Fault decision of computer numerical control machine system using grey clustering analysis and rough set theory

• Main Authors: Xianlin Ren, Leo Chen, DeShun Li, ZeZhao Pang
• Format: Article
• Language: English
• Published: SAGE Publishing 2019-05-01
• Series: Advances in Mechanical Engineering
• Online Access: https://doi.org/10.1177/1687814019852846

The computer numerical control machine is important industrial equipment, and its reliability has been one of the most important symbols to measure the modernization of advanced manufacturing and it is critical in the aspects of reliability design improvement, fault monitoring, and fault repair for the computer numerical control machine. The computer numerical control machine’s assembly process is a significant part in its manufacturing process, and assembly operation is a major factor in determining the whole machine’s quality, and assembly process quality analysis is necessary for computer numerical control machines, in which reliability allocation is an essential part of its reliability design. In order to quickly locate the fault of computer numerical control machine tool and accurately judge the fault grade, a method of fault classification decision of computer numerical control machine tool based on motion micro-unit is proposed, which includes the following steps: (1) from the point of the decomposition of system function, the computer numerical control machine tool is decomposed layer by layer into the layer of micro-actions, and the conceptual model of motion unit is given; (2) from the level of action, the types of fault modes of motion units are comprehensively analyzed and summarized; and (3) combining grey clustering theory and rough set theory, a fast and accurate fault classification decision-making method is formed. Finally, the validity of this method is verified by an example analysis of motion micro-units of a computer numerical control machine rack. The contributions of this work can be summarized as (1) the proposed grey fixed weight clustering analysis, (2) the graded fault classification using the decision table approach, (3) the knowledge reduction of decision rules using the rough set theory, and (4) the quicker and accurater decision and effectiveness validated by the given study case.