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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 μm, 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 tolerance assignment while taking gear functionality and production cost into account. [ABSTRACT FROM AUTHOR]

The reconfigurable manufacturing system is an advanced field of research that has surpassed the efficiency of other manufacturing systems due to its high throughput, cost-effectiveness, and ability to accommodate product variety. An important problem addressed in the field of reconfigurable manufacturing systems is process planning which assigns configurations to different operations. Process planning has been the focus of research for almost two decades; however, a comprehensive review is lacking to highlight the possible streams of future contributions to this field of research. To this end, this study presents a systematic review of the process planning in a reconfigurable manufacturing system with a focus on optimization efforts. This review is organized in two interconnected phases, i.e., a theoretical phase and an implementation phase. The theoretical phase reviews the concerned literature regarding the levels of analysis, reconfigurable manufacturing system (RMS) characteristics, different research themes, and change agents. On the other hand, the implementation phase reviews the literature regarding the use of different objective functions, constraints, solution approaches, nature of problem/solution, and the use of industrial applications. Several future research streams are provided which can guide, in a broader sense, the advancement of process planning literature. As a demonstration, quality, supply chain, and human/operator issues are highlighted to advance the scope of applicability of the concerned literature. A thorough analysis of the operator aspects in scheduling literature is presented which can benefit practitioners working in a changeable/reconfigurable manufacturing environment. Moreover, practical process planning approaches are offered to analyze the cost, time, and quality aspects. Finally, the conclusion of the study is provided. [ABSTRACT FROM AUTHOR]

In the context of manufacturing, support vector machines (SVM) are commonly used to predict quality, i.e., predict the characteristics of a product according to the manufacturing parameters. The prediction accuracy of a SVM model is affected by a number of factors: training set size, data set quality, etc. Manufacturing datasets are usually prone to measurement uncertainties. Such uncertainties affect the observed values of the manufacturing parameters, thereby affecting the predictive performance of the SVM. To address this issue, several works in the literature have been proposed to improve the robustness of SVM to measurement uncertainties. These works, however, do not evaluate the contribution of the uncertainties of each parameter to the overall impact. For this reason, this paper focuses on quantifying the impact of the uncertainties of each parameter on the accuracy of the SVM prediction. Three approaches are proposed to do so. The first two approaches are based on Monte-Carlo simulation and allow providing quantitative measures that represent the impact of the uncertainties of each manufacturing parameter on the accuracy of the SVM. On the other hand, the third approach relies on simple statistical tools in order to estimate the impact of the uncertainties of each parameter. The proposed approaches would eventually make it possible to identify the uncertainties of the parameters that mostly affect the SVM. Such parameters are referred to as key measurement uncertainties. Identifying the key measurement uncertainties would provide a better understanding of how the SVM is affected by uncertainties, as it would provide a strong basis for improving the robustness of SVM in future works. The proposed approaches are applied to four datasets, and their performances are discussed and compared. [ABSTRACT FROM AUTHOR]

The adoption of laser powder bed fusion (L-PBF) for metals by the industry has been limited despite the significant progress made in the development of the process chain. One of the key obstacles is the inconsistency of the parts obtained from L-PBF. Due to its complexity, there are many potential fluctuations that can occur within the process chain which can lead to quality inconsistency in L-PBF parts. Machine learning (ML) has the possibility to overcome this obstacle by utilising datasets obtained at various stages of the L-PBF process chain. In this perspective article, the integration of ML into the different stages of L-PBF process chain, which potentially lead to better quality control, is explored. Prior to L-PBF, ML can be used for part designs and file preparation. Then, ML algorithms can be applied in the process parameter optimisation and in situ monitoring. Finally, ML can also be integrated into the post-processing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]