Metamodelling of Manufacturing Processes and Automation Workflows towards Designing and Operating Digital Twins

Abstract

The automation of workflows for the optimization of manufacturing processes through digital twins seems to be achievable nowadays. The enabling technologies of Industry 4.0 have matured, while the plethora of available sensors and data processing methods can be used to address functionalities related to manufacturing processes, such as process monitoring and control, quality assessment and process modelling. However, technologies succeeding Computer-Integrated Manufacturing and several promising techniques, such as metamodelling languages, have not been exploited enough. To this end, a framework is presented, utilizing an automation workflow knowledge database, a classification of technologies and a metamodelling language. This approach will be highly useful for creating digital twins for both the design and operation of manufacturing processes, while keeping humans in the loop. Two process control paradigms are used to illustrate the applicability of such an approach, under the framework of certifiable human-in-the-loop process optimization.

A Comprehensive Analysis of Sensitivity in Simulation Models for Enhanced System Understanding and Optimisation

“…We generate outputs to which we apply regression analysis. The issue of metamodels has been dealt with, for example, in works [32][33][34].…”

Malzeme Mühendisliğinde Gelişmiş Malzeme Davranışı Tahmini ve Üretim Süreci Optimizasyonu için Dijital İkizler ve Uygulamaları

“…Figure 1 represents the total number of published articles in these databases regarding these periods. Reference Aerospace [10,11] Marine [13] Automotive and Autonomous Vehicular Systems [12] Healthcare [15,16,17] Agriculture [18] Electronics [14] Manufacturing Processes [19,20,21,22,23] Predictive Maintenance [24] The advent of Industry 4.0 and the digital revolution have opened up new avenues for technological advancements in various fields. One such innovation is the concept of digital twins, which holds great potential to redefine the landscape of materials engineering.…”

“…This research aims to investigate the capabilities of digital twins in predicting material behavior and optimizing manufacturing processes, ultimately contributing to the evolution of advanced materials manufacturing. Materials engineering plays a pivotal role in various industries, ranging from aerospace [10,11], automotive and autonomous vehicular systems [12], marine [13], and from electronics [14] to healthcare [15,16,17] and agriculture [18], also can be implemented to many manufacturing processes such as cutting [19,20], milling [21] and other processes [22,23] and predictive maintenance systems [24] as depicted in Table 1. The demand for advanced materials with superior performance has led to the exploration of novel techniques to enhance material behavior and optimize manufacturing processes.…”

“…They've concluded that the reliability of digital twins relies on its adaptability to different manufacturing technologies. Similarly, [20] established a metamodeling framework that utilizes an automation workflow knowledge database for classification. In [20], the authors emphasize the significance of using a classification system for technologies and a unified metamodelling framework as powerful tools for designing and operating manufacturing processes.…”

“…Similarly, [20] established a metamodeling framework that utilizes an automation workflow knowledge database for classification. In [20], the authors emphasize the significance of using a classification system for technologies and a unified metamodelling framework as powerful tools for designing and operating manufacturing processes. The framework is particularly adept at systems integration, technology capabilities documentation, and workflow formation.…”