Autonomous maintenance decision model for lathe machine using fuzzy analytical hierarchy process (AHP) method

Abstract

Deterioration on production machine may lead to high production costs. One of the preventive maintenance strategies to reduce deterioration of machine is Autonomous Maintenance (AM). The aim of autonomous maintenance is to achieve a high degree of cleanliness, excellent lubrication and proper fastening on the machine. However, the conventional AM practice, the process of initial cleaning might increase the maintenance cost and the time required. Therefore, to make this process more effective and efficient, this study proposes an AM decision model using fuzzy Analytical Hierarchy Process (AHP) method to identify the critical components and to determine the right AM activities. A case study of a lathe machine is used to validate the model. The data were collected through personnel interview with technicians at machine shop laboratory in UniMAP. In this study, fuzzy AHP is carried out using pairwise comparison data to verify the critical components. Finding of the analysis reveals that there are eight critical components of the lathe machine that have been identified. By having this information, the model does help in minimizing the maintenance costs and time by identifying the right component for maintenance and so to determine the right maintenance activities to be carried out. Thus, this study has provide theoretical and practical inferences about the development of AM decision model.