Degree Type

Creative Component

Semester of Graduation

Fall 2020


Industrial and Manufacturing Systems Engineering

First Major Professor

Guiping Hu


Master of Science (MS)


Industrial Engineering


The rising popularity of smart factories and Industry 4.0 has made it possible to collect large amounts of data from manufacturing production processes. Thus, supervised machine learning methods such as classification can viably predict product compliance quality using manufacturing data collected during production. While there has been thorough research on predicting the quality of specific manufacturing processes, the adoption of classification methods to predict the overall compliance of production batches has not been extensively investigated. Data pertaining to processes performed on a multi-model production line would contain significantly more features than that of an isolated process. The difficulty of analyzing such a large dataset makes it ideal for the application of data mining techniques to derive useful knowledge. This paper aims to design machine learning based classification methods for quality compliance and validate the models via case study of a multi-model appliance production line. The proposed classification model could achieve an accuracy of 0.99 and Cohen’s Kappa of 0.91 for the compliance quality of unit batches. Thus, the proposed method would enable implementation of a predictive model for compliance quality. The case study also highlights the importance of feature construction and dataset knowledge in training classification models.

Copyright Owner

Sankhye, Sidharth

File Format


Embargo Period (admin only)