stage multi-objective analysis method has two advantages: (1) large dominated solutions can be quickly eliminated which can reduce the workload of solution selection; (2) TOPSIS method is easy to implement, which can work with attribute weights and scores of each design option. Finally, using the conceptual design of a centrifugal compressor as an example, the method's availability and practicality are demonstrated.

Product personalization will play a key role in the future of society by making these products available for everyone, everywhere. Personalized production requires the involvement of the customer in the design process. Thus, tools to identify which components and modules allow for customer interaction are needed, and to further assess the effects of customer interaction on the product design and the manufacturing system. In this article, we propose a framework to analyze the extent of personalization a designer and a manufacturing firm can achieve in the context of efficient personalized production. We study this as a dyad of product design and manufacturing system, which uses two main attributes: product modularity and manufacturing configuration complexity. To demonstrate our ideas, we use two applications. The first is a case study for gearbox, usually manufactured at high levels of efficiency under the mass production paradigm. The second case is an application for the footwear industry for the particular case of a sneaker, a sports shoe that is prone to be customized. These case studies show the flexibility of this framework to a wide set of industries. In both cases, we compare a personalization scenario with a baseline scenario and provide managerial insights. [ABSTRACT FROM AUTHOR]

Path tracking error control is an essential functionality in the development of autonomous vehicles to follow a planned trajectory. Significant path tracking errors could lead to a collision or even out of the control of the vehicle. Model-based control strategies have been developed to minimize the vehicle's path tracking errors. However, the vehicle model may not truly represent the actual vehicle dynamics. Furthermore, the parameters employed in the vehicle dynamic model may not represent the actual operating conditions of the vehicle under environmental uncertainty. This paper proposes a real-time bias-learning method coupling with the model predictive control (MPC) to improve the fidelity of a baseline vehicle model with the aid of a few experiments (or virtual experiments) so that the path tracking error can be reduced in real-time operation. Gaussian process (GP) regression and recurrent neural network (RNN) are employed for bias-learning and their effectiveness are compared under different scenarios. GP regression learns non-linearity of the model bias through its nonlinear kernel function, whereas the RNN model formulates the bias as a linear combination of hidden nodes which capture the non-linearity of the model bias with a recurrent form. Results reveal that RNN is more effective for real-time learning of the nonlinear model bias than the classical GP regression and the proposed bias-learning model is able to improve the fidelity of a baseline vehicle dynamic model. Consequently, path tracking performance can be greatly improved under environmental uncertainty using the bias-learning-based MPC. [ABSTRACT FROM AUTHOR]

Designers should adequately develop user considerations such as usability, safety, and comfort during the design process of new systems. Nevertheless, incorporating human factors engineering principles during early design phases is not simple. The objective of this work is to assist designers in implementing human factors engineering principles during early design phases using a functional model framework. This effort expands our previous work on automating the function-human error design method (FHEDM) implementation. In this work, we use data mining techniques in a design repository to explore the construction of association rules between components, functions, flows, and user interactions. Such association rules can support designers assessing user-system interactions during the early design stages. To validate this approach, we compare the associations generated by expert designers using the FHEDM while designing a new product to those generated by an algorithm using the repository data. The results show notable similarities between the associations extracted by the algorithm and the associations identified by designers. Thus, the overall results show that association rules extracted from a rich dataset can be used to distinguish user-product interactions, demonstrating the potential of automating the identification of user-product interactions from a functional model. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is used to produce load-bearing, safety-critical components in industries like aerospace, automotive, and medical devices. Designers can create AM components with complex internal features, organic topologies, and lattice structures to reduce part mass or part count. However, such complex features can make designs difficult or impossible to inspect using mature nondestructive testing (NDT) methods. Professional organizations suggest designers keep quality assurance and quality control (QA/QC) in mind early in the design process. The Design for Inspectability (DfI) framework is suggested as a way of meeting the need for early-stage QA/QC considerations. This work presents a case study, where a group of designers considered one type of NDT, known as Pulse-Echo Ultrasonic (PEU) testing. Using heuristics derived from relevant literature, designers were able to create designs with increased inspectability. This improved inspectability came at the cost of other design objectives, however, such as strength and mass. This implies that certain design objectives may be inversely related to increased inspectability, raising significant concerns for the field. This work marks the first step toward mapping out the trade-offs between inspection and performance objectives. [ABSTRACT FROM AUTHOR]

In recent years, the demand for smarter and more customized products has increased. Traditional manufacturing systems are typically designed for mass production, which is not suitable for producing customized products. It requires a new manufacturing system that is flexible, scalable, and reconfigurable, for example, a matrix-structured manufacturing system. As the manufacturing system and product design form a symbiotic relationship, products should also be designed for the new manufacturing system to optimize performance. This paper presents a four-stage methodology to improve the product and product family design targeted production in matrix-structured manufacturing systems. The four stages include a product family analysis, indicator calculations, product optimization, and digital twin-based verification and validation. The proposed methodology is applied in a laboratory case study. The result shows that the improved product design has a significantly higher design efficiency than the original ones. We conclude that the methodology of Design for Matrix-Structured Manufacturing Systems has a holistic view on the product family and can optimize the production efficiency in matrix-structured manufacturing systems. [ABSTRACT FROM AUTHOR]

The following case study portrays the several steps required to conceive a product from scratch. The first step involves an in-depth analysis of today's electric bicycle market in order to obtain data and information relating to the levels of innovation and comfort required by customers. Then, we evaluate the implementation of a useful method to understand the level of innovation that the product must have to be competitive on the market. The second part studies the architecture of the product, considering the different components already sold on the market which will become part of the project. The third part concerns a comparison between different stylistic trends that the vehicle may have (in order to outline the best one). The fourth part concerns the CAD realization of the virtual model complete with all its parts, including a structural verification study of the frame. The last part studies the presentation of the product to the customer, exploring different effective ways to communicate what the strengths of the new product will be (also allowing them to customize it before its realization). The plan for the realization of the new product, starting from the concept to arrive at the final presentation to the customer, follows the methods proposed by applying a series of steps to develop a generic new product in an efficient, sensible, and methodical manner. Therefore, we will refer to quality function deployment (QFD), benchmarking (BM), design for X, until reaching the final prototyping and testing phases. [ABSTRACT FROM AUTHOR]

The fuzzy front end of engineering design can present a difficult challenge, and as such, recent engineering design research has focused on guiding and influencing the way a designer ideates. Early ideation can be especially difficult when attempting to integrate specific design objectives in product design, called Design for X (DfX). This paper presents two experiments exploring the efficacy of a structured Design for the Environment (DfE) design method called the Guidelines and Regulations for Early design for the Environment (GREEn) Quiz that provides designers with sustainable design knowledge during the conceptual design phase. The GREEn Quiz operates on a web-based platform and queries the designer about their design concepts; an end-of-quiz report provides abstract DfE knowledge to designers. While this abstract knowledge was able to be applied by designers in a former study, we hypothesize that providing targeted, specific design strategies during conceptual design will enable novice designers to better integrate DfE. In this study, we created these DfE strategies, integrated these into the GREEn Quiz, and studied the efficacy of these strategies when presented to designers at both the expert and novice levels. Results suggest that respondents with access to the strategy-based GREEn Quiz produced concepts with evidence of a broader range of sustainable design decisions and higher solution quality scores. This work shows the promise of supplemental DfE methods for concept generation to enable the design of more environmentally sustainable products. [ABSTRACT FROM AUTHOR]

With limited time and resources available to carry out Engineering for Global Development (EGD) projects, it can be difficult to know where those resources should be allocated to have greater potential for meaningful impact. It is easy to assume that projects should occur in a particular location based on personal experience or where other development projects are taking place. This can be a consideration, but it may not lead to the greatest social impact. Where to work on a project and what problem to work on are key questions in the early stages of product development in the context of EGD. To aid in this process, this article presents a method for assessing global needs to ensure thoughtful use of limited EGD resources. We introduce a method for identifying locations where there is human need, gaps in technological achievement, and what the work environment is in a country. Results of the method are compared to what countries receive the most foreign aid dollars per capita. Measures were calculated using the principal component analysis on data from development agencies. These results can help practitioners in selecting where to undertake development projects with an eye toward targeting locations that may yield high levels of social impact. [ABSTRACT FROM AUTHOR]

Decision-making models such as Analytical Hierarchy Process (AHP), Weighted Decision Matrix (WDM), Pugh Matrix and the likes have been able to assist in the decision process considering the objectives of each evaluation criteria in the alternatives. However, these models need to consider the qualitative and subjective nature of the design features. In order to reduce the unbalanced scale of judgment and the uncertainty associated with the crisp information in the decision process, fuzzified and hybridized models are necessary. Existing hybridized decision models applied for machine concept selection deploy several design features and sub-features at the conceptual product design, which thus make the decision making process to be tedious. In light of this, this article presents a hybridized decision-making model, which harness the comparative strength and computational integrity of fuzzy pairwise comparison matrix and fuzzy weighted average, to numerically analyse a reasonable amount of machine design features, thereby making decision making process less tedious. Design for reconfigurability and functionality which are peculiar to reconfigurable machines was introduced using a Reconfigurable Assembly Fixture (RAF) as a case study while other design features related to design concept evaluation were grouped under design for X. The result of the hybridized model shows that, concept three is the optimal design from four sets of designs. This is compared to previous publication using the RAF design concepts with different design features and sub-features. The comparison indicates that there is a close range in the final values of the designs due to the inclusion of several sub-features in the decision process which were not used in the previous study.

Through appropriate operations and policies, such as green processes and product development process (PDP), companies can respond to environmental sustainability. To remain competitive, one such approach, Design for X (DFX), involves considering different environment and sustainable strategies through different factors Xs. With regard to the availability of different DFX techniques that consider environmental issues, the decision as to which approach needs to be adopted remains absent. This paper aims at presenting an overview from 1980 to 2020 of the developed research, applications, and DFX techniques for assessing green issues. Selected DFX techniques are linked with strategies used in organizations. Following a literature analysis, a collaborative knowledge‐based framework that addresses the design concepts needed to assess environmental, safety, and health concerns in the development of green products is proposed. Furthermore, as a pillar for considering the Semantic Web and an evolving approach linked with natural language processing (NLP) and artificial intelligence (AI), an ontology‐based knowledge management model for green assessment is developed for the representation, acquisition, organization, and capitalization of knowledge in a computer interpretable manner. The findings are useful for both managers and practitioners as they provide a coherent domain ontology that can help them manage knowledge, improve teamwork, and make decisions in a collaborative green PDP. Besides, an understanding of the essential design considerations that are required to implement environmental, safety, and health issues, as well as competencies used in the PDP is presented. Key barriers, managerial and strategic implications, and mitigation actions are also identified in this paper. [ABSTRACT FROM AUTHOR]

Most methodologies developed to support the Product-Service System (PSS) design consider the integration of service features into the product design from a high-level of abstraction and are usually focused on the conceptual phase, neglecting the detailed level of design. Besides, the Knowledge Management perspective is not considered in those methodologies, also affecting how new design knowledge is created, formalized, and shared across the company's organization. The PSS Design GuRu Methodology, grounded on Concurrent Engineering and Design for X approaches, was developed to fill these issues. This study presents how the PSS Design GuRu Methodology can be incorporated into a PSS detailed design process in a B2B company operating in the food and bakery machinery sector, focusing the analysis on its contribution to promoting Knowledge Management. In particular, a detailed case of development and integration of a service feature—the installation service—to a product in the PSS scope is conducted. The PSS Design GuRu Methodology proves to be effective in supporting the generation, management, use, sharing, and reuse of new knowledge in the shape of design guidelines and rules. [ABSTRACT FROM AUTHOR]

When designing for additive manufacturing (AM), designers often need assistance in breaking out of their conventional manufacturing mind-set. Previously, the authors derived design heuristics for AM (DHAM) to assist designers in doing this during the early phases of the design process. This study proposes a set of 25 multimodal cards and objects to accompany each of the design heuristics for AM and studies their effect through a series of controlled, novice user studies conducted using both teams and individuals who redesign a city E-Bike. The resulting AM concepts are analyzed in terms of the quantity of design modifications relevant for AM, AM flexibility, novelty, and variety. It is found that the DHAM cards and objects increase the inclusion of AM concepts, AM modifications, and the unique capabilities of AM in the concepts generated by both individuals and teams. They also increase the creativity of the concepts generated by both individuals and teams, as measured through a series of defined metrics. Furthermore, the objects in combination with the cards are more effective at stimulating the generation of a wider variety of designs than the cards alone. Future work will focus on studying the use of the DHAM cards and objects in an industrial setting. [ABSTRACT FROM AUTHOR]

Industry 4.0, as the fourth industrial revolution, represents significant challenges and numerous innovation opportunities for future product realization. A critical area of Industry 4.0 is the advancement of new design theories, design methods, and design principles to drive and enable the revolution with designers, engineers, teams, and organizations. This paper focuses on the advancement of a design theory and design principles for a growing manufacturing capability for Industry 4.0: additive manufacturing (AM). With high degrees of freedom, the field and use of AM requires design guidance and highly practical knowledge for supporting ideation processes, enabling understanding of capabilities, and creating a basis to innovative with the technology. Some design principles for AM exist in the literature; however, designers seek more fundamental and practical design guidelines for successfully creating and building their customized design artefacts, especially as Industry 4.0 moves forward. In this study, a crowdsourced repository for additively manufacturable components is used as the source of design data, within an empirical study, to extract practical design principles for AM. A total of 23 crowdsourced design principles for AM are extracted and clustered according to level specificity: (i) design for manufacturing, (ii) design for digital manufacturing, (iii) design for AM, and (iv) design for fused deposition modeling. These 23 AM design principles, as a foundation for AM design and Industry 4.0, are provided in a common framework; expressed for ready use by designers, developers, and researchers; and illustrated through some contemporary designs. [ABSTRACT FROM AUTHOR]

Cyber-physical-social systems (CPSS) with highly integrated functions of sensing, actuation, computation, and communication are becoming the mainstream consumer and commercial products. The performance of CPSS heavily relies on the information sharing between devices. Given the extensive data collection and sharing, security and privacy are of major concerns. Thus, one major challenge of designing those CPSS is how to incorporate the perception of trust in product and systems design. Recently, a trust quantification method was proposed to measure the trustworthiness of CPSS by quantitative metrics of ability, benevolence, and integrity. The CPSS network architecture can be optimized by choosing a subnet such that the trust metrics are maximized. The combinatorial network optimization problem, however, is computationally challenging. Most of the available global optimization algorithms for solving such problems are heuristic methods. In this paper, a surrogate-based discrete Bayesian optimization method is developed to perform network design, where the most trustworthy CPSS network with respect to a reference node is formed to collaborate and share information with. The applications of ability and benevolence metrics in design optimization of CPSS architecture are demonstrated. [ABSTRACT FROM AUTHOR]

Prior research suggests that excess (purposeful inclusion of margin beyond what is required for known system uncertainties) can limit change propagation and reduce system modifications. Reducing change costs increases system flexibility, permitting adaptions that satisfy uncertain future requirements. The benefits of excess, however, must be traded against higher costs of the initial system and likely performance decreases. Assessing the benefits and costs of excess requires evaluating what forms, locations, and magnitudes of excess inclusion are optimal. This paper improves the state-of-the-art in two ways. First, prior research has generally assessed excess in system-level properties (aggregating component properties into a single metric). The approach presented in this paper extends excess assessment to the component level so that the effects of excess on change propagation may be explicitly captured. Second, this approach holistically assesses the value of excess by evaluating both its costs and benefits. The approach borrows from Decision-Based Design and Model Based System Engineering (MBSE) in creating a generic modeling method capable of excess valuation. A desktop computer example is used for demonstrating how excess is valued in a system and the potential gains associated with excess inclusion when mining cryptocurrency. A single component optimization of the power supply capacity for the desktop is assessed to be 750 W, which balances the initial cost against the future flexibility. A system-level optimization then demonstrates the identification of critical change propagation pathways and illuminates both where and how excess may be included to inhibit change propagation. This key component was identified as the motherboard-central processing unit (CPU) slot in the tested systems. [ABSTRACT FROM AUTHOR]

In the industrial production process, the tolerance design of product is directly related to the product and production cost. This paper proposes an optimization method for the geometrical tolerance design of sheet metal parts based on joining simulation. Finite Element Method (FEM) is applied to simulate the influence of Body In White (BIW) joining process on the assembly deviation. Because the profile tolerance is widely used in the BIW sheet metal parts as the manufacturing requirements for the single parts as well as the assemblies to ensure the dimensional quality of the product, this type of tolerance on parts are mapped on the FE meshes in this work subjected to the Product Manufacturing Information (PMI). A sensitivity analysis is implemented to rank the tolerances by constructing meta-models. Without compromising the dimensional stability of the assembly, the geometrical tolerances of the single parts are optimized through a two-level optimization system. An automotive reinforcement assembly is studied to illustrate the proposed method. The maximum allowable tolerance ranges of the reinforcement part are adapted with respect to a pre-defined process capability index. The result provides a quantitative tolerance optimization strategy for BIW parts in an early development phase. [ABSTRACT FROM AUTHOR]

Networking complex sociotechnical systems into larger Systems of Systems (SoS) typically results in improved performance characteristics including sustainability, efficiency, and productivity. The response, or lack thereof, of many SoS to unexpected constituent system failures undermines their effectiveness in many cases. SoS performance after faults can be improved by improving the SoS's hard (physical design) or soft (human intervention) resilience. The current approaches to increase resilience are limited due to the cost and necessary of human response increasing non-linearly with SoS scale. The limitations of current approaches require a novel design approach to improve SoS network resilience. We hypothesize that biologically inspired network design can improve SoS resilience. To illustrate this, a systems dynamics model of a Forestry Industry is presented and an optimization search over potential hard and soft resilience approaches is compared to a biologically inspired network improvement. SoS network resilience is measured through the newly developed System of System Resilience Measurement (SoSRM). Our first result provides evidence that biologically inspired network design provides an approach to increase SoS resilience beyond hard and soft resilience improvements alone. Second, this work provides evidence that having a SoS constituent fulfill the ecosystem role of detrital actor increases resilience. Third, this paper documents the first case study using the new SoSRM metric to justify a design decision. Finally, this case study provides a counter-example to the theory that increased sustainability always results in increased resilience. By comparing biologically inspired network redesign and optimized traditional resilience improvements, this paper provides evidence that biologically inspired intervention may be the needed strategy to increase sociotechnical SoS network resilience, improve SoS performance, and overcome the limitations of traditional resilience improvement approaches. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) enables engineers to improve the functionality and performance of their designs by adding complexity at little to no additional cost. However, AM processes also exhibit certain unique limitations, such as the presence of support material. These limitations must be accounted for to ensure that designs can be manufactured feasibly and cost-effectively. Given these unique process characteristics, it is important for an AM-trained workforce to be able to incorporate both opportunistic and restrictive design for AM (DfAM) considerations into the design process. While AM/DfAM educational interventions have been discussed in the literature, few studies have objectively assessed the integration of DfAM in student engineering designers' design outcomes. Furthermore, limited research has explored how the use of DfAM affects the students' AM designs' achievement of design task objectives. This research explores this gap in literature through an experimental study with 301 undergraduate students. Specifically, participants were exposed to either restrictive DfAM or dual DfAM (both opportunistic and restrictive) and then asked to participate in a design challenge. The participants' final designs were evaluated for (1) build time and build material (2) the use of the various DfAM concepts, and (3) the features used to manifest these DfAM concepts. The results show that the use of certain DfAM considerations, such as part complexity, number of parts, support material mass, and build plate contact area (corresponding to warping tendency), correlated with the build material and build time of the AM designs--minimizing both of which were objectives of the design task. The results also show that introducing participants to opportunistic DfAM leads to the generation of designs with higher part complexity and lower build plate contact area but a greater presence of inaccessible support material. [ABSTRACT FROM AUTHOR]

In product development, early decisions made within the requirements specification and design phase have significant impact on overall functionality, quality and lifecycle cost of the product. In Systems Engineering, these oftentimes iterative design phases are steered by underlying development models, providing processes, guidelines and best practices. One family of guidelines are the "Design for X" (DfX) tools or principles. In the development of mechatronic systems e.g. production systems, traditionally the mechanical, electric and software domain are to be distinguished. Over time, driven by trends of digitalization, communication and interconnection of systems, the product innovation and thus the required development effort has been shifting from the mechanical domain towards the software domain. The question of system interoperability thereby is of a fundamental nature. This is especially relevant within the current paradigms of Industry 4.0 and the Digital Twin. To reflect the growing importance of interoperability (i.e. the ability of systems to purposefully interchange interpretable information) as a requirement for the product within the design phase of technical systems, this paper introduces "Design for Interoperability" (DfIOp) as a new DfX principle. First, this is motivated by the ever-growing importance of information technology for current and future products. Then, the main contribution of the paper is the combination of the state of the art in the research domains of DfX, interoperability, Systems Engineering and Industry 4.0. Consequently, DfIOp will be presented, including a systematization model as well as a first method for its implementation. Further contemplation on DfIOp shows, that it enables derivative principles such as "Design for Data Analytics" and "Design for Digital Twin". In future research, the underlying models as well as methods of DfIOp are to be further detailed and integrated into the research context. [ABSTRACT FROM AUTHOR]

Though academic research for identifying and considering the social impact of products is emerging, additional insights can be gained from engineers who design products every day. This paper explores current practices in industries used by design engineers to consider the social impact of products. Forty-six individuals from 34 different companies were interviewed to discover what disconnects exist between academia and industry when considering a product's social impact. These interviews were also used to discover how social impact might be considered in a design setting moving forward. This is not a study to find "the state of the art," but considers the average engineering professional's work to design products in various industries. Social impact assessments (SIA) and social life cycle assessments (SLCA) are two of the most common processes discussed in the literature to evaluate social impact, both generally and in products. Interestingly, these processes did not arise in any discussion in interviews, despite respondents affirming that they do consider social impact in the product design. Processes used to predict social impact, rather than simply evaluate, were discussed by the respondents. These tended to be developed within the company and often related to industry imposed government regulations. To build on this study, the findings herein should be further validated for executives, managers, and engineers. A study specific to these roles should be designed to understand the disconnect better. Additionally, processes should be developed to assist engineers in considering the social impact of their products. Work should also be done to help educate engineers and their leaders on the value of considering the social impact in product design. [ABSTRACT FROM AUTHOR]

Process plans in additive manufacturing (AM) have a profound impact on the performance of fabricated parts such as geometric accuracy and mechanical properties. Due to its layer-based, additive nature, AM processes can be controlled at multiple scales starting from the scan vector/pixel scale. However, most process planning methods in AM configure process settings at the part scale. This leaves large unexplored regions in the design space that may include optimal designs. To address these untapped potentials, we present a process planning strategy based on the concept of manufacturing elements (MELs) to harness process variables at low scales for design. First, we decompose a part design into multiple MELs that contain geometric and manufacturing information. Two-scale process-structure-property (PSP) relationships are then constructed for MELs and their assembly. Decision tools, including the compromise decision support problem, are employed to navigate two-scale PSP relationships for supporting designers in design exploration on process variables and optimization of process plans. The proposed strategy is illustrated with a process planning example for a lattice structure, which has multiple design goals and is to be fabricated using material extrusion. [ABSTRACT FROM AUTHOR]

With the growth of sustainability challenges, the automotive is regarded as one of the most important and strategic industries in the manufacturing sector. Reducing time in the product development process, seeking higher product quality, maintaining sustainable products, lowering product cost in the manufacturing process, and fulfilling customers' requirements are the key factors of the success of a company. To achieve these requirements, automotive companies must consider the use of new sustainable models that ensure design efforts, customer, and societal needs from product ideation until its end-of-life. To do so, the leading companies adopt Design for X (DFX) as a concurrent approach, which considers several issues through different factors Xs. However, with the modified applications for various domains, several researchers have developed many DFX techniques. This multiplicity makes it difficult for researchers and practitioners to keep up with DFX development. Hence, the aim of this paper is first to use mixed and different techniques to organize and select the most prominent DFXs that consider quality and customer satisfaction strategies in designing automotive product. Second, a conceptual framework called, Design for Relevance (DFRelevance) is introduced. It addresses the design factors (guidelines) of each DFX and their associated modules to facilitate the collaboration between designers and all the project team during the whole product lifecycle. Furthermore, a modeling approach based on unsupervised learning is used to accomplish DFRelevance concerns. The aim of this approach is to cluster similar modules into homogenous groups to facilitate the simultaneous implementation of the concurrent engineering strategy. [ABSTRACT FROM AUTHOR]

Engineered products often have more social impacts than are realized. A product review was conducted to bring this to light. In this paper, we show the extent to which different social impacts in 11 impact categories are co-present in 150 products and how this can help engineers and others during the product development process. Specifically, we show how social impact categories not previously considered can be identified. The product review resulted in 13,200 data points that were divided into two data sets, one with 8800 data points from which a social impact probability table was created. The remaining data points were then used to validate the table. All data points were then combined to create a final social impact probability table. This table provides insight for how various social impact categories correlate and can assist engineers in expanding their views to include additional social impact objectives and thus achieve a design with broader social impact or a design with minimized unwanted negative social impact. A simple method for predicting social impact is also created in order to assist engineers when developing products with social impacts in mind. [ABSTRACT FROM AUTHOR]

All products impact the lives of their users, this is called social impact. Some social impacts are commonly recognized by the engineering community, such as impacts to a user's health and safety, while other social impacts can be more difficult to recognize, such as impacts on families and gender roles. When engineers make design decisions, without considering social impacts, they can unknowingly cause negative social impacts. Even harming the user and/or society. Despite its challenges, measuring a program's or policy's social impact is a common practice in the field of social sciences. These measurements are made using social impact indicators, which are simply the things observed to verify that true progress is being made. While there are clear benefits to predicting the social impact of an engineered product, it is unclear how engineers should select indicators and build predictive social impact models that are functions of engineering parameters and decisions. This paper introduces a method for selecting social impact indicators and creating predictive social impact models that can help engineers predict and improve the social impact of their products. As a first step in the method, an engineer identifies the product's users, objectives, and requirements. Then, the social impact categories that are related to the product are determined. From each of these categories, the engineer selects several social impact indicators. Finally, models are created for each indicator to predict how a product's parameters will change these indicators. The impact categories and indicators can be translated into product requirements and performance measures that can be used in product development processes. This method is used to predict the social impact of the proposed, expanded U.S. Mexico border wall. [ABSTRACT FROM AUTHOR]