Tool maintenance optimization for multi-station machining systems with economic consideration of quality loss and obsolescence

  • Authors:

  • Sun Ji-wen;Xi Li-feng;Du Shi-chang;Pan Er-shun

  • Affiliations:

  • School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China;School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China;School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China;School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China

  • Venue:
  • Robotics and Computer-Integrated Manufacturing
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Tools used in a machining process are vulnerable to frequent wear-outs and failures during their useful life. Maintenance is thus considered essential under such conditions. Additionally, it is widely recognized that the maintenance of manufacturing equipments and the quality of manufactured product are highly interrelated. However, few detailed study has been found in the literature dealing with the effects of maintenance policies on the operational performance of such a system, especially the long-term average cost. The need for a method to determine the optimal tool maintenance policy has become increasingly important. Since the multiple tools in a multi-station machining system generally have significant interactive impacts on the product quality loss, the optimal multi-component maintenance models for several policies are investigated to address the interdependence among these tools. Three distinctive multi-component maintenance policies, i.e., age replacement, block replacement, and block replacement with minimal repair, are identified and analyzed. The proposed approach focuses on these maintenance policies with consideration of both component catastrophic failures, and the interdependence of component degradations on the product quality loss as well as the obsolescence cost. The effects of various maintenance policies on the system performance are simulated, and they are used to determine the best policy for a given system. An illustrative example is used to demonstrate effectiveness and applicability of the proposed approach. The results presented a comparative analysis of specified maintenance policies with respect to the total maintenance cost with consideration of the product quality loss and the obsolescence cost.