Nirmal Mangal ¹, M. Ramesh Nachiappan ¹, M. Elangovan ², V. Sugumaran ¹

Affiliations
1 School of Mechanical and Building Sciences, VIT University, Chennai - 600127, Tamil Nadu, IN
2 Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore. Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu, IN

Abstract

Objective: Tool condition monitoring is an important aspect of the modern day manufacturing system. It plays a significant role in increasing the efficiency of machining operation by identifying defects at a very early stage. Tool wear decreases the life of the tool considerably, increases the length of the machining process, also affects the surface finish and the dimensional accuracy of the product. To identify whether the tool is in a good or faulty condition, a monitoring system is essential. Method/Analysis: The fault diagnosis of the single point cutting tool was accomplished with the vibration signals obtained from auniaxial accelerometer attached to the cutting tool in a lathe machine. In this study, three different spindle speeds, feed rates and depth of cuts and four different wear levels of cutting tool are considered. Statistical data obtained from the signals is classified using a decision tree algorithm to get substantial features. The recognized features are considered in classifying data by using Simple CART classifier. Findings: The accuracy of the classifier was found to be 73.38% for the model with all the signals combined. The classification accuracy was observed to improve with the reduction in complexity of the model. The classification accuracy obtained for the model with only varying feed rate and depth of cut was in the range of 81–87 %. On further reduction of the model to have varying depth of cut was found to have a classification accuracy in the range of 81.5–91 %. The model with all the parameters independent yielded classification accuracy in the range of 81–100 %. Applications/Improvements: This study broadly analysed the use of simple CART classifier to diagnose fault in the cutting tool during machining. It can be used to increase productivity and reduce machine downtime. The improvements can be made to this study by considering different feature extraction techniques for more reliability.

Keywords

Decision Tree, Feature Extraction, Simple CART, Statistical Features, Tool Condition Monitoring

Full Text

   

Cynthia Deb ¹, M. Ramesh Nachiappan ¹, M. Elangovan ², V. Sugumaran ¹

Affiliations
1 School of Mechanical and Building Sciences, VIT University, Chennai - 600127, Tamil Nadu, IN
2 Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore. Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu, IN

Abstract

Objectives: There is a wide range of methods implemented for tool condition monitoring in the erstwhile manufacturing industry to ensure that the process continues uninterruptedly with minimal supervision. This monitoring method reduces the overall maintenance cost of machinery and prevents the occurrence of failure by prediction. This prior detection of tool wear, in turn, reduces the machine downtime and enhances machining efficiency. The progressive wear of a cutting tool can be detrimental to the quality of the machined surface, tolerances, dimensional accuracy and also adversely change the work or tool geometry. So the requirement of a diagnosing system with consistency is vital. Method/Analysis: This study deals with acquiring vibrational signals using accelerometer during turning operation performed on a lathe machine with good and fault simulated single point cutting tool. From the acquired signals, certain statistical features such as standard deviation, kurtosis etc. were extracted and substantial features were recognised using a decision tree algorithm. Those recognised features were deliberated in classifying data using random forest classifier. Findings: The accuracy of classification by the random forest classifier for all the signals combined together yields 74.4%. When considering feed rate and depth of cut as varying parameters yields an accuracy around 84%. Further an accuracy of around 88% was observed when considering depth of cut as varying parameter. When considering every experiment as a separate model yields around 95% classification accuracy. Applications/Improvements: This research work analysed the utilization of random forest classifier to identify the tool wear. It can be used in identifying the tool wear which affects surface finish, dimensional accuracy and tolerance of the part during machining. This work can be improved by analysing with different classifier algorithms to efficiently predict the tool wear.

Keywords

Confusion Matrix, Decision Tree, Feature Extraction, Random Forest, Statistical Features, Tool Condition Monitoring

Full Text

   

M. Ramesh Nachiappan ¹, V. Sugumaran ¹, M. Elangovan ²

Affiliations
1 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Chennai - 600127, Tamil Nadu, IN
2 Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham,Amrita University, Coimbatore – 641112, Tamil Nadu, IN

Abstract

Objective: A variety of tool condition monitoring techniques in modern manufacturing system plays a key role in estimating the tool wear which can save the machine downtime and increase the cutting tool utilization. Tool wear compromises dimensional accuracy and affects the precision, tolerance and surface finish. An active condition monitoring system of tool health is required for superior productivity. Method/Analysis: In this experimental study, the accelerometer was used to acquire the vibration signal during the turning operation in a lathe machine with good and fault simulated single point cutting tool. The signals are acquired for all combinations of spindle speeds, feed rates, depth of cuts and tool wear level. In this study, 3 different spindle speeds, feed rates and depth of cuts, and 4 different tool wear levels were considered. Statistical features were extracted from the acquired signal and substantial features were recognized using a decision tree algorithm. The identified substantial statistical features were considered in classifying signals using logistic model tree classifier. Findings: The classification accuracy obtained for all the signals combined (i.e., variable spindle speeds, feed rates, depth of cuts and tool wear levels) were found to be 74.27%. The classification accuracy achieved was improved through simplifying the model by considering feed rate and depth of cut as variable factor. The accuracy of the classification was found to be in the range of 82-86%. Further, the classification accuracy was found to increase to the range of 82-93%, when considering the depth of cut alone as variable factor. Application/Improvement: The utilization of logistic model tree to identify the tool wear level in a single point cutting tool during turning operation was comprehensively analysed in this study. The performance of the classifier on fault diagnosis of single point cutting tool and its improvement by reducing the complexity of the model was discussed.

Keywords

Decision Tree, Feature Extraction, Logistic Model Tree, Logistic Regression, Statistical Features Tool Condition Monitoring.

Full Text