Search

Modelling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a phenomenon, one is interested, e.g., in so-called T-optimal designs. These consist of the (design) points from a generally continuous design space at which the models deviate most from each other under the condition that they are best fitted to those points. Thus, the T-criterion represents a bi-level optimization problem, which can be transferred into a semi-infinite one but whose solution is very unstable or time consuming for non-linear models and non-convex lower- and upper-level problems. If one considers only a finite number of possible design points, a numerically well tractable linear semi-infinite optimization problem arises. Since this is only an approximation of the original model discrimination problem, we propose an algorithm which alternately and adaptively refines discretizations of the parameter as well as of the design space and, thus, solves a sequence of linear semi-infinite programs. We prove convergence of our method and its subroutine and show on the basis of discrimination tasks from process engineering that our approach is stable and can outperform the known methods. [ABSTRACT FROM AUTHOR]

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model. [ABSTRACT FROM AUTHOR]

Thin-film flows are frequently used in process engineering and are therefore of great interest in research. In most cases, waves occur which have a positive effect on the material and heat transport and significantly influence the dynamics of the film flow. A combination of µ-particle image velocimetry and confocal chromatic distance measurement is used to measure phase-averaged flow velocities in naturally developed film waves. A trigger signal is sent to the PIV system and a recording starts when a wave reaches the wave crest threshold. This makes it possible to trigger the µPIV system depending on the film thickness. The focus of this work is on the triggering of the wave crest. The functionality and quality of the measurements depend on the correct positioning of the confocal chromatic sensor, with the light spot at the level of the camera section. Simultaneously, the minimum, average, and maximum film thickness, as well as the wave amplitude and wave frequency, are recorded. Film flow is investigated on a glass plate at various angles (5°, 10°, 15°) of inclination, Reynolds numbers (30-200), and measurement positions. The results are compared with the theoretical solutions according to Nusselt. In addition, the influence of the tilt angle and the measuring position is explained. The measuring position, in particular, has a non-negligible influence since the characteristics of the film flow are largely dependent on the streamwise measuring position. Compared to the theoretical solutions according to Nusselt, the velocities and film thicknesses are significantly greater due to the presence of the waves. With increasing inclination angle and streamwise measuring position, the measured mean and maximum velocities of the film flow increase. They almost double when the inclination angle increases from 5° to 15°. When comparing the velocities depending on the measuring position (300–500 mm), it is noticeable that the velocities increase between 5 and 15%, depending on the angle, due to wave development and gravity. [ABSTRACT FROM AUTHOR]

Thread milling is increasingly used in engineering manufacturing because of its advantages, but there must be some error in the thread due to the machining principle. In response to this problem, this paper discussed the causes of thread milling error and established the machining error model for the full machining process of thread milling. Firstly, the geometric model of thread milling is developed based on the parametric definitions of the cutter tooth surface, thread surface, and tool-path. Then, based on the cutter motion characteristics of the thread milling penetration and retraction segments and machining segment, the machining error model is established. The calculation of machining error is transformed into solving for the relationship between the position of the intersection of the cutter profile line and the thread profile line generated in the cross-section, which means that the three-dimensional problem is transformed into the two-dimensional problem. Finally, the proposed model is proven via thread milling experiments. The simulation results of machining error agree well with the experimental results, which prove the correctness and validity of the model. The model provides theoretical support for further research into thread milling machining error. [ABSTRACT FROM AUTHOR]

The industrial domain is undergoing a major transformation, pushed forward by emerging technologies originating from research or industry. The resulting trend, better known by the term Industry 4.0, advances automation within these manufacturing companies by providing ubiquitous interconnection. This enables the integration of technologies mainly used in the Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS) or Big Data with the goal to optimize production processes or facilitate intelligent decision-making. While those processes are progressively supported by methodologies coming from the area of artificial intelligence (AI) like machine learning algorithms, sustainable and consistent storing of production data becomes increasingly important. Concluding, production systems engineering and information engineering are correlating with each other, as the respective results could be used to the advantage of the respective other discipline. In order to address these issues while developing such flexible production systems, the Reference Architecture Model Industrie 4.0 (RAMI 4.0) has been introduced. However, practical applications are lacking as this standard is mainly described in theory, which makes it difficult to actually apply this framework. Thus, the main goal of this paper is to specify a detailed architecture description of the Information Layer to ensure the practical application of RAMI 4.0, which allows stakeholders to utilize model-based Systems Engineering (MBSE) for developing data aspects of industrial systems on the one hand and enable Information Engineering on the other hand. Supported by the concept of the Zachman Framework, the resulting architecture is applied and validated with the help of a real-world case study. [ABSTRACT FROM AUTHOR]

In order to enhance the strength of 3D-printed parts made of polymer materials and reduce the anisotropy caused by the fused filament fabrication process, this paper proposes an inter-layer interleaved composite path planning method based on the directional partition of the principal stress field. This method not only ensures intra-layer reinforcement under specific working conditions but also increases the strength of the printed part by enhancing the adhesion of adjacent layers through staggered filling between layers. The mechanical performance is improved by 10 to 30% compared to other conventional filling patterns such as the zigzag filling algorithm. Inter-layer interleaved composite enhancement path planning method is suitable for path planning of structures with complex shapes and is easily integrated into existing general computer-aided engineering processes. [ABSTRACT FROM AUTHOR]

Turning is recognized as one of the main manufacturing processes. Feed and cutting depth are investigated by researchers who aim to continuously improve this process. Faced with sustainability challenges, "greener" manufacturing engineering is sought, using statistical methods and machining techniques with minimal or no use of cutting fluid. Generally, roughness quantifies the quality of the machined surface, an important property in tribology. Classical amplitude parameters are insufficient to determine functional properties and are necessary to associate them with more significant ones. Chip formation also influences surface quality, as it is related to the interaction at the chip/tool interface. This study investigated the influence of the feed rate and cutting depth on the Rq, Rsk, Rku, Rk, Rpk and Rvk roughness parameters, and the chip thickness ratio during longitudinal dry turning of AISI 1020 steel. Trend measurements, a normality test, factorial analysis of variance and linear correlation were used in the methodology. Results showed predominant influence and correlation of the feed on the roughness parameters and the chip thickness ratio compared to the cutting depth. The analysis performed on the insert rake surface led to the hypothesis that the chip breaker geometry provides changes in chip deformation as the cutting depth increases. Through linear and polynomial trend lines drawn on the graphs, it was found that the roughness and the chip thickness ratio have a positive correlation with the feed, but in different proportions. [ABSTRACT FROM AUTHOR]

Over the past few decades, several methods have been introduced for the recognition of machining features from design files. These methods entail considerable effort to infer the characteristics of machining features from the design and manufacturing information commonly expressed in different and incompatible formats on the geometry, topography, dimensions, tolerances, and surface finishes. The competence of these traditional methods in implementing their assignment is still challenging. The deep learning approach as an advanced facility in artificial intelligence is considered as a promising substitute. This approach has already been successfully and efficiently employed in different facets of social life. Its use in manufacturing engineering and industries is considered as a breakthrough, especially in some disciplines such as process planning, and machining feature recognition is in its embryonic phase. The input data in the present methods is the output information of a computer-aided design system. This is commonly faced with some limitations, the most serious ones of which are the loss of data from the design file occurring due to the geometric interference of features and slow extraction of features due to the extensive and excessive information existing in the design files. In the present study, a deep learning-based system named MFR-Net has been developed for the recognition of machining features from the images of workpieces. In addition to CAD systems, other tools such as the camera images of workpieces can also be employed to enter the input. The MFR-Net can also identify other relevant information needed for machining, including the position of the features, dimensions, and various symbols such as numbers, decimal points, and positive and negative signs employed in tolerances, parentheses, etc. [ABSTRACT FROM AUTHOR]

Machine learning methods have shown potential for the optimization of production processes. Due to the complex relationships often inherent in those processes, the success of such methods is often uncertain and unreliable. Therefore, understanding the (algorithmic) behavior and results of machine learning methods is crucial to improve the prediction of production processes. Here, mathematical tools may help. This paper shows how efficient algorithms for the training of neural networks and their retraining in the framework of transfer learning are expressed in a discrete as well as a time-continuous formulation. The latter can be analyzed and investigated using mathematical techniques from kinetic gas dynamics. The results obtained provide a first step towards explainable artificial intelligence. Based on the mathematical description, an adapted ensemble method for retraining of neural networks is proposed and compared with backpropagation algorithms. The process of training and retraining is a common task and therefore demonstrated for two very different production processes. The first one involves the prediction of specific cutting forces and the second one the prediction of particle properties in a plasma spraying coating process. For both use cases, the presented algorithms are applied and their performance is evaluated giving thereby an indication how mathematically inspired methods improve classical tasks in production processes. [ABSTRACT FROM AUTHOR]

Modern cutting tools like end mills, drilling tools, and reamers underlie high requirements regarding geometrical accuracy, cutting edge quality, and production costs. However, the potential for process optimization is limited due to the process kinematics during grinding. Consequently, a novel tool grinding process for the manufacture of cutting tools has been developed recently at the Institute for Production Engineering and Machine Tools (IFW). This continuous generating grinding process allows the simultaneous production of all flutes and circumferential flank faces of rotational symmetrical cutting tools. The present paper focuses on the geometrical process design and develops a method to determine the necessary basic rack and process parameters in order to create a desired cutting edge geometry by continuous generating grinding. The developed method can define all parameters with an accuracy of up to 5 µm and 0.2° within a simulation in five iteration steps and allows not only the quantitative design of the cutting tool geometry but a qualitative modification of the flute geometry as well. Subsequently performed grinding tests showed that the presented method allows the design of grinding worms for continuous generating grinding of cutting tools and enables the successful implementation of these processes. [ABSTRACT FROM AUTHOR]

Optimizing machining parameters and pass numbers under machining stability constraint is important for manufacture engineers planning a multi-pass milling operation. Considering that milling stability varies with the machine tool dynamics when the tool and workpiece position changes, a generalized regression neural network (GRNN) is adopted to obtain the position-dependent stability constraint. Then a multi-objective optimization model of a multi-pass milling is established, where the objectives are the total cutting time (Tc) and surface roughness (Ra) and the variables are the machining position and cutting parameters for each pass. A non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the multi-objective optimization model for providing an optimal Pareto front, from which one satisfactory solution well balancing the Tc and Ra is selected by combining the entropy-weighted algorithm (EWA) and technique for ordering preferences by similarity to ideal solution (TOPSIS). A case study was carried out to establish a multi-objective optimization model after the milling stability constraint and surface roughness were predicted with the aid of GRNN and back-propagation neural network (BPNN), respectively. An ideal solution containing optimal machining position and cutting parameters was solved using NSGA-II, EWA, and TOPSIS, which was compared with solutions of two mono-objective models for optimizing Tc and Ra, respectively, to validate its feasibility. The observed stability and acceptable Ra value of the milling test under the ideal solution also indicated that the proposed optimization method can realize a trade-off between two conflict objectives and fascinate the process planning of a multi-pass milling. [ABSTRACT FROM AUTHOR]

Process reuse technology has been widely studied and applied in the manufacturing industry. However, the current NC machining process reuse generally assumes that the micro process is compatible with the macro process but, in fact, reusable processes from similar local structures from multiple parts are usually incompatible with each other when under the overall manufacturing requirements of the target parts. As a result, a large number of user interactions are still required for modification and adjustment in practical engineering applications and process reuse do not provide a significant improvement to the design efficiency. Therefore, an efficient NC process scheme generation method based on reusable macro and micro process fusion is proposed in this paper. Firstly, according to the calculation of semantic distance of process design intention, the micro process is mapped to the macro process to realize fusion of the two processes, and a compatibility credibility evaluation model is established to evaluate the fusion results. Then, when the fusion result is credible, the machining areas corresponding to the process scheme are adjusted and optimized at the geometric level. The adjustment and optimization of machining areas provides the integration of machining areas and the optimization of the machining sequence. Finally, the effectiveness and feasibility of the proposed method are verified through a test of example parts. [ABSTRACT FROM AUTHOR]

Game-based learning (GBL) is familiar in several respects, as it can embed problem-solving challenges in an interactive virtual environment to improve students' motivation. Designing a stealth assessment is a tedious task that requires experienced professionals to manually derive evidence and competency approaches to precisely deduce student knowledge and skills. This necessitates that stealth assessment designers perform manual feature engineering and develop probabilistic graphical models. Since the manual feature engineering process is complex, this paper develops Optimal Deep Learning with GBL (ODL-GBL) for stealth assessment. The proposed ODL-GBL model involves parameter-tuned bi-directional long short-term memory (Bi-LSTM) with an oppositional salp swarm algorithm. The optimal Bi-LSTM model extracts hierarchical features from low-level data in an automated way (e.g., a series of student behaviours in a GBL platform) to infer evidence for competencies. The proposed ODL-GBL model alleviates the need for the costlier and more tedious process of developing evidence models. A detailed experimental analysis is used to validate the effective predictive performance of the proposed method. Among all the compared methods, the Bi-LSTM model shows superior performance, with accuracies of 60.56% and 61.34% under 70 and 40 hidden units, respectively. [ABSTRACT FROM AUTHOR]

Engineering plastics have specific properties in strength, hardness, impact resistance, and aging persistence, often used for structural plates and electronic components. However, the holes made by the drilling process always shrink after the cutting heat dispersion due to their high thermal expansion coefficient. Drilling parameters must be discussed thoughtfully especially in the small-hole fabrication to acquire a stable hole quality. This study developed parameter models by the Taguchi-based neural network method to save the experimental resources on the drilling of engineering plastic, polyetheretherketone (PEEK). A three-level full-factorial orthogonal array experiment, L27, was first conducted for minimizing the thrust force, hole shrinkage in diameter, and roundness error. In terms of the network modeling, four variables were designated to the input layer neurons included the three drilling parameters (spindle speed, depth of peck-drilling, feed rate) and the thrust force detected, and that of the output layer neurons were two hole characteristics of diameter shrinkage and roundness. The models were trained by a stepped-learning procedure to expand the network's field information stage by stage. After three stages of training, the models developed can provide precise simulations for the network's training sets. For the non-trained cases, the prediction accuracy of the hole's characteristics discussed was below 1 μm in the drilling of a 1-mm-diameter hole. [ABSTRACT FROM AUTHOR]

The present study aims to increase pyruvate production by engineering Yarrowia lipolytica through modifying the glycerol metabolic pathway. Results: Wild-type Yarrowia lipolytica (Po1d) was engineered to produce six different strains, namely ZS099 (by over-expressing PYK1), ZS100 (by deleting DGA2), ZS101 (by over-expressing DAK1, DAK2, and GCY1), ZS102 (by over-expressing GUT1 and GUT2), ZS103 (by over-expressing GUT1) and ZSGP (by over-expressing POS5 and deleting GPD2). Production of pyruvate from engineered and control strains was determined using high-performance liquid chromatography (HPLC). Subsequently, the fermentation conditions for producing pyruvate were optimized, including the amount of initial inoculation, the addition of calcium carbonate (CaCO3), thiamine and glycerol. Finally, for scaled-up purposes, a 20-L fermentor was used. It was observed that pyruvate production increased by 136% (8.55 g/L) in ZSGP strain compared to control (3.62 g/L). Furthermore, pyruvate production by ZSGP reached up to 110.4 g/L in 96 h in the scaled-up process. We conclude that ZSGP strain of Y. lipolytica can be effectively used for pyruvate production at the industrial level. [ABSTRACT FROM AUTHOR]

As a nontraditional processing technology, electrochemical discharge machining (ECDM) can process glass and engineering ceramics precisely. This technology has proven to be a potential process for glass 3D microstructure. However, the key to expanding the application of ECDM is how to improve machining accuracy. This research conducted micro-hole and microgroove machining. Power voltage and frequency influence on hole processing efficiency, hole entrance diameter, and hole limit depth was explored. We considered four factors affecting ECDM—the voltage and frequency of the pulse power supply, the tool electrode feed rate, and the rotation speed. We studied their influence on the roughness of the microgrooves. The results show that machining efficiency, entrance diameter, and limit depth of micro-holes increased with the increase in voltage but decreased with the increase in power frequency. The results show that the roughness of microgrooves has an apparent positive correlation with the power voltage. In contrast, it had an evident negative correlation with the power frequency and electrode speed. The bottom surface roughness of microgrooves can be as minor as 0.605 μm. Various complex 3D microstructures on the glass surface by layer-by-layer method proved the great potential of ECDM. [ABSTRACT FROM AUTHOR]

To meet the requirement of lightweight, an increasing number of solid shafts are designed to be hollow in transportation industry. In this study, a novel method of flat-knifing cross-wedge rolling (FCWR) with single guide is proposed including a modified roller, a horizontal mill, and a single-guide structure, and its key problems are studied by numerical simulations and experimental tests. A mathematical model of FCWR roller is established, which reveals that the wedge length of rollers is effectively reduced by modifying knifing wedge from normalized roller. Furthermore, a horizontal multifunctional mill is invented and constructed to carry out the FCWR experiment with single guide. According to the results from the numerical simulations and corresponding experiments, it is observed that the typical defects of hole expansion and knifing groove are absolutely avoided because the improved flat-knifing wedge produces a radial force to shrink the inner hole and avoid the deformation concentration of the outer surface during knifing stage. Moreover, the single-guide rolling performed in the horizontal mill efficiently improves rolling stability because the workpiece is restricted into a smaller workspace. To the authors' knowledge, all these integrated improvements of FCWR roller, single-guide rolling, and horizontal mill are innovative, which are of great engineering significance to manufacture hollow shafts on account of the advantages of avoiding forming defect, reducing roller diameter, improving rolling stability, and simplifying mill structure. [ABSTRACT FROM AUTHOR]

Glass-fibre-reinforced unsaturated polyester (GFRUP) is one the thermoset composites that has many applications in manufacturing engineering. CO2 laser cutting of this composite has been significantly developed in industries. It is believed that the presence of glass fibre network in the matrix of thermoset affects the laser cutting process of this composite. In addition, the three-dimensional matrix of monomers along with carbon atoms in the atomic network's joints can also influence the laser cutting parameters. The aim of this research is to experimentally investigate the effect of these complicated structural properties on the CO2 laser-air cutting of GFRUP sheets. First, the maximum cutting speeds, in which the cut occurs, were obtained for various sheet thicknesses using a variety of powers. The kerf width was measured with using an optical stereo-microscope. Then, the laser cutting mechanism was discussed using the SEM of the cut edge surfaces. The volume cutting efficiency was also studied. The results indicate that the upper kerf width is wider than the lower kerf width. The upper kerf width is larger than the spot size of 0.3 mm. The chaotic striations on the cut edge surface are the resolidified carbon-glass fibres compounds and can be seen as something like stalactites which are in black due to the carbon dust. Opening of the lower kerf due to the shrinkage of the resolidified carbon-glass fibre compounds is reported as a phenomenon. The cutting efficiency decreases as the sheet thickness increases. [ABSTRACT FROM AUTHOR]

In this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches. [ABSTRACT FROM AUTHOR]

Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge. [ABSTRACT FROM AUTHOR]

The theory of fractional integral inequalities plays an intrinsic role in approximation theory also it has been a key in establishing the uniqueness of solutions for some fractional differential equations. Fractional calculus has been found to be the best for modeling physical and engineering processes. More precisely, the proportional fractional operators are one of the recent important notions of fractional calculus. Our aim in this research paper is developing some novel ways of fractional integral Hermite–Hadamard inequalities in the frame of a proportional fractional integral with respect to another strictly increasing continuous function. The considered fractional integral is applied to establish some new fractional integral Hermite–Hadamard-type inequalities. Moreover, we present some special cases throughout discussing this work. [ABSTRACT FROM AUTHOR]

Wetting is a fundamental and important phenomenon that is encountered in various engineering processes, and particularly, the understanding of mesoscopic dynamic wetting of not only pure liquids but also suspensions with nanoparticles is required with the recent miniaturization of devices. Therefore, it is important to establish a technique to precisely measure the dynamic behavior of liquid thin films near the three-phase contact line. In the present study, the mesoscopic wetting behavior of nonvolatile polydimethylsiloxane (PDMS, 20 cSt) and a PDMS suspension containing PDMS-modified SiO2 nanoparticles at a concentration of 1 wt% is measured using a phase-shifting imaging ellipsometer (PSIE) developed in our previous study, although the aggregates of nanoparticles are formed in the suspension. The PSIE can measure the two-dimensional thickness profile of liquid thin films from nanometer to micrometer scales. From the measured thickness profiles, we investigate the effect of the suspended nanoparticles on the apparent contact angle θa and precursor film length Lp. The dependence of θa and Lp on the velocity of contact line U ranging from 10–8 to 10–6 m/s is characterized. The contact angles of both fluids reflect the Cox–Voinov law, i.e., the θa is proportional to U1/3. At the same U, the θa of the suspension is higher than that of pure PDMS. The Lp of both fluids is proportional to U−1 and is the same at the same U. Furthermore, the Lp and thickness profiles at the nanometer scale are consistent with adiabatic precursor film theory. [ABSTRACT FROM AUTHOR]

Mass customization (MC) is recognized as a new manufacturing paradigm for market competition, and several empirical studies have discussed elements of organizational structure that are conducive to MC. A previous study proposed a model of quality at source and identified cultural values that can be changed by cultural artifacts, such as the stop-the-line system and the Andon system. This paper explores the impacts of three groups of MC enabler variables (personalized design, modular design, and automated processes) on two internal dimensions of organizational culture (work cooperation and relationship harmony).This paper surveyed 160 samples across five manufacturing industries. The results confirm that the three MC enablers (personalized design, modular design, and automated processes) correlate positively with two internal dimensions of the organizational culture of Chinese manufacturers (work cooperation and relationship harmony). Of the two internal dimensions, work cooperation enhances organizational performance, while relationship harmony has no impact on organizational performance. Modular design, automated processes, and work cooperation are powerful tools in the implementation of MC and organizational performance. Personalized design and relationship harmony are useful in MC. However, these tools are intermediate and limited, and they do not contribute to the general performance of the entire organization. Thus, in terms of internal- and team-oriented cultural factors, work cooperation is more important than relationship harmony in the implementation of MC. Relationship harmony is a tool for managing work atmosphere, and this is a new characteristic feature of MC as a new operational paradigm. [ABSTRACT FROM AUTHOR]

The processing and interpretation of microseismic (MS) signals are the basis for obtaining source information of MS events in rock engineering. However, MS signal processing is subjective and time-consuming, in which different strategies have to be empirically adopted to cope with different issues. To facilitate MS signal analysis, this study proposes an entirely data-driven method based on local mean decomposition (LMD), which has been applied in various signal processing fields. Through some numerical experiments, the superiorities (e.g., smaller reconstruction errors and fewer sifting iterations) of the proposed method over other methods are demonstrated, and the proposed method is identified as an effective filtering and frequency analysis tool to process recorded MS signals. Our analysis results show that the proposed method can effectively remove interfering noise existing in MS signals collected from the Lianghekou hydropower station during underground rock excavation. Moreover, the proposed method can be employed to analyze the frequency revolution features of MS signals before and after rock mass failure. In addition, it is found that MS signal frequency migrates to a low-frequency range when approaching rock failure, which can be regarded as an early warning indicator of rock instability. The proposed method is believed to facilitate MS monitoring and offer a new clue to conduct MS signal processing and interpretation. [ABSTRACT FROM AUTHOR]

To meet lightweight demands, complex support or near-net shaped structures are increasingly applied in modern industry sectors. Wire arc additive manufacturing (WAAM) of these structures requires a profound knowledge of the interactions between material properties, welding process and mechatronic engineering. This study shows a new WAAM strategy, termed skeleton arc additive manufacturing (SAAM), adapted especially for building freeform wire-structured aluminium component, and further contributes a bead modelling study to establish welding parameters, geometric features and material properties relationship. The results show that good geometrical features and sound metallurgical properties can be achieved for wire-structure aluminium parts using the proposed approach. The strut diameter gradually increased with the amplified welding arc-on time and wire feed speed; however, they present significant changes in the microstructural evolution and mechanical strength. A sample demonstration further displays an effective path to create typically wire-structure part with good quality and offers the main processing challenge and coping strategy during SAAM. [ABSTRACT FROM AUTHOR]

Current rock mass quality classification methods include the rock mass rating (RMR), the Q system, the geological strength index (GSI) and the basic quality (BQ) system. Among these methods, the BQ system has been adopted as the standard for engineering classification of rock mass in China. The base value of the BQ system is determined by the intactness index of the rock mass (Kv) and the uniaxial compressive strength of intact rocks (UCS). However, it is not always possible to calculate Kv in engineering processes, thus making it difficult to classify a rock mass. In this study, the relationship among the Kv, the rock quality designation (RQD), and the Lugeon value (ω) is established using Maerdang hydropower station, China, as a case study. Results from this study enable the proposal of a modified BQ system based on UCS, RQD, and the Lugeon value. By comparing evaluation results obtained using the original BQ system with this proposed system, very similar results were obtained for rock mass at the Maerdang hydropower station. The addition of more engineering examples over time will enable further verification of the proposed modified BQ system. [ABSTRACT FROM AUTHOR]

Safe construction and operation of tunnels in high seismic risk regions require special aseismic design. A possible solution to this issue is to consider the earthquake damage that a tunnel may suffer beforehand in the process of engineering rock mass classification during the geotechnical investigation phase. Then, the tunnel support system is designed with this modified classification result. To verify this hypothesis, this work investigates the Chinese engineering rock mass classification system, the basic quality (BQ) rating system, considering seismic effects. The main contribution of the current work is to quantitatively modify the rock mass intactness index Kv in the BQ system considering the potential earthquake damage under various levels of design peak ground acceleration. The procedure to obtain the rock mass basic quality index considering the design peak ground acceleration is proposed and verified with real field data from the 2008 Wenchuan earthquake. This approach is a promising enhancement of the empirical methods for considering the seismic effects for tunnels that require special design against possible earthquake impacts. [ABSTRACT FROM AUTHOR]

Estimates of the measurement uncertainty are crucial, without which precision measurements are useless from a practical point of view. Dimensional compliance with geometric product specifications (GPS) plays a key role in decision-making in manufacturing engineering. Without rigorous uncertainty assessment, manufacturers risk making incorrect decisions. Coordinate measuring machine (CMM) measurements are suitable when complex measurement tasks of a workpiece must be made, such as measurements of positional tolerances or repetitive measurements. Here, we attempted to solve problems related to the thermal influences in the assessment of uncertainty in Coordinate Metrology. Task-specific experiments based on the identification of residual errors using a hole plate standard calibrated by Physikalisch-Technische Bundesanstalt (PTB) were performed to compare the measurement uncertainty results in the different temperature ranges. The thermal issues of CMMs include measurements of the temperature of the environment and CMM errors, estimation of the uncertainties of the thermal errors of the CMM, and reduction of the uncertainties related to thermal errors of CMMs. Computation of the thermal errors must account for environmental influences and internal heat sources of the CMM. Assessment of the uncertainty of thermal errors covers almost all factors related to the uncertainty of the CMM. The state-of-the-art technique for determining the uncertainty of the measurements is discussed. [ABSTRACT FROM AUTHOR]

Within product development processes, computational models are used with increasing frequency. However, the use of those methods is often restricted to the area of focus, where product design, manufacturing process, and process chain simulations are regarded independently. In the use case of multi-material lightweight structures, the desired products have to meet several requirements regarding structural performance, weight, costs, and environment. Hence, manufacturing-related effects on the product as well as on costs and environment have to be considered in very early phases of the product development process in order to provide a computational concept that supports concurrent engineering. In this contribution, we present an integrated computational concept that includes product engineering and production engineering. In a multi-scale framework, it combines detailed finite element analyses of products and their related production process with process chain and factory simulations. Including surrogate models based on machine learning, a fast evaluation of production impacts and requirements can be realized. The proposed integrated computational product and production engineering concept is demonstrated in a use case study on the manufacturing of a multi-material structure. Within this study, a sheet metal forming process in combination with an injection molding process of short fiber reinforced plastics is investigated. Different sets of process parameters are evaluated virtually in terms of resulting structural properties, cycle times, and environmental impacts. [ABSTRACT FROM AUTHOR]

Riveting is an important interference-fit joining technology, which has been widely applied in aircraft assembly and many manufacturing fields. The rivet head flushness is an import industrial standard for the quality evaluation of a riveted joint. It would influence the aerodynamic performance of aircraft if not carefully controlled. In this paper, a finite element model (FEM) of automatic countersunk riveting is established and the formation of rivet head flushness is studied by the simulation results. Then, based on the coordinated motion of the riveting bar and the anvil tool, several compensation strategies are proposed to reduce the rivet head flushness and ensure the riveting quality, in which the riveting bar and anvil tool feed with the same speed (RASS) strategy is considered as the most efficient one. Finally, riveting experiments are conducted on a dual-machine-based automatic drilling and riveting system. The experimental results indicate that the simulation result is accurate, and the RASS compensation strategy is applicable and effective. This paper studies the formation mechanism and compensation method of the rivet head flushness for the countersunk rivet and provides scientific guidance for the riveting process optimization in engineering applications. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant global shift towards use of polymer matrix composite materials in a wide range of industries, including aerospace, automotive, marine, and sports, among others. Despite the rapid uptake and widespread adoption of this material technology, there are still technical challenges faced daily by manufactures due to the inherent complexities of different composite processing techniques. This paper aims at establishing a new scheme for better quality control management of resin transfer molding (RTM) processing for thermosetting composites, using a Bayesian belief network (BBN). The data were collected through knowledge engineering among the manufacturing experts with the help of real-life application history. A total of 13 governing factors for the RTM process, which would theoretically have a role on the final product quality, were identified. The sensitivity analysis of the BBN showed that the major contributing factors of the quality are the resin viscosity profile, part design features, resin cure peak temperature, and reinforcement permeability. The conditional probability tables were constructed using a quality index from industrial experts, and the causal relationships captured by the BBN were built using knowledge engineering. It is also shown how the basic BBN model can be further updated by integrating the interaction weights between the attributes that define the product quality. [ABSTRACT FROM AUTHOR]

At present, the majority of welding robots belong to the teach-and-playback category in welding manufacturing engineering applications; trajectory teaching in advance of welding is time-consuming and lack of efficiency. The currently published welding seam tracking methods are also based on existing trajectories. Intelligent Welding Manufacturing (IWM) is the current research focus in welding manufacturing. The core technology of IWM is Intelligent Robot Welding Technology (IRWT). A sensing-technology-based system was the key to realize IRWT. In this paper, a teaching-free welding method based on laser visual sensing system (LVSS) for robotic gas metal arc welding (GMAW) is studied. First, a LVSS was established. A fast-unified calibration method for LVSS is proposed to improve the calibration efficiency. Then, using an image processing method based on prior knowledge, feature point with sub-pixel accuracy can be obtained in real-time. Finally, an online welding trajectory planning method is proposed to implement teaching-free welding. In order to verify the accuracy and robustness of the proposed method, experiments on V-grooves and fillet welds were performed. The results showed that the control accuracy on the V-groove and the fillet welds is suitable for most robot welding applications. [ABSTRACT FROM AUTHOR]

The paper deals with the situation of manufacturing of customized engineering products under intense competitive environment. Such customized engineering products can be manufactured by different types of production systems such as job production, batch production, mass production, and flexible manufacturing systems. It then becomes an important decision to choose the best one among these production systems which is also dependent on multiple criteria. The three basic criteria predominantly considered in the context of manufacturing are cost, quality, and flexibility. The paper applies the efficient interpretive ranking process (IRP) as an interpretive multi-criteria ranking method to demonstrate its utility in this context. It considers the design of the decision problem by treating the cross-interaction matrix of 'Alternatives × Criteria' as a unit matrix, i.e. each alternative is linked with each criterion. In this type of decision problem, the IRP method is implemented with transitive dominance relationships. Further, it carries out the sensitivity analysis concerning different ordinal weights of various criteria. The paper also makes a methodological contribution in IRP by way of computing dominance index rather than simply considering net dominance (which would be either positive or nil or negative). The dominance index gives a better depiction of the dominance of one alternative over the others. [ABSTRACT FROM AUTHOR]

One of the exciting applications of laser directed energy deposition (LDED) is the fabrication of complex-shaped engineering components involving graded composition. In the present work, a 2 kW fibre laser-based LDED system is used to deposit copper-nickel (CuxNi(100−x) (x = 25, 50, and 75)) layers on copper (Cu) substrate. The single tracks are deposited at different combination of laser power, scan speed, and Cu-Ni compositions. Track geometry (width and height) indicates direct and inverse dependence with laser power and scan speed, respectively. For same LDED processing parameters, there is lower track height and wider track width with increasing Cu in Cu-Ni composition due to improved wettability. Further, analysis of variance (ANOVA) shows that laser power and scan speed are more significant for track width and track height, respectively. Graded bulk structures built at identified process parameters are characterized using optical microscopy, microhardness, and single cycle automated ball indentation (SC-ABI). Deposited Cu-Ni graded bulk has a density of > 99% with pure Cu and Ni phase. The microscopy analyses reveal defect-free deposition with fine dendritic growth in the bottom and middle layers, while equiaxed grains are observed in top layers. Microhardness measurements show a smooth hardness transition from deposit to the substrate with higher hardness. SC-ABI presents an increase in energy storage capacity by 2.25 times with the increase in Cu percentage. This work paves a way for fabricating Cu-Ni graded components for manufacturing engineering components. [ABSTRACT FROM AUTHOR]

The electrochemical micromachining (ECMM) system is a modern equipment well thought out as the phenomenon of conventional electrochemical machining (ECM) system in microscale. The ECM system and ECMM system are similar, but the practice method and mechanism involved in the ECM system are analogous to those of the ECMM system, i.e., the differences in machining conditions and machining performance give rise to a factor called the scale effect. In this paper, ECMM has been applied with the advantageous concept called scale effect. Single factor experiments are conducted in which voltage, duty ratio, and tool feed rate are considered as the input factors for the exploration of the different grades of scale effect through the material removal rate, circularity, and overcut. The scale effects that occurred during machining are found using the similarity precision of the micro- and macrosystems based on the similarity theory. Quantitative evaluation of the scale effects is done for the ECMM process. The evaluation showed greater similarity precision indicating a larger significant scale effect. The output values of MRR, circularity, and overcut were normalized which also showed a positive scale effect. The research on scale effects delivers benefits in engineering such as optimizing the processing parameters involved and improving the machining performances of ECMM. [ABSTRACT FROM AUTHOR]

Core samples to a depth of approximately 50 m and undisturbed soil samples were retrieved from eight locations in the Saga Plain, Japan, to investigate the effects of the depositional environment and post-depositional processes on the engineering properties of Quaternary clays in the Saga Plain. The engineering properties of these soil samples were measured, and the test results showed that the clays in the Saga Plain have a high void ratio, high water content, high compressibility, high sensitivity, and low stiffness. The one-dimensional compression curves plotted above the sedimentation compression line proposed by Burland, and the compression index was high, which indicated that the clays had been cemented to some degree. The clays have high strength sensitivity and high stress sensitivity, indicating that they have strong structures. It was considered that a considerable amount of volcanic ash from the four large volcanic eruptions that occurred during the deposition of these clays settled into the clays, resulting in a certain degree of cementation, which was attributed as the main driver of the strong structure in these clays. Comparisons of the NaCl concentration, pH, and oxidation-reduction potential (ORP) of the clay samples representing the initial depositional environment and the current ground environment showed that salt leaching occurred in the Ariake clay formation, and that the ground environment changed. When the NaCl concentration was less than approximately 2 g/L, the strength sensitivity of the Ariake clay samples with low-activity clay minerals significantly increased due to the decrease in the liquid limit and subsequent significant decrease in the remolded undrained shear strength. Therefore, salt leaching was probably a main cause of the high strength sensitivity of the clays in the Saga Plain. The depositional environment at some locations changed from an alkaline, reducing environment, to an acidic, oxidative environment, with the potential to change into a strongly acidic environment, which could have deteriorated the strength of the cement/lime-treated clays in the Saga Plain. [ABSTRACT FROM AUTHOR]

The evolution of any given product family in manufacturing enterprises follows the epicycles in product lifecycle development, thus generating many useful but unmanageable engineering documents. These documents record nearly all the information about product lifecycle and thus provide domain-specific concepts and significant relations for ontology extraction. A qualified ontology, in turn, facilitates the management and reuse of manufacturing knowledge. Consequently, this paper proposes a biologically inspired adaptive growth (BIAG) approach to learn domain ontologies (DO) from these documents. BIAG is modeled on the growth and development mechanisms of a tree, where genes and three key stages of tree lifecycle including seed formation, root development, and tree growth are reference for DO learning. In BIAG, axioms and constrains that serve the same function as genes of a tree are first generated by a semi-supervised learning algorithm, to control the construction process of DO; then three adaptive growth strategies inspired by the above three stages of tree lifecycle are proposed to extract DO from engineering documents. The results of the DO construction and application examples demonstrate the feasibility and effectiveness of BIAG, which could, therefore, be a good choice for DO construction towards manufacturing knowledge reuse. [ABSTRACT FROM AUTHOR]

Establishing how to effectively manufacture cell therapies is an industry-level problem. Decentralised manufacturing is of increasing importance, and its challenges are recognised by healthcare regulators with deviations and comparability issues receiving specific attention from them. This paper is the first to report the deviations and other risks encountered when implementing the expansion of human pluripotent stem cells (hPSCs) in an automated three international site–decentralised manufacturing setting. An experimental demonstrator project expanded a human embryonal carcinoma cell line (2102Ep) at three development sites in France, Germany and the UK using the CompacT SelecT (Sartorius Stedim, Royston, UK) automated cell culture platform. Anticipated variations between sites spanned material input, features of the process itself and production system details including different quality management systems and personnel. Where possible, these were pre-addressed by implementing strategies including standardisation, cell bank mycoplasma testing and specific engineering and process improvements. However, despite such measures, unexpected deviations occurred between sites including software incompatibility and machine/process errors together with uncharacteristic contaminations. Many only became apparent during process proving or during the process run. Further, parameters including growth rate and viability discrepancies could only be determined post-run, preventing 'live' corrective measures. The work confirms the critical nature of approaches usually taken in Good Manufacturing Practice (GMP) manufacturing settings and especially emphasises the requirement for monitoring steps to be included within the production system. Real-time process monitoring coupled with carefully structured quality systems is essential for multiple site working including clarity of decision-making roles. Additionally, an over-reliance upon post-process visual microscopic comparisons has major limitations; it is difficult for non-experts to detect deleterious culture changes and such detection is slow. [ABSTRACT FROM AUTHOR]

In the orbital mode of electrical discharge machining (EDM), an electrode with a complex profile causes deviation in the cavity profile from the design objective. In this paper, electrode compensation techniques according to the envelope theory are presented to eliminate deviation errors. The succession of orbits traced out a family of electrode curves based on kinematic geometry integrated with the motion path of tool bodies and constraints. The boundary of the family of electrode curves is referred to as the orbiting profile. The deviation error between the orbiting and objective profiles can be eliminated by using a compensation electrode in the orbiting process. Thus, the generated orbiting profile conforms to the objective profile. Experimental results of the tested example for the machining profile of the compensation electrode by using the orbital process revealed that profile deviation can be eliminated to ensure that the EDM profile closely conforms to the objective profile. This compensation method for orbiting EDM provided an exact process to engineers for easy control of the machining precision of EDM. [ABSTRACT FROM AUTHOR]

Recent trends show that products are becoming more complex and multi-variant. Therefore, future production systems need to become more advanced in terms of reconfigurability, flexibility, and transformability. To achieve these advancements, future systems must be highly changeable and support plug-and-produce approaches. The majority of today's engineering methods focus on static workflows based on predefined assets and setups. As a consequence, changes in the production system come with high costs, especially during production process execution. Therefore, new engineering methods are required which are explicitly designed for highly changeable production systems. To contribute towards fully changeable production systems, an I4.0 framework is proposed that covers the entire engineering process. The focus is set on presenting a graphical I4.0-enabled engineering method that enables dynamic workflows with varying assets and setups. Moreover, in order to evaluate the method, a user study was conducted, in which participants were asked to solve multiple engineering tasks by utilizing the presented I4.0-enabled method as well as a conventional approach. The results indicated that the proposed I4.0-enabled engineering method significantly outperformed the conventional method in terms of required engineering times and subjective ratings. [ABSTRACT FROM AUTHOR]

Selective laser melting, an additive manufacturing technique that allows 3D printing of metal components, has recently gained great interest. Despite its growing popularity, this technology continues to suffer from deficiencies in standards and qualifications, factors that limit wide industrial use. Numerical modeling is currently a standard tool in production engineering for both process optimization and a more comprehensive understanding of the process physics. However, inherent to any model implementation and computation are various sources of uncertainties and errors. It is of major importance to identify them and assess whether their influences on outputs is significant. This can be accomplished using sensitivity analysis. To determine the parameters that most influence variability in the computational results, global sensitivity analyses were performed using an in-house developed nonlinear finite element model of the selective laser melting process. The computational load was limited by utilizing a 2D model for a single-layer simulation. The studies were performed on 26 process parameters including material properties, their dependencies on temperature, laser-related parameters, and boundary conditions, among others. Computed maximal temperatures and melt pool widths and lengths were obtained. Two sensitivity analyses were performed using the elementary effect method: one included process parameters and the other excluded them. Among the 26 input parameters tested, 16 did not show significant effects in either study. By including process parameters, they were found to be the most influential. By excluding them, the significant influence of the emissivity coefficient on output variability was revealed. These results evidence the parameters that should be given higher priority in modeling, the sources of error to be considered during validation, and insights into which parameters should be prioritized for further studies, both experimental and computational. [ABSTRACT FROM AUTHOR]

This paper reports on a development of feature-focused dynamic routing policy and its evaluation in a flexible manufacturing systems (FMS) simulation framework. The dynamic policy is based on an integrative methodology in which process planning system IMPlanner is integrated with FMS simulation module. IMPlanner's rule-based system process selection system performs knowledge-intensive task of generating alternative processing options for each feature for parts in production plan. Generated alternative routings (process plan network) for each part are utilized in the FMS simulation module such that routing decision in FMS are made on periodic intervals by considering alternative processes for each feature and making decision based on the current system status and performance. The proposed framework has been evaluated in an experimental FMS simulation module, implemented in Arena, in which two performance criteria, machine utilization and WIP, were used to make routing decisions. The feature-focused approach is compared with traditional static decision-making, and its improved performance is demonstrated. [ABSTRACT FROM AUTHOR]

With increasing power density in modern integrated circuits, thermal issues are becoming a critical problem in System-on-a-Chip (SoC) testing. In this paper, we develop the thermal-aware test scheduling methods using Voltage/Frequency Scaling (VFS) and Test Partition (TP) to reduce the expensive Test Application Time (TAT). First, we develop a quick temperature estimation method in test scheduling to ensure the test temperature within the given range. Second, we propose a thermal-aware test scheduling method based on the mixed-integer linear programming model (MILP) (called STP-M) that applies VFS and TP to search the optimum scheduling and further reduce the TAT. Third, we develop a heuristic method based on Rectangular Strip Packing (called H-RSP) to quickly access the quasi-optimal scheduling. The experimental results on ITC’02 benchmarks showed that the STP-M obtains the most optimized result for every benchmark and saved 15.5% and 8.0% TAT on average compared with the existing works, while H-RSP takes less than 10 seconds to access the quasi-optimal scheduling that is close to that of STP-M. [ABSTRACT FROM AUTHOR]

This paper presents an experimental parameter optimization of electrochemical honing process (ECH) on the surface finish generation capability on cast iron of ASTM 35 with Brinell Hardness Hb 212. Taguchi orthogonal array of L9 experiment is used for optimization. Results from the main effect plot, regression analysis, correlation analysis, and interaction plots for the means indicate that best surface finish is obtained where the electric voltage is 15 V, electrolyte concentration is 20%, electrolyte pressure is 0.667 Mpa, and motor rotational speed is 60 rpm. However, significant variation is seen in the control of electric voltage as compared to the other three. Higher signal-to-noise ratio is observed on electric voltage followed by electrolyte concentration and electrolyte pressure signifying electric voltage is the determining factor. From standard deviation analysis, electrolyte pressure and electrolyte concentration are found to need close attention for better surface roughness along with electric voltage. The interaction plot for the three factors also indicates that electric voltage at highest level is more significant than the other factors. However, it is noted that the interaction of the electric voltage, electrolyte concentration, and electrolyte pressure significantly determines the effectiveness of the ECH process. Meanwhile, the level of control between electrolyte pressure and electrolyte concentration needs careful attention as the two factors work against each other. [ABSTRACT FROM AUTHOR]

This paper aims to enable an industrial robot to realize real-time adaptation to uncertainty, which is generally associated with the issue of machine flexibility of a flexible manufacturing system (FMS). A new robotic scheme called dynamic compensation robot with a new high-speed vision system is presented. Under the proposed scheme, a traditional multi-joint industrial robot is designated for fast and coarse global motion, whereas a direct-driven add-on module is realizing local compensation of accumulated uncertainty. 1000 fps image sensing and processing is realized within the new high-speed vision system. Overall latency of high-speed visual feedback was measured to be within 3.0 ms. As an early stage showcase towards flexible manufacturing application, contour tracing of planar target with uncertainty was evaluated. Effectiveness of the proposed method as well as the new high-speed vision was validated by experimental results. [ABSTRACT FROM AUTHOR]

In this study, a novel tool structure (i.e., the conductive cathode tip is retracted into the side-insulated layer) is proposed to improve the machining accuracy in electrochemical micro-machining (EMM). To deeply understand the effect of this novel tool, a multi-physics model taking electrical field, fluid field, and temperature field into account is developed for simulation. Using this coupled model, the effect of the tip retraction depth and machining conditions on the machined anodic profile is numerically studied to optimize the tool structure and process parameters. Subsequently, comparative experiments between the traditional and novel tool are performed to verify the simulation. The simulated and experimental results both show that the novel tool structure has the potential to effectively improve the shape accuracy and surface quality in EMM. [ABSTRACT FROM AUTHOR]

In assembly activities, performing assembly planning is a crucial issue for the human-centered manufacturing. The challenges involve in retrieving and utilizing the real-time data about human-based work activities in a shop floor. Instead of the simulation-based assembly planning, the marker-based motion capture system can acquire realistic motion data of workers in assembling sites, but this method is inclined to corrupt due to the occlusion and is troublesome to be installed within the shop floor. Therefore, based on the complement of the optical and inertial sensor, this paper presents a self-contained motion capture method for assembly planning in real shop floor. It can provide a real-time and portable motion capturing for workers, avoiding the failure of traditional outside-in motion capture system due to occlusions or incorrect installations. What is more, the portable motion capture method can run on consumer mobile devices, providing a convenient and low-cost way to perceive the workers' motion in shop floor, which is significant for extensive applications in assembly verification and planning for digital factories. Finally, experiments are carried out to demonstrate the accuracy and feasibility of the proposed motion capture method for assembly activities. [ABSTRACT FROM AUTHOR]

In order to reduce the effects of degradation and emission rate for production system, a good management of integrated maintenance and production strategies is necessary. In this context, this paper presented a jointly production, maintenance and emission problem for a manufacturing system subjected to degradation. The manufacturing system composed of one machine subjected to random failure, producing one type of product and causing harmful emissions. The degradation degree of the machine is increased with time and according to production rate and caused the decreasing of availability of the machine and the increasing of the emission rate. This paper proposed an optimal strategy of maintenance and production which integrates the emission control taking into account the impact of the system deterioration. The objective is to determine the economical production rate and the optimal maintenance strategy which minimize the total cost of production, inventory, maintenance and emission, basing a failure rate and emission relationship. A numerical example and a sensitivity analysis are applied to present the result efficiency of the theoretical study and to illustrate the robustness of the proposed strategies. [ABSTRACT FROM AUTHOR]

The increasing demand and the globalization of the market are leading to increasing levels of quality in production processes, and thus, nowadays, multiple product characteristics must be tested because they are considered critical. In this context, decision makers are forced to interpret a huge amount of quality indicators, when monitoring production processes. This fact leads to a misunderstanding as a result of information overload. The aim of this paper is to help practitioners when monitoring the capability of processes with a huge amount of product characteristics. We propose a methodology that reduces the amount of data in capability analysis by structuring hierarchically the multiple quality indicators obtained in the quality tests. The proposed methodology may help practitioners and decision makers of the industry in three aspects of statistical process monitoring: to identify the part of a complex production process that presents capability problems, to detect worsening over the time in multivariate production processes, and to compare similar production processes. Some illustrative examples based on different kinds of production processes are discussed in order to illustrate the methodology. A case of study based on a real production process of the automotive industry is analyzed using the proposed methodology. We conclude that the proposed methodology reduces the necessary amount of data in capability analysis; and thus, that it provides an added value of great interest for managers and decision makers. [ABSTRACT FROM AUTHOR]

Cellular bucket brigade (CBB) is known as the self-organizing and self-balancing production system, but in different constraints, its self-organizing feature also presents different characteristics. According to constraints of station, this article divides the CBB system into three types: (1) CBB-1, no obvious location, in which an arbitrary position can be an exchange of work; (2) CBB-2, location limited, in which any place can be a transition; and (3) CBB-3, location limited, which cannot be exchanged within the stations. Then, the corresponding mathematical models are established to analyze the performance of the CBBs. At last, numerical simulation provides the following results: Both CBB-1 and CBB-2 have only one balance point and good convergence from any states to balance point, while the balance point of CBB-3 is of uncertainty. [ABSTRACT FROM AUTHOR]