Classifying defect factors in fabric production via DIFACONN-miner: A case study

In this paper a data mining based case study is carried out in a major textile company in Turkey in order to classify and analyze the defect factors in their fabric production process. It is aimed to understand the causes of the defects in order to minimize their occurrence. The main motivation behind this study is to minimize scrap loses in the company and enabling more sustainable production via data mining. In the analyses, a data mining tool (DIFACONN-miner) that was recently developed by authors is employed. DIFACONN-miner is a novel data mining tool which combines several metaheuristics and artificial neural networks intelligently and it is capable of producing comprehensive classification rules from any data type.

Research highlights
► In this paper a novel approach is provided to classify quality defects in fabric production.
► A real life case study and its results are provided.
► A novel data mining tool, DIFACONN-miner which was recently provided by the authors was utilized to model and solve the problem.
► It is shown that DIFACONN-miner is able to model and classify quality defects in fabric production.