Quality Improvement using Data Mining in Manufacturing Processes

Nowadays, manufacturing enterprises have to stay competitive in order to survive the competition in the global market. Quality, cost and cycle time are considered as decisive factors when a manufacturing enterprise competes against its peers. Among them, quality is viewed as the more critical for getting long-term competitive advantages. The development of information technology and sensor technology has enabled large-scale data collection when monitoring the manufacturing processes. Those data could be potentially useful when learning patterns and knowledge for the purpose of quality improvement in manufacturing processes. However, due to the large amount of data, it can be difficult to discover the knowledge hidden in the data without proper tools. Data mining provides a set of techniques to study patterns in data “that can be sought automatically, identified, validated, and used for prediction” (Witten and Frank 2005). Typical data mining techniques include clustering, association rule mining, classification, and regression. In recent years data mining began to be applied to quality diagnosis and quality improvement in complicated manufacturing processes, such as semiconductor manufacturing and steel making. It has become an emerging topic in the field of quality engineering. Andrew Kusiak (2001) used a decision tree algorithm to identify the cause of soldering defects on circuit board. The rules derived from the decision tree greatly simplified the process of quality diagnosis. Shao-Chuang Hsu (2007) and Chen-Fu Chien (2006 and 2007) demonstrated the use of data mining on semiconductor yield improvement. Data mining has also been applied to product development process (Bakesh Menon, 2004) and assembly lines (Sébastien Gebus,2007). Some researchers combined data mining and traditional statistical methods and applied to quality improvement. Examples are the use of MSPC (multivariate statistical control charts) and neural networks in detergent-making company (Seyed Taghi Akhavan Niaki, 2005; Tai-Yue Wang, 2002), the combination of automated decision system and six sigma in the General Electric financial Assurance businesses (Angie Patterson, 2005), the combined used of decision tree and SPC with data from Holmes and Mergen (Ruey-Shiang Guh, 2008), the use of SVR (support vector regression) and control charts (Ben Khediri ISSam, 2008), the use of ANN (artificial neural O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg