Development of a smart machining system using self-optimizing control

Abstract

In recent years, due to intense competition through globalization, most manufacturing companies have focused on increasing the added value and reducing the production costs of their products. Inevitably, this has led to the use of advanced technology to carry out manufacturing in an effective and efficient way. Monitoring and control of machining processes are becoming increasingly important for maintaining consistent quality in machined parts. The quality of product can be affected by disturbances during machining process. The paper presents a self-optimizing control system (SOCS) for smart machining that applies information science to enable next-generation quality control, in which the need for expensive post-process inspection is eliminated. In the smart machining system with SOCS, each machine is an autonomous entity. The machining system reacts to disturbances autonomously based on the reaction of each autonomous entity or the cooperation among them. In order to develop the SOCS, the disturbances that happened in the machining shop for manufacturing the clutch housing products were analyzed to classify them and to find out the corresponding management methods such as non-negotiation, negotiation, and rescheduling. To prove that the proposed SOCS is self-monitoring, self-adjusting as well as cooperation, a machining process related to tool conditions was considered in this paper. If the disturbance belongs to the non-negotiation type, for example the tool wear, the machine with SOCS adjusts the cutting parameters in consideration of the amount of tool wear to keep the quality of the machined part. In case the disturbance belongs to the negotiation type such as the tool wear exceeding the allowed limit or tool broken, ant colony inspired cooperation among machines is implemented to find out the most appropriate machine for carrying out the machining operation. The best solution is chosen based on the evaluation of pheromone values of the alternative machines in case many machines satisfy the requirements. The work of the machine in which the disturbance happens is performed at another machine in order to keep the machining system running. The experimental results prove that the mechanism of the proposed SOCS enables the system to adapt to the disturbances successfully.