Mining Approximate Dependencies from Diesel Engine Assembling Data with a MILP Model based on Rough Set  
Author Chunyu Gao


Co-Author(s) Wenbing Chang; Yiyong Xiao; Shenghan Zhou


Abstract Data mining is a process of discovering previously unknown and hidden knowledge from data sources. In this paper, we use the data mining technology to discover the hidden relationship between assembly clearance parameters and the quality levels of diesel engine. The diesel engine assembly clearance parameters are important factors that affect the quality of diesel engine assembling which is one of the most important factors that affect the long-run quality of diesel engine. We describe the problem with a mixed integer linear programming (MILP) model which is based on the rough set theory and solve it by using AMPL/CPLEX. A case study is provided which uses the model to discover the approximate dependencies between the assembly clearance parameters and the quality levels of the six-cylinder diesel engine.


Keywords assembly clearance, rough set, clustering method, mixed integer linear programming, data mining
    Article #:  22377
Proceedings of the 22nd ISSAT International Conference on Reliability and Quality in Design
August 4-6, 2016 - Los Angeles, California, U.S.A.