Abstract
Nowadays, the new era of industry 4.0 is forcing manufacturers to develop models and methods for managing the geometric variation of a final product in complex manufacturing environments, such as multistage manufacturing systems. The stream of variation model has been successfully applied to manage product geometric variation in these systems, but there is a lack of research studying its application together with the material and order flow in the system. In this work, which is focused on the production quality paradigm in a model-based system engineering context, a digital prototype is proposed to integrate productivity and part quality based on the stream of variation analysis in multistage assembly systems. The prototype was modelled and simulated with OpenModelica tool exploiting the Modelica language capabilities for multidomain simulations and its synergy with SysML. A case study is presented to validate the potential applicability of the approach. The proposed model and the results show a promising potential for future developments aligned with the production quality paradigm.
Highlights
The industries, as they advance in the postulates advocated in different research and development roadmaps, e.g., industry 4.0, are transforming their factories and their management and control strategies following some of the emerging manufacturing paradigms such as smart manufacturing or production quality
We focused our work on a type of assembly system (AS) that has many of the properties that should be present in advanced manufacturing systems
The results notably improved thethe quality of results obtained obtainedshow showthat thatthe thecontrol controlstrategy strategyproposed proposedhas has notably improved quality the parts produced, observing a generalized decrease in the maximum deviations detected
Summary
The industries, as they advance in the postulates advocated in different research and development roadmaps, e.g., industry 4.0, are transforming their factories and their management and control strategies following some of the emerging manufacturing paradigms such as smart manufacturing or production quality.On the one hand, production quality was formulated as a paradigm that combines quality, production logistics, and maintenance methods and tools to maintain the throughput and the service level of conforming parts under control and to improve them over time, with minimal waste of resources and materials [1]. The industries, as they advance in the postulates advocated in different research and development roadmaps, e.g., industry 4.0, are transforming their factories and their management and control strategies following some of the emerging manufacturing paradigms such as smart manufacturing or production quality. One of the main issues in production quality is the variation propagation modelling for quality control, process monitoring, and root cause identification for multistage manufacturing systems (MMS). These manufacturing systems are highly complex, and the development of an effective and cost-efficient quality control strategy that intermeshes and links closed-loop quality control systems at various levels of the company is crucial [1]. A clear example can be found in [2], where different control strategies are proposed using newly available sensor data from shopfloors to prevent the generation and propagation of defects throughout the process.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
