There have been many studies, mainly by the use of statistical modeling techniques, as to predicting quality characteristics in machining operations where a large number of process variables need to be considered. In conventional metal removal processes, however, an exact prediction of surface roughness is not possible or very difficult to achieve, due to the stochastic nature of machining processes. In this paper, a novel approach is proposed to solve the quality assurance problem in predicting the acceptance of computer numerical control (CNC) machined parts, rather than focusing on the prediction of precise surface roughness values. One of the data mining techniques, called rough set theory, is applied to derive rules for the process variables that contribute to the surface roughness. The proposed rule-composing algorithm and rule-validation procedure have been tested with the historical data the company has collected over the years. The results indicate a higher accuracy over the statistical approaches in terms of predicting acceptance level of surface roughness.
Traditionally, the quality of a machined product has been measured based on the specifications, once the machining process has been completed. However, this post-process inspection has several shortcomings: (1) the manufacturing cost has already been incurred when a non-conformance is detected, (2) it is difficult to isolate the causes of the defect, (3) there could be a significant time lag between discovery of the defect and corrective action, and (4) rework of any scope increases manufacturing cost and can be very difficult to accomplish. Today, efforts of manufacturers are shifting from the post-process inspection to improved monitoring of the manufacturing process, utilizing sensors and other measurement devices to effectively control the process. Improvements in machining precision can only be accomplished by the development of manufacturing systems that are capable of monitoring processes. Process monitoring reduces scrap, rework, lead-time, and conventional non-value-added inspection activities, and thereby increases the system's productivity. However, monitoring has to be based on sound, reliable process control algorithms. On the human side, an expert machinist can detect and/or predict deteriorating cutting conditions through the use of his/her senses so that appropriate corrective actions can be taken before the part quality is lost. This level of expertise requires many years of experience, yet does not guarantee that conditions will be monitored in effective and consistent manner, even for the most experienced operator. On the mechanical side, computer numerical control (CNC) of machine tools can produce consistent part quality, yet most cases, do not utilize sensor data to compensate for anomalies generated by the cutting process (e.g., tool wear, chatter, incorrect machine setup, etc.). If sensors such as cutting force, vibration and spindle motor current were integrated into CNC machine tools, the control functions should be able to interpret and respond to sensory data as process continues. However, when many process variables need to be considered, it is rather difficult to predict quality attribute in machining (i.e., surface roughness).
This study, unlike the conventional statistical regression modeling approach, uses a data mining technique called the rough set theory (RST) [1] to identify variables affecting the quality characteristic of CNC machining operations. Instead of predicting exact surface roughness values, the focus is on the prediction of quality acceptance in machined parts. RST is a viable approach for extracting meaningful knowledge and making predictions for an individual data object, rather than a population of objects [2]. RST is introduced as an extension of the set theory for the study of intelligent systems characterized by incomplete information to classify imprecise, uncertain, or incomplete information or knowledge expressed in terms of data. RST is an effective tool for multi-attribute classification problems. This can be instrumental in constructing an intelligent control system, especially when a clear delineation within variables as to how they affect the surface roughness is difficult to achieve.
Based on the historical data, this study employed rough set theory (RST) that connects with the causal relationships between the features of the machining process and acceptance of surface roughness. According to the rule-composing algorithm and the rule-validation procedure, RST provides a high-accuracy prediction tool for investigating features that contribute to surface roughness. The features included in the rules derived by a rough set algorithm form patterns of different shapes and therefore, offer a different predictive power. Furthermore, the derived rules constitute the basis for developing a rule-based intelligent control system for surface roughness in the machining operation process. The analytical effectiveness of a rule-based intelligent control system relies on its ability to explain its reasoning results. RST provides important information for acceptance of surface roughness in the machining operations. The results showed practical viability of the RST approach for quality control.
