A Framework for Integration of Resource Allocation and Reworking Concept into Design Optimisation Problem

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

An integrated resource allocation and tolerance allocation optimization: A statistical-based dimensional tolerancing

Today’s industrial world is facing rising demand for highly reliable and safe products. Complex industries, such as automobiles, medical, and aircraft, require a well-designed engineering plan which has a comprehensive understanding of the various certainties and uncertainties that occur in reality. Consequently, the need for reliable and precise parts has impacted the tolerancing activity. Key functions of complex products can often be realized by high precision part use. Thus, producers are confronted with high-quality requirements, cost pressure, and a rising number of demands. The introduction of new technologies and the need to meet the requirements have broadened the scope of tolerancing. In this paper, a statistical tolerance allocation model is developed to study the economic impact of allocated tolerances on an assembled product. The problem is aimed at optimizing the allocated tolerances to each part of the product while minimizing manufacturing costs. A modular cost model is proposed to determine the manufacturing costs related to each activity and part. The manufacturing costs include processing cost, inspection cost, scrap cost, assembly cost, and warranty cost. Furthermore, a genetic algorithm is adapted to study the applicability of the model developed on an exemplary assembled product

Diagnosis on Energy and Sustainability of Reconfigurable Manufacturing System (RMS) Design: A Bi-level Decomposition Approach

Sustainability and energy consumption awareness led industrial sector to reduce energy consumption. This reduction is regarded as a solution to reduce greenhouse gas emissions. Moreover, international regulations about maintenance activities involve hazardous energy-any electrical, mechanical, nuclear or other energies that can harm personnel- as a rising threat. Thus, energy audits and diagnosis of existing manufacturing systems are crucial to achieve energy efficiency. Future manufacturing paradigms as reconfigurable manufacturing system (RMS) have shown high responsiveness to cope with new challenges such as sustainability. This paper proposes a sustainable RMS design through process plan generation. The approach is developed to generate a process plan while diagnosing energy flow and assigning preventive maintenance activities related to reliability reduction in system components. More specifically, a mixed-integer non-linear program is proposed, then solved using a bi-level decomposition approach. The lower-level considers process plan generation following parts requirements and guided by energy loss as an objective. Afterwards, the upper-level diagnoses the reliability of the lower-level selected machines and tools. Moreover, it checks if preventive maintenance is required due to the level of hazardous energy and maintenance plan. The approach applicability is validated through an illustrative example

A Framework for Integration of Resource Allocation and Reworking Concept into Design Optimisation Problem

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

Modular cost model for Tolerance allocation, Process selection and Inspection planning

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

Méta-modèle et optimisation pour l’allocation dynamique des tolérances et des stratégies d'assemblage

Cette thèse se concentre sur le développement fonctionnel de systèmes assemblés complexes, tels que les micro-engrenages, qui reposent sur des composants de haute précision. La performance de ces systèmes est déterminée par divers facteurs, notamment la conception, la fabrication, l'assemblage et les conditions internes et externes. Pour faire face à la complexité de ces assemblages et au défi que représente l'analyse de leur comportement fonctionnel, des modèles de substitution sont proposés pour estimer les effets de la tolérance et leur impact sur la fonctionnalité. L'utilisation de techniques d'apprentissage automatique, en particulier l'IA, est explorée comme une approche efficace pour prédire la précision de l'assemblage et améliorer la rentabilité et le gain de temps. En plus de proposer des modèles de substitution, cette thèse présente des stratégies de production, telles que l'allocation des ressources, le retraitement et les stratégies d'assemblage, afin d'améliorer la qualité de l'assemblage et de réduire les coûts de production. Un nouveau cadre de modèle coût-activité est également introduit pour identifier les spécifications à coût élevé et à faible tolérance dans le processus de production et permettre aux fabricants d'optimiser l'équilibre entre le coût et la tolérance tout en maintenant la qualité. La structure modulaire du modèle offre une approche organisée et systématique de l'analyse et de l'optimisation du processus de fabrication.This thesis focuses on the functional behavior of intricate assembled systems, such as micro gears, which rely on high-precision components. The performance of these systems is determined by various factors, including design, manufacturing, assembly, and internal and external conditions. To address the complexity of these assemblies and the challenge of analyzing their functional behavior, surrogate models are proposed to estimate the effects of tolerance and their impact on functionality. The use of machine learning techniques, particularly AI, is explored as an efficient approach to predict assembly precision and improve cost-effectiveness and time-saving. In addition to proposing surrogate models, this thesis presents production strategies, such as resource allocation, reworking, and assembly strategies, to enhance assembly quality and reduce production cost. A novel cost-activity model framework is also introduced to identify high-cost and low-tolerance specifications in the production process and enable manufacturers to optimize the balance between cost and tolerance while maintaining quality. The modular structure of the model provides an organized and systematic approach to analyzing and optimizing the manufacturing process

Méta-modèle et optimisation pour l’allocation dynamique des tolérances et des stratégies d'assemblage

This thesis focuses on the functional behavior of intricate assembled systems, such as micro gears, which rely on high-precision components. The performance of these systems is determined by various factors, including design, manufacturing, assembly, and internal and external conditions. To address the complexity of these assemblies and the challenge of analyzing their functional behavior, surrogate models are proposed to estimate the effects of tolerance and their impact on functionality. The use of machine learning techniques, particularly AI, is explored as an efficient approach to predict assembly precision and improve cost-effectiveness and time-saving. In addition to proposing surrogate models, this thesis presents production strategies, such as resource allocation, reworking, and assembly strategies, to enhance assembly quality and reduce production cost. A novel cost-activity model framework is also introduced to identify high-cost and low-tolerance specifications in the production process and enable manufacturers to optimize the balance between cost and tolerance while maintaining quality. The modular structure of the model provides an organized and systematic approach to analyzing and optimizing the manufacturing process.Cette thèse se concentre sur le développement fonctionnel de systèmes assemblés complexes, tels que les micro-engrenages, qui reposent sur des composants de haute précision. La performance de ces systèmes est déterminée par divers facteurs, notamment la conception, la fabrication, l'assemblage et les conditions internes et externes. Pour faire face à la complexité de ces assemblages et au défi que représente l'analyse de leur comportement fonctionnel, des modèles de substitution sont proposés pour estimer les effets de la tolérance et leur impact sur la fonctionnalité. L'utilisation de techniques d'apprentissage automatique, en particulier l'IA, est explorée comme une approche efficace pour prédire la précision de l'assemblage et améliorer la rentabilité et le gain de temps. En plus de proposer des modèles de substitution, cette thèse présente des stratégies de production, telles que l'allocation des ressources, le retraitement et les stratégies d'assemblage, afin d'améliorer la qualité de l'assemblage et de réduire les coûts de production. Un nouveau cadre de modèle coût-activité est également introduit pour identifier les spécifications à coût élevé et à faible tolérance dans le processus de production et permettre aux fabricants d'optimiser l'équilibre entre le coût et la tolérance tout en maintenant la qualité. La structure modulaire du modèle offre une approche organisée et systématique de l'analyse et de l'optimisation du processus de fabrication

Méta-modèle et optimisation pour l’allocation dynamique des tolérances et des stratégies d'assemblage

This thesis focuses on the functional behavior of intricate assembled systems, such as micro gears, which rely on high-precision components. The performance of these systems is determined by various factors, including design, manufacturing, assembly, and internal and external conditions. To address the complexity of these assemblies and the challenge of analyzing their functional behavior, surrogate models are proposed to estimate the effects of tolerance and their impact on functionality. The use of machine learning techniques, particularly AI, is explored as an efficient approach to predict assembly precision and improve cost-effectiveness and time-saving. In addition to proposing surrogate models, this thesis presents production strategies, such as resource allocation, reworking, and assembly strategies, to enhance assembly quality and reduce production cost. A novel cost-activity model framework is also introduced to identify high-cost and low-tolerance specifications in the production process and enable manufacturers to optimize the balance between cost and tolerance while maintaining quality. The modular structure of the model provides an organized and systematic approach to analyzing and optimizing the manufacturing process.Cette thèse se concentre sur le développement fonctionnel de systèmes assemblés complexes, tels que les micro-engrenages, qui reposent sur des composants de haute précision. La performance de ces systèmes est déterminée par divers facteurs, notamment la conception, la fabrication, l'assemblage et les conditions internes et externes. Pour faire face à la complexité de ces assemblages et au défi que représente l'analyse de leur comportement fonctionnel, des modèles de substitution sont proposés pour estimer les effets de la tolérance et leur impact sur la fonctionnalité. L'utilisation de techniques d'apprentissage automatique, en particulier l'IA, est explorée comme une approche efficace pour prédire la précision de l'assemblage et améliorer la rentabilité et le gain de temps. En plus de proposer des modèles de substitution, cette thèse présente des stratégies de production, telles que l'allocation des ressources, le retraitement et les stratégies d'assemblage, afin d'améliorer la qualité de l'assemblage et de réduire les coûts de production. Un nouveau cadre de modèle coût-activité est également introduit pour identifier les spécifications à coût élevé et à faible tolérance dans le processus de production et permettre aux fabricants d'optimiser l'équilibre entre le coût et la tolérance tout en maintenant la qualité. La structure modulaire du modèle offre une approche organisée et systématique de l'analyse et de l'optimisation du processus de fabrication

A Sustainable Reconfigurable Manufacturing System Designing With Focus On Environmental Hazardous Wastes

International audienceDue to awakening environmental awareness and corresponding tightening of environmental protocols in the industrialized world, new production challenges arise. These challenges are to meet the continuously growing worldwide demand for capital and consumer goods while considering the associated economic, environmental, and social aspects. The next generation manufacturing systems must adjust themselves rapidly and cost-effectively. The goal is to respond to changing market needs while minimizing adverse effects on the environment. Reconfigurable Manufacturing Systems (RMSs) - due to its flexibility and characteristics - can increase the system sustainability and responsiveness to satisfy the market needs. In this paper, we propose an environmental oriented multi-objective problem for a sustainable reconfigurable manufacturing system. As design objectives, we consider the total production time, the total production cost and the amount of environmental hazardous wastes. The environmental hazardous wastes considers both liquid hazardous waste and greenhouse gas emissions (GHG). Weighted goal programming is used to tackle this multi-objective problem. The applicability of our approach is illustrated through a numerical example

Towards a Sustainable Reconfigurable Manufacturing System (SRMS): Multi-Objective Based Approaches for Process Plan Generation Problem

International audienc