A Review of Business Intelligence Techniques for Mild Steel Defect Diagnosis

In this competitive era, manufacturing companies have to focus on the quality of the produced products. The quality of the product produced is affected by many influential parameters during the process. The product once produced with a lower quality then usually ends up with incurring loss in certain terms to the company. Hence, it is extremely important to know the defect causing parameters and perform defect diagnosis. Various techniques like SPC-SQC, Six-Sigma and Kaizen have been used for quality analysis. But since last few years machine learning and data mining is being used for analysis due to advancement in the field and its advantages. This paper conducts an analytical survey of various business intelligence techniques used in for defect diagnosis. The paper concludes with the analytical results as random forest performs the best in terms of performance compared to other techniques and shows the future research scope in this area. Moreover, we find that random forest has not been introduced yet in steel defect diagnosis. General Terms Statistics, data mining, neural network, genetic algorithm, and ensemble approach