AI-based reconfigurable inspection system (RIS): comprehensive model and implementation in industry

Due to global competition and continuously changing customer demand, manufacturers nowadays face frequent and unpredictable market shifts. Introducing reconfigurability into contemporary manufacturing systems can enhance cost-effective and rapid responsiveness to these variations. A Reconfigurable Manufacturing System (RMS) can provide a tailored production process in response to changes in operating procedures or machine statuses. Just like any other manufacturing system, RMS requires effective and timely diagnosis as well as prognosis to function smoothly. A Reconfigurable Inspection System (RIS) is designed within an RMS for data-oriented detection of product quality with a minimum number of inspection units. Existing studies about reconfiguration, however, focus on production while disregarding inspection. Artificial Intelligence (AI) has the potential to significantly assist manufacturers over the next decade due to their heavy dependency on data. AI applications such as Machine learning (ML) and Deep Learning (DL) can aid in addressing issues such as tracking manufacturing failures back to specific phases in the manufacturing process by learning relevant data patterns. Thus, this paper aims to provide an overview of the current literature on RMS as well as ML/DL technologies that can be integrated into RIS to enhance performance. Subsequently, a comprehensive model of an AI-based RIS is proposed based on the experimental results derived from existing publications, and the retrofitting procedure of a case study is presented. However, the proposed model and the retrofitting procedure are not validated by experimental results or physical implementation.