Edge-Cloud Collaboration Architecture for AI Transformation of SME Manufacturing Enterprises

Abstract

A new edge-cloud collaboration architecture for small and medium sized enterprises (SME) manufacturers is introduced in this work. Lager manufacturers with sufficient resources already invested heavily in smart manufacturing system. There are rapidly emerging needs to help small and medium sized enterprises manufacturers with limited resources to implement smart and highly adaptable manufacturing systems to compete and sustain in global economy. We present an illustrative case study of how to implement and manage AI projects in practice for SME manufacturers. We demonstrated how our proposed architecture can help accelerate one of the United Nations Sustainable Development Goals, i.e. Goal 9: Industry, Innovation and Infrastructure, by exhibiting the practicality and scalability of our proposed solution. In particular, we elaborate on the key manufacturing issues concerning company-wide resource distribution, problem solving and decision making. It will be demonstrated that more advanced AI systems such as deep learning and deep reinforcement learning emerge naturally with one's quality management system which already in place and come with a well-defined semantics of their process functions in the context of collaborative edge-cloud architecture. Furthermore, equipment used in the smart factory includes manufacturing equipment, functional testing equipment and defect detection equipment. In this work, we will present the management and implementation of on-device AI defect detection and classification to show the feasibility and effectiveness of the edge-cloud collaboration architecture approach.