DATA MINING APPLICATION USING DECISION TREE: INTRODUCTION

INTRODUCTION

Manufacturing, the fundamental part of the economy, is one of the most complex industries. Manufacturing systems consist of several subparts processed parallel or sequential manner and they are influenced by many factors. Each of these parts produces data during the processing. Developments in database technology and computer science (faster computers, more memory, storage capability, and automatic data collection tools) enable the companies to collect and store significant amount of data easily. The complex structure of manufacturing systems, traditional data analysis techniques have some limitations. They have computational limitations in terms of number of dimensions, number of observations, etc. Interactions among the process variables are not easily modelled by the traditional techniques.

The challenge of modern machining industries is mainly focused on the achievement of high quality, in term of work piece dimensional accuracy, surface finish, high production rate, less wear on the cutting tools, economy of machining in terms of cost saving and increase of the performance of the product with reduced environmental impact. End milling is a very commonly used machining process in industry. The ability to control the process for better quality of the final product is paramount importance. The mechanism behind the formation of surface roughness in CNC milling process is very dynamic, complicated, and process dependent. Several factors will influence the final surface roughness in a CNC milling operations such as controllable factors (spindle speed, feed rate and depth of cut) and uncontrollable factors (tool geometry and material properties of both tool and work piece.

The use of data mining techniques in manufacturing began in the 1990 and it has gradually progressed by receiving attention from the production community. Data mining is now use in many different areas in manufacturing engineering to extract knowledge for use in predictive maintenance, fault detection, design, production, quality assurance, scheduling, and decision support systems. Data can be analyzed to identify hidden patterns in the parameters that control manufacturing processes or to determine and improve the quality of products. A major advantage of data mining is that the required data for analysis can be collected during the normal operations of the manufacturing process being studied and it is therefore generally not necessary to introduce dedicated processes for data collection.

Data mining model (Decision Tree & Artificial Neural Network) is state of the art artificial intelligent method that has possibility to enhance the prediction of surface roughness. This paper will present the application of data mining to predict surface roughness for CNC milling process. The accuracy of these techniques to predict surface roughness will be compared with each other.

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