An Artificial Intelligence-based Integrated Framework for Lean and Smart Manufacturing: A Case Study of the Rail Industry

This study deals with the development of an Artificial Intelligence-based integrated digital technology framework for lean and smart manufacturing implementation. Some technologies and techniques driving Industry 4.0 that are considered in this study include Additive Manufacturing (AM), Automatic Train Control (ATC), smart sensors, Internet of Things (IoT), big data, cloud computing, Total Productive Maintenance, Predictive Maintenance (PM), and Artificial Intelligence (AI). Using the rail industry as a case study, an overview of the enabling technologies was presented to acquaint the manufacturing industries with the potentials and applications of these technologies to manufacturing. The study also develops a conceptual framework which incorporates the main driving technologies of lean and smart manufacturing for adoption by the rail industry. The validation of the proposed integrated framework involves the use of AI technology for total productive and predictive maintenance using the rail industry as a case study was conducted in the MATLAB 2020 b environment. The outcome of the demonstration shows the Remaining Useful Life (RUL) of a bearing component of a railcar can be predicted using AI. The predicted RUL of the wheel bearing indicates 500 hours over the next 40 days before failure occurs. Thus, this study adds to the understanding of AI in the lean and smart manufacturing domain. The implementation of the integrated framework can promote efficiency and effectively service delivery in industries that employs the concept of lean and smart manufacturing.