CLOSED-LOOP ENGINEERING INTEGRATING REVERSE ENGINEERING AND DIMENSIONAL VARIATION ANALYSIS (DVA) TO MITIGATE LIFECYCLE RISK IN HEAVY EQUIPMENT

Abstract

This paper explores a closed-loop engineering methodology where reverse engineering (RE) and dimensional variation analysis (DVA) is coupled to reduce lifecycle risk for heavy equipment manufacturing and maintenance. The research is based on 120 key components of the working system of the 15 heavy machinery-run heavy loaded performance. The reverse engineering technique was employed to re-model the objects as digital computer models, achieved an average geometry fidelity of 18.6% over the legacy CAD data. Statistical tolerance models were developed for dimension variability analysis in 3 manufacturing and 4 refurbishment cycles. The reduction rate of the screw sleeve's dimensional deviation is 27.4%, and that of the assembling fit degree consistency is 21.8%. Through the proposed closed-loop feedback, a real-time updating of design was feasible which resulted in an overall decrease by 32.1% rework and 24.7% unplanned downtime. Life cycle risk scores, based on failure probability and cost-impact indices, reflect a reduction of the overall risk with 29.3% along the life time. Furthermore, the range of maintenance cost was decreased by 19.5%, and the use efficiency of parts was improved from 62% to 81%. These quantitative results provide reliable evidence to support dimensionally correct and cost-effective closed-loop engineering systems combined with RE and DVA for industrial product lifecycles, informing data-driven decisions in an effort to improve sustainability and resilience of the heavy machinery.