DATA MINING APPLICATION USING DECISION TREE: LITERATURE REVIEW

LITERATURE REVIEW

Data mining tools were reported to be successful to extract desired knowledge from the manufacturing data. For example, Braha & Shmilovici (2002) identified the factors that are significant in the cleaning process in semiconductor industry by using decision tree and neural network Previously, Feng & Wang (2003) developed an empirical model for surface roughness by using non-linear regression and neural network techniques to predict quality in metal casting industry.

Oktem et al. (2005) proposed the genetic programming approach to predict surface roughness based on cutting parameters (spindle speed, feed rate and depth of cut) and on vibrations between cutting tool and work piece. From this research, they conclude that the models that involve three cutting parameters and also vibrating, give the most accurate predictions of surface roughness by using genetic programming.

Later on 2007, Chang et al. were established a method to predict surface roughness in-process. In their research, roughness of machined surface was assumed to be generated by the relative motion between tool and work piece and the geometric factors of a tool. The relative motion caused by the machining process could be measured in process using a cylindrical capacitive displacement sensor (CCDS). The CCDS was installed at the quill of a spindle and the sensing was not disturbed by the cutting. A simple linear regression model was developed to predict surface roughness using the measured signals of relative motion. Surface roughness was predicted from the displacement signal of spindle motion.

T asdemir et al. (2008) applied ANN to predict surface roughness a turning process. This method was found to be quite effective and utilizes fewer training and testing data.

Representative APR 391%

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