scholarly journals Flexibility Evaluation Method of Production Systems Corresponding to Quantitative Fluctuation(Manufacturing Systems Division Conference 2010)

2010 ◽  
Vol 76 (772) ◽  
pp. 3190-3197
Author(s):  
Shigeru HARASHIMA ◽  
Katsuhisa OHNO
Keyword(s):  
Manufacturing Systems ◽  
Evaluation Method ◽  
Production Systems ◽  
Special Issue ◽  
Method Of Production

scholarly journals Algorithm to Use Some Specific Lean Manufacturing Methods: Application in an Industrial Production Process

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 641
Author(s):  
Ana Cornelia Gavriluţă ◽  
Eduard Laurenţiu Niţu ◽  
Constantin Alin Gavriluţă
Keyword(s):  
Production System ◽  
Lean Manufacturing ◽  
Manufacturing Systems ◽  
Evaluation Method ◽  
Production Systems ◽  
Work Processes ◽  
Continuous Increase ◽  
Production Area ◽  
Manufacturing Methods ◽  
Performance Evaluation Method

Lean Manufacturing includes an ensemble of methods to analyze and continuously improve the functioning of manufacturing systems. The research presented in the literature highlights the fact that these methods are, on their own, in a process of continuous improvement as tools, being used in different ways, for different production systems. The paper presents an algorithm that facilitates the choice of the performance evaluation method, and the choice of the method of improvement that needs to be implemented for an efficient analysis and for a continuous increase of the manufacturing system performance. In addition to these, for the JobObservation and 5S methods, chartflows are proposed and specific tools are developed (questionnaires, forms etc.) that are meant to facilitate the implementation and to focus (guide) the user in the direction of improvement for the analyzed process. The algorithm, techniques, and tools developed in this research were used in a case study that took place in a production system “plastic injection”. Thus, a series of important improvements were made in the functioning of the production system, consisting of the reduction of production area, decrease of cycle time, decrease of the number of operators, stabilization, standardization, and securing of the work processes. All this has led to the improvement of several key performance indicators (KPIs) of the production system. The analysis of the investment in the reorganization of the production system in relation to the obtained gains shows a payback of approximately 1 month, proving the efficiency of use in such a form of the Lean Manufacturing methods.

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

scholarly journals Special Issue on Smart Sustainable Manufacturing Systems

2019 ◽  
Vol 9 (11) ◽  
pp. 2264
Author(s):  
Gökan May ◽  
Dimitris Kiritsis
Keyword(s):  
Manufacturing Systems ◽  
Sustainable Manufacturing ◽  
Digital Technologies ◽  
Special Issue ◽  
Challenges And Opportunities

With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities [...]

scholarly journals Robust scheduling and dispatching rules for high-mix collaborative manufacturing systems

2021 ◽  
Author(s):  
Andrea Maria Zanchettin
Keyword(s):  
Manufacturing Systems ◽  
Production Systems ◽  
Adaptive Scheduling ◽  
Dispatching Rules ◽  
Numerical Verification ◽  
Robust Scheduling ◽  
Collaborative Manufacturing ◽  
Processing Times ◽  
Increasing Demand ◽  
Variable Production

AbstractMotivated by the increasing demand of mass customisation in production systems, this paper proposes a robust and adaptive scheduling and dispatching method for high-mix human-robot collaborative manufacturing facilities. Scheduling and dispatching rules are derived to optimally track the desired production within the mix, while handling uncertainty in job processing times. The sequencing policy is dynamically adjusted by online forecasting the throughput of the facility as a function of the scheduling and dispatching rules. Numerical verification experiments confirm the possibility to accurately track highly variable production requests, despite the uncertainty of the system.

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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