Advanced manufacturing technology and next-generation artificial intelligence (AI) are deeply integrated into next-generation intelligent manufacturing (NGIM). Intelligent manufacturing systems are complex systems that are composed of humans, cyber systems, and physical systems and aim to achieve specific manufacturing goals at an optimized level. The next-generation intelligent smart factory system that is proposed in this paper could improve product quality and realize flexible, efficient, and sustainable product manufacturing by comprehensively improving production and management innovation via its digital network and intelligent methods that reflect the characteristics of its printed circuit board (PCB) manufacturing design and on-site implementation.
Therefore, the system meets all the proposed objectives. The results show that all test cases passed. A total of 28 test cases were performed to cover all its functionalities. The development of this system was based on the design of an architecture that combines the KDT methodology with a DSL to manage and configure the tests to integrate the self-diagnostic test system into a CPS. This article arises from the need to find a solution to solve this problem, which was to develop a self-diagnostic system that will ensure the reliability and integrity of the cyber-physical system, passing a detailed state of the art. During this process, the radios are submitted to several tests, raising the problem of how the machine detects errors in several radios consecutively, making it impossible to know if the device has a problem since it has no module to see if it works correctly. CONTROLAR provides Bosch with an intelligent functional testing machine used to test the correct functioning of the car radios produced.
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