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With the introduction of new technologies, the scope of miniaturization has broadened. The conditions under which complicated products are designed, manufactured, and assembled ultimately influence how well they perform. The intricacy and crucial functionality of products are frequently only fulfilled through the use of high-precision components such as micro gears. In power transmission systems, gears are used in a variety of industries. Micro gears or gears with micro features, with tolerances of less than 5 m, are pushing manufacturing processes to their technological limits. Monte-Carlo simulation methods enable an accurate forecast of inaccuracies in compliance. The complexity of the micro gear's design, on the other hand, increases the simulation computation and runtime. An alternative method for simulation is to create a surrogate model to predict the behavior. This paper proposes a statistical surrogate model to predict the conformity of a pair of micro gears. Afterward, the advantage of the surrogate model enables the optimal tolerances assignment while taking gear functionality, and production cost into account.

Additive manufacturing (AM), and particularly metal laser powder bed fusion (L-PBF) processes are rapidly being industrialized. Still, the current L-PBF process lacks both process quality and reproducibility. For that reason, robust process monitoring needs to be developed to reduce the process variation and ensure quality. Accordingly, to deal with this issue, this work proposes a new approach to predict the quality of L-PBF products. The approach consists of selecting relevant statistical features from optical data and validating these features by assessing their ability to predict the different products’ density classes. The approach was applied on cubical specimens produced with different process parameters. Support vector machines (SVMs) were used as classification tools, and the first results are promising with a prediction accuracy higher than 90%.”

The need for highly reliable and precise products has forced industries to study potential uncertainties during designing needed parts. The reliability and acceptance of the product rely on several factors and tolerancing activity plays an important role to assure that the manufactured product meets the requirements. The importance of tolerancing activity can be noticed once designers prefer tight tolerances to ensure product performance and in contrast manufacturers want loose tolerances to reduce manufacturing and assembly complexity and then cost, to decrease the non-conformance rate. Therefore, tolerance allocation and inspection-planning design can be formalized as an optimization problem which the objective function represents the cost impacted by several aspects of the quality management: cost of failure, cost of the inspection. This paper details a modular cost model which includes four components: the manufacturing cost, the inspection cost, the scrap cost (internal failure), and the cost of external failure. Moreover, to improve the efficiency of the cost model, it integrates several factors such as frequencies of the monitoring and inspection activities, probability of conformed product, probability of non-detection of non-conformity, and probability of nondetection of confirmed. The applications of this model are illustrated and demonstrated through an industrial case study.

The life cycle of an assembled product faces various uncertainties considering the current state of the manufacturing line. Varied of activities are integrated with the manufacturing line including processing, inspection, reworking, assembly, etc. Therefore, any decision taken concerning each activity, will affect the end-product of the manufacturing line. In an early stage, designers define tolerances on parts to ensure the functionality of the end-product. In this regard, this paper integrates resource allocation (as a decision to assign practical resources to parts) and reworking decision (as a decision to improve parts conformity rate) into the tolerance allocation problem. A modular-based cost modelling approach is proposed objecting to minimisation of manufacturing cost concerning resource allocation and reworking decisions. Eventually, a genetic algorithm and Monte-Carlo simulation are adapted to analyse the applicability of the model.

Reconfigurable manufacturing system is an active field of research for more than two decades, due to its enhanced efficiency and high throughput. An important aspect of such system is process planning which assigns reconfigurable machines to different operations. This study examines the process planning problem subject to different defects and considers novel optimization criteria based on scrap cost, re-work cost, number of failed and conforming units produced by a process plan. A multi-objective model has been developed to optimize the total cost and the quality decay index of the process plan. Due to NP hard nature of the problem, a heuristic called multi-objective particle swarm optimization has been implemented and a numerical example has been analyzed. The results will help decision makers in understanding the impact of quality on process plan selection and a trade-off between different components of the proposed model.

The automotive sector is facing new challenges and increased competition nowadays. Customer satisfaction depends on products and parts quality, as well as possible customizations. To reach these objectives, productivity is key, meaning machines availability needs to be maxed and not impacted by unplanned breakdowns, which cost a lot of money and time, and possibly quality issues on parts produced during the deteriorating phase of the machine. Industry 4.0 will play an essential role, as it comes with new digital tools to improve productivity through real-time interactions from the digital world to the physical world. It is especially true with the maintenance policies, which are changing from corrective to planned ones from predictions of machine failures. We use the terms Condition-Based Maintenance (CBM) or Predictive Maintenance (PdM) in these cases; they are based on data analysis to propose a health assessment of critical components, to predict future issues. The Prognostics and Health Management (PHM) framework proposes methodologies to deal with such problems. In the recent years, many researches focused on these topics; however, few of them deal with the full scope of implementing practically this strategy in the industry, particularly in the automotive sector. Thus, this paper aims at reviewing current approaches, and presenting the strategy employed as well as the first use case investigated in the Clean Energy Systems division of Plastic Omnium.

Reconfigurable Manufacturing System is a modern manufacturing topology which has been designed at its outset according to product requirements. One main issue in the field of RMS is process planning which assigns configurations to operations, however, existing literature on process planning lacks in analyzing the quality. To overcome it, this study performs a multi-objective assessment by optimizing the total cost, the quality decay index, the diversity of operations and the reconfiguration effort. The problem is NP-hard and in order to solve it, two meta-heuristics namely, non-sorting genetic algorithm and multi-objective particle swarm optimization are administered. The results of these algorithms are compared regarding their computation time and number of Pareto solutions. Furthermore, the acquired non-dominated solutions and detailed process plans are listed according to the optimal values of objective functions. Finally, a conclusion is provided.

Reliability has always been an important factor for any manufacturing companies. An appropriate level of reliability in a manufacturing system could mean less maintenance cost, higher efficiency and steadier production state. Because each machine in a manufacturing system has its individual level of reliability, reliability on the system level would depend largely on how the machines are configured. In traditional efforts on reconfigurable manufacturing system (RMS) reliability assessments, mean time between failure (MTBF) is usually adopted as reference index of reliability. Also, in existing research efforts of applying reliability analysis in manufacturing system, reliability is merely a single and over-simplified index in the framework. But there exist various forms of reliability inside a RMS, and the complexity of this concept would keep increasing with the development of new technology in manufacturing industry. To analyze reliability in RMS in a more comprehensive way, we built conceptual maps as first step for research agenda -- from the perspective of general layer, reliability analysis and RMS.

This study considers quality aspects in the process planning of a reconfigurable manufacturing system. The goal is to analyze how the variation in quality impacts the process planning, i.e., cost-based design and modular features. Besides this, the analysis helps in identifying the number of conforming and failed products delivered by a process plan. First, a multi-objective mixed integer non-linear programming model is proposed that contains the novel objectives of cost, quality decay, and modular efforts. Secondly, the model is implemented on an industrial case study by using an exact solution approach and a novel hybrid version of two popular meta-heuristics, namely non-sorting genetic algorithm and multi-objective particle swarm optimization. The hybrid heuristic helps strengthening the application of approaches by creating a balance in searching the solution space. The performance of different approaches is assessed by using two metrics and two termination criteria. The findings will help the decision-makers in assessing how quality-related issues impact the choice of a process plan and in understanding the trade-off among cost, quality, and modularity. Finally, conclusion and future research avenues are provided.

International audience; The tolerance analysis aims to check if the tolerances specified on mechanism components allow to respect the assembly and functional requirements. Several papers illustrate that the form defects have an impact on the mechanism behavior, but most tolerance analysis techniques neglected them. To perform the tolerance analysis, three issues must be addressed: the geometrical deviations modelling, the geometrical behavior modelling and the technique of tolerance analysis. Therefore, this paper presents one contribution for each issue to perform tolerance analysis of over-constrained mechanism with form defect: • Modal representation of the form defect • Behavior modelling technique of the contact with form defect • Tolerance analysis technique with the integration of the different contact types.

Tolerance analysis is a well-accepted key element in industry for ensuring product quality as well as for reducing manufacturing costs. At the same time, and particularly in light of the recent advances in simulation technology, tolerancing decisions are also becoming increasingly important during earlier stages of design. One critical task hereby is the simulation of the “real-world” behavior of the product with minimum uncertainty, i.e. the calculation how geometrical deviations impact the mechanical behavior and/or multiple simultaneous physical phenomena in a multiphysical system. Given the short iterations in design, this usually represents a compromise between two contradictory requirements: an acceptable computation time and the accuracy of the results. The presented paper addresses this challenge by presenting a framework to assess the impact of model parameter uncertainty of the multiphysical system behavior on the accuracy of the results. The framework integrates evidence and probability theories to propagate geometrical variability and model imprecision for tolerance analysis. The information regarding geometrical variability is modelled using probability distributions; and the information regarding the model imprecision is more faithfully modelled using families of probability distributions encoded by probability-boxes (upper & lower cumulative distribution functions). Monte Carlo simulation is used for probabilistic analysis while nonlinear optimization is used for interval analysis.

The Laser Powder Bed Fusion (L-PBF) process is being adopted in different industrial fields. However, L-PBF currently lacks process reproducibility and quality. Hence, quality monitoring techniques need to be adopted in order to reduce the process variability and to ensure a high-quality process. Accordingly, this work proposes a quality monitoring approach based on machine learning which links the optical signal of a layer to the density of the final part. The approach consists of selecting relevant statistical features from optical data and validating these features by assessing their ability in predicting the different density classes of different products. Afterwards, the approach is compared to a new deep learning framework that allows predicting a part density from the corresponding raw optical signals. This comparison allows assessing the relevance of the identified statistical features. The proposed monitoring approach is applied on cubical specimens produced with different process parameters, and the results are then discussed and analyzed.

With the emergence of the Industry 4.0 and the big data era, many organizations had recourse to data-based approaches for Quality management. One of the main aims of the data-based approaches in manufacturing industries is quality classification. Classification methods provide many solutions related to quality problems such a defect detection and conformity prediction. In that context, this paper identifies a suitable technique (Support Vector Machine) for quality data classification, as it proposes an appropriate approach to optimize its performances. The proposed approach is tested on a chemical manufacturing dataset and a rolling process dataset, in order to evaluate its efficiency.

Reconfigurable Manufacturing System is a modern manufacturing topology which has been designed at its outset according to product requirements. One main issue in the field of RMS is process planning which assigns configurations to operations, however, existing literature on process planning lacks in analyzing the quality. To overcome it, this study performs a multi-objective assessment by optimizing the total cost, the quality decay index, the diversity of operations and the reconfiguration effort. The problem is NP-hard and in order to solve it, two meta-heuristics namely, non-sorting genetic algorithm and multi-objective particle swarm optimization are administered. The results of these algorithms are compared regarding their computation time and number of Pareto solutions. Furthermore, the acquired non-dominated solutions and detailed process plans are listed according to the optimal values of objective functions. Finally, a conclusion is provided.

International audience; During the design stage of product manufacturing, the designers try to specify only the necessary critical dimensions or what is called “Key Characteristics”. Knowing that dealing with Key Characteristics is time consuming and costly, it is preferable to reduce their number and exclude the non-contributing parameters. Different strategies that are based on qualitative or quantitative approaches for the identification of these dimensions are followed by the companies. The common way is to define the critical functional requirements which are expressed in terms of dimensions. When the functional requirements are set as critical, all the involved dimensions are labelled as Key Characteristics. However they do not have the same importance and need to be classified between contributing and non-contributing parameters. There is not a quantitative method that serves for the identification of Key Characteristics in the critical functional requirements. This paper suggests a numerical methodology which can be a step forward to a better ranking of the Key Characteristics. It is based on the global sensitivity analysis and more precisely on Sobol’ approach. The sensitivity of the Non Conformity Rate corresponding to the production of the product is measured with respect to the variable parameters characterizing the dimensions. The method is applied, first on a simple two-part example, then on a system having a linearised functional requirement and finally on a system with two non-linear functional requirements. The results show the main effects of the dimensions in addition to their interactions. Consequently it is possible to prioritize some and neglect the effect of the others and classify them respectively as Key Characteristics or not.

International audience; Reconfigurable manufacturing systems constitute a new manufacturing paradigm and are considered as the future of manufacturing because of their changeable and flexible nature. In a reconfigurable manufacturing environment, basic modules can be rearranged, interchanged, or modified, to adjust the production capacity according to production requirements. Reconfigurable machine tools have modular structure comprising of basic and auxiliary modules that aid in modifying the functionality of a manufacturing system. As the product’s design and its manufacturing capabilities are closely related, the manufacturing system is desired to be customizable to cater for all the design changes. Moreover, the performance of a manufacturing system lies in a set of planning and scheduling data incorporated with the machining capabilities keeping in view the market demands. This research work is based on the co-evolution of process planning and machine configurations in which optimal machine capabilities are generated through the application of multi-objective genetic algorithms. Furthermore, based on these capabilities, the system is tested for reconfiguration in case of production changeovers. Since, in a reconfigurable environment, the same machine can be used to perform different tasks depending on the required configuration, the subject research work assigns optimum number of machines by minimizing the machining capabilities to carry out different operations in order to streamline production responses. An algorithm has also been developed and verified on a part family. As a result of the proposed methodology, an optimized reconfigurable framework can be achieved to realize optimal production of a part family. Finally, the proposed methodology was applied on a case study and respective conclusions were drawn.

The aim of this paper is to provide an overview of a developed statistical tolerance analysis technique for over-constrained mechanism. Statistical tolerance analysis is a more practical and economical way of looking at tolerances and works on setting the tolerances so as to assure a desired functionality; however, most of statistical tolerance analysis techniques are dedicated to isoconstrained mechanisms and simple over-constrained mechanisms, they need an explicit assembly response function. The developed technique is based on: –the formalization of the geometrical behavior of the mechanism by an implicit assembly response function, –the formalization of the tolerancing requirement by the quantifier notion, –the coupling of some optimization techniques and reliability techniques.

During the design of manufactured products, functional requirements that need to be fulfilled are defined; they are expressed in terms of the dimensions. Functional requirements are considered as critical if they have a major impact on the performance of the system. In the current industrial practice, the dimensions involved in a critical functional requirement are identified as Key Characteristics. This study aims to rank the influence of the dimensions involved in a critical functional requirement with respect to their impacts on the non-conformity of a mechanical system. This paper presents a methodology for the identification of Key Characteristics by combining the Sobol’ global sensitivity method (based on the variance decomposition) with the Advanced Probability-based Tolerance Analysis approach (APTA). The method is applied on an electrical plug having a functional requirement with a linear formulation. The results showed a possibility to prioritize some dimensions and neglect the effect of the others.

International audience; The statistical tolerance analysis has become a key element used in the design stage to reduce the manufacturing cost, the rejection rate and to have high quality products. One of the frequently used methods is the Monte Carlo simulation, employed to compute the non-conformity rate due to its efficiency in handling the tolerance analysis of over-constrained mechanical systems. However, this simulation technique requires excessive numerical efforts. The goal of this paper is to improve this method by proposing a probabilistic model of gaps in fixed and sliding contacts and involved in the tolerance analysis of an assembly. The probabilistic model is carried out on the clearance components of the sliding and fixed contacts for their assembly feasibility considering all the imperfections on the surfaces. The kernel density estimation method is used to deal with the probabilistic model. The proposed method is applied to an over-constrained mechanical system and compared to the classical method regarding their computation time.

Tolerance analysis aims toward the verification impact of the individual tolerances on the assembly and functional requirements of a mechanism. The manufactured products have several types of contact and are inherent in imperfections, which often causes the failure of the assembly and its functioning. Tolerances are, therefore, allocated to each part of the mechanism in purpose to obtain an optimal quality of the final product. Three main issues are generally defined to realize the tolerance analysis of a mechanical assembly: the geometrical deviations modeling, the geometrical behavior modeling, and the tolerance analysis techniques. In this paper, a method is proposed to realize the tolerance analysis of an over-constrained mechanical assembly with form defects by considering the contacts nature (fixed, sliding, and floating contacts) in its geometrical behavior modeling. Different optimization methods are used to study the different contact types. The overall statistical tolerance analysis of the over-constrained mechanical assembly is carried out by determining the assembly and the functionality probabilities based on optimization techniques combined with a Monte Carlo simulation (MCS). An application to an over-constrained mechanical assembly is given at the end.

Customers' desires and requirements are increased day to day. Manufacturing companies should adapt themselves to them as soon as possible to survive in the global market. Therefore they looking for a way to decrease their costs. In addition, customers want products with the best quality. Therefore quality should be considered too. This article is tried to identify different human factors which have effects on human performance and as a result, quality of products. In the following, these factors are integrated with worker assignment in a defined assembly line. The proposed model is a multi-objective linear mathematical model that is solved with augmented ∊-constraint method and GAMS solver software. In continuous, some computational examples are presented to test this method. As results, it can be mentioned that always there is a pay-off table between costs and quality. It means that there is a conflict between them. So it's up to decision maker to choose the best solution based on situations.

To improve product quality, tolerance analysis represents nowadays a key element in industry. It aims to verify whether allocated tolerances satisfy functional and assembly requirements. Tolerance analysis approaches are generally proposed considering idealized surfaces (such as planes, cylinders, spheres, etc.) and the shape of the components is parameterized to take into account the geometric deviations. However, this approach considers only ideal substitute models, which may strongly differ from the actual geometry of the manufactured components. To enable high-precision products, there is a strong need to integrate parts form defects in the assembly modeling. Form defects are firstly generated by modal decomposition: a basis of modes of deviation is predefined and each mode is associated with a randomly generated weight to account the variability in the geometry of the components. The form defects are subsequently taken into account when handling with constraints in assembly with gaps. Assembly simulation is defined by a mathematical optimization problem, which includes the definition of the signed distance between points of the surfaces undergoing form defects and potentially in contact. The contact configuration is assessed by determining the relative positioning of parts in the assembly. An application example demonstrates the relevance of the procedure; it considers two surfaces classes: planes and cylinders. The assembly probability and functional probability are computed when mechanism is rigid and which is firstly without form defects and then with form defects. The paper closes with a comparison between the reference methods and the proposed procedure.

In tolerancing analysis area, a classical approach consists in handling sets of linear constraints. These sets of constraints characterize the boundaries of the relative displacements between two surfaces of the same workpiece or between two surfaces of two different parts, potentially in contact. The relative position between any two surfaces of a mechanism is determined by operations on these sets of constraints (Minkowski sum and intersection). A method for solving these operations is to model each set of constraints by a polytope, which by definition is a bounded intersection of many finitely closed half-spaces in some . However, the intersection of half-spaces simulating geometric constraints or contact is generally not bounded. This is due to the degree of invariance of a surface and the degree of freedom of a joint characterizing theoretically unbounded displacement. This article introduces the concept of “cap” half-spaces to delimit sets of constraints in . They are added to the operand set and in this way determining the relative position of two surfaces of a mechanical system is based solely on operations on operand polytopes generating a calculated polytope. By checking that a calculated polytope is included within a functional polytope the conformity of a mechanical system can be simulated with respect to a functional requirement. The addition of cap half-spaces to the operand sets will affect the topology of a calculated polytope. Hence it has to be possible to differentiate among all the facets of a calculated polytope between those that are generated by the cap half-spaces and the others generated by half-spaces that derive from geometric and contact constraints. This is essential in order to validate the geometric tolerances that ensure that a mechanical system is compliant in relation to a functional requirement. This article describes how to identify the facets generated by the cap half-spaces of a polytope resulting from a Minkowski sum or an intersection between two operand polytopes.

International audience; Additive manufacturing (AM) became an advanced research topic due to its ability to manufacture complex shapes. But the ability to achieve predictable and repeatable shapes is critical. Therefore, to optimize the design of an additive manufactured product, tolerancing is a key issue. This paper focuses on geometrical quality assessment of an AM product. It includes a process oriented geometrical model to predict the surface roughness and dimensional deviations, and a geometrical simulation tool to assess the impacts of these deviations on the geometrical behaviour of the joint. An application of the approach isillustrated through a case study.

International audience; Tolerance analysis aims on checking whether specified tolerances enable functional and assembly requirements. The tolerance analysis approaches discussed in literature are generally assumed without the consideration of parts’ form defects. This paper presents a new model to consider the form defects in an assembly simulation. A Metric Modal Decomposition (MMD) method is henceforth, developed to model the form defects of various parts in a mechanism. The assemblies including form defects are furtherassessed using mathematical optimization. The optimization involves two models of surfaces: real model and difference surface-base method, and introduces the concept of signed distance. The optimization algorithms are then compared in terms of time consumption and accuracy. To illustrate the methods and their respective applications, a simplified over-constrained industrial mechanism in three dimensions is also used as a case study.

International audience; In tolerancing analysis area, the most various existing approaches do not take form defects of parts into consideration. As high precisions assemblies cannot be analyzed with the assumption that form defects are negligible, the paper focuses in particular on the study of the form defects impacts on the assembly simulation and that by comparing two optimization algorithms (iHLRF and Quapro). The study is limited firstly to the cylinders. For the optimization, two main types of surfaces modelling are considered: difference surface-based method and real model. The compared models allow assessing the non-interferences between cylinders with form defects, potentially in contact. This is in the main issue to validate a tolerance analysis approach.

International audience; In many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for quality assessment (QA) in manufacturing industries. In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set.Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

Geometric tolerancing analysis consists of simulating the behavior of a mechanical system according to geometric defects in the constituent parts. The aim is to verify system compliance in terms of the functional requirements for its expected operation. When carrying out the simulation the geometric specifications of the constituent parts and specifications of parts potentially in contact must be taken into account. One approach using polytopes consists of characterizing the specifications of the parts, the specifications of the contacts and the functional requirements of the mechanical system using sets of geometric constraints. This article describes modeling different sets of constraints manipulated by polytopes. We introduce the operations that are applied (Minkowski sum and intersection) to determine the relative position of any two surfaces of a mechanical system. Finally, tolerancing analysis of a simple mechanical system is described.

International audience; In tolerance analysis, it is necessary to check that the cumulative defect limits specified for the component parts of a product are compliant with the functional requirements expected of the product. Defect limits can be modelled by tolerance zones constructed by offsets on nominal models of parts. Cumulative defect limits can be modelled using a calculated polytope, the result of a set of intersections and Minkowski sums of polytopes. A functional requirement can be qualified by a functional polytope, in other words a target polytope. It is then necessary to verify whether the calculated polytope is included in the functional polytope. The purpose of this article is to determine the Minkowski sum of 3-dimension polytopes and apply this effectively in order to optimise the filling of the functional polytope. This method can be used to determine from which vertices of the operands the vertices of the Minkowski sum derive. It is also possible to determine to which facets of the operands each facet of the Minkowski sum is oriented. First, the main properties of the duality of normal cones and primal cones associated with the vertices of a polytope are described. Next, the properties of normal fans are applied to define the vertices and facets of the Minkowski sum of two polytopes. An algorithm is proposed which generalises the method. Lastly, there is a discussion of the features of this algorithm, developed using the OpenCascade environment.