scholarly journals A multi-objective approach for resilience-based system design optimisation of complex manufacturing systems

Procedia CIRP ◽  
2021 ◽  
Vol 100 ◽  
pp. 536-541
Author(s):  
Christina Latsou ◽  
John Ahmet Erkoyuncu ◽  
Maryam Farsi
Keyword(s):  
System Design ◽  
Manufacturing Systems ◽  
Design Optimisation ◽  
Multi Objective ◽  
Complex Manufacturing Systems

scholarly journals Sustainable Reconfigurable Manufacturing System Design Using Adapted Multi-Objective Evolutionary Based Approaches

2021 ◽  
Author(s):  
Imen Khettabi ◽  
Lyes Benyoucef ◽  
Mohamed Amine Boutiche
Keyword(s):  
Genetic Algorithms ◽  
System Design ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Total Production ◽  
Reconfigurable Manufacturing ◽  
Reconfigurable Manufacturing System ◽  
Nsga Ii ◽  
Multi Objective ◽  
Manufacturing System Design

Abstract Nowadays, manufacturing systems should be cost-effective and environmentally harmless to cope with various challenges in today's competitive markets. In this paper, we aim to solve an environmental oriented multi-objective reconfigurable manufacturing system design (ie., sustainable reconfigurable machines and tools selection) in the case of a single unit process plan generation. A non-linear multi-objective integer program (NL-MOIP) is presented first, where four objectives are minimised respectively, the total production cost, the total production time, the amount of the greenhouse gases emitted by machines and the hazardous liquid wastes. Second, to solve the problem, we propose four adapted versions of evolutionary approaches, namely two versions of the well known non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), weighted genetic algorithms (WGA) and random weighted genetic algorithms (RWGA). To illustrate the efficiency of the four approaches, several instances of the problem are experimented and the obtained results are analysed using three metrics respectively hypervolume, spacing metric and cardinality of the mixed Pareto fronts. Moreover, the influences of the probabilities of genetic operators on the convergence of the adapted NSGA-III are analysed and TOPSIS method is used to help the decision maker ranking and selecting the best process plans.

Reale Daten für Simulationen im digitalen Zwilling*/Real data for simulation in the digital twin -Study on how to record Profinet data and reproduce them in complex simulation environments

2019 ◽  
Vol 109 (09) ◽  
pp. 662-666
Author(s):  
M. Chemnitz ◽  
O. Heimann ◽  
A. Vick
Keyword(s):  
Twin Study ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Real Data ◽  
Control Data ◽  
Cause Analysis ◽  
Digital Twin ◽  
Root Cause ◽  
Complex Simulation ◽  
Complex Manufacturing Systems

Die hohen Anforderungen an moderne Fertigungssysteme erfordern leistungsfähige Engineering-Lösungen. Wie man die Identifikation von Fehlerursachen in komplexen Anlagen erleichtert, wurde in einer Machbarkeitsstudie des Fraunhofer IPK im Auftrag von Siemens DI FA untersucht. In der vorgestellten Lösung werden die Daten der Anlage auf Feldbusebene erfasst und in den digitalen Zwilling eingespeist. So kann das Verhalten der Komponenten taktgenau nachvollzogen werden. Dies elaubt einen tiefen Einblick in das System und unterstützt so bei der Fehlerbehebung.   Powerful engineering tools are required to keep modern production systems manageable. Siemens DI FA and the Fraunhofer IPK present a novel tool for root cause analysis within complex manufacturing systems. The solution combines a CAx plant model with control data recorded from the field bus. This creates a comprehensive digital twin, allowing to analyse past machine behavior with bus clock resolution.

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