Intelligent process planning for smart manufacturing systems: a state-of-the-art review

Abstract

Process planning is a decision-making procedure to determine the required manufacturing processes and machine tools, and the sequence of the activities that the processes should follow, to produce a designed part or a product component. The quality of the process plan has a major impact on the efficiency and productivity of the whole production process in addition to the quality of the final product. Computer-aided process planning (CAPP) systems have been constantly developing and attracted considerable research efforts during the last four decades. Several approaches such as search logic structure, variant or case-based, generative approach, hybrid, and knowledge-based or expert systems have been employed to develop the CAPP systems with the aim of reducing and finally removing the role of experienced process planners in providing a reliable and optimized process plan to achieve the automated and intelligent CAPP system. However, despite these huge efforts, the process planning task is not completely automated yet and still depends on human experiences and knowledge. Artificial Intelligence, Machine Learning, and Data Analytics seem to be promising tools to achieve the total independence of CAPP systems on the experience of the process planners in the era of Industry 4.0 and smart manufacturing systems. The ideal intelligent CAPP systems will be able to collect the experience and knowledge of the technology experts in addition to being adaptive and self-learning according to the machining process real-time data and work history. In this article, a literature review is carried out with the aim of gaining a comprehensive insight into the state-of-the-art intelligent process planning approaches. Several articles and books based on the types and the applications of the systems are reviewed with a focus on the publishing date within the last decade. The advantages and disadvantages of different approaches are presented and finally, the future research and investigation pathways are demonstrated.