Abstract
Growth in the manufacturing sector demands extensive production with precision, accuracy, tolerance, and quality. These essential factors need to be ensured for any kind of job. The listed factors stated above depend upon the condition of the tool used for manufacturing. A lot of methods have been proposed for the tool condition monitoring, based on the data acquired through acquisition techniques. Despite the continuous intensive scientific research for more than a decade, the development of tool condition monitoring is an on-going attempt. The proposed method deals with monitoring the health condition of the carbide inserts using vibration analysis. The statistical information extracted from the vibration signals was analyzed using machine learning approach in order to predict the tool condition.
Highlights
In manufacturing industries, single point cutting tool inserts and drill bits are widely used as a tool for machining the components
The results show the effectiveness of the features that were extracted features from the acquired vibration signals
The statistical information like sample variance, standard error, kurtosis, skewness, minimum, standard deviation, maximum, count, mean, median, mode, and sum are extracted from the raw vibration signals under each conditions using a suitable feature extraction technique
Summary
Single point cutting tool inserts and drill bits are widely used as a tool for machining the components. Statistical features extracted from the vibration signals were used for monitoring the tool condition. Babudevasenapathy, Sakthivel, and Ramachandran (2011) used decision tree for feature selection in the tool condition monitoring study. Babudevasenapati, Sakthivel, Ramachandran, 2011) developed an expert system for condition monitoring of a single point cutting tool using decision tree algorithm. Support vector machine algorithm using statistical features was studied for monitoring the condition of a single point cutting tool (Elangovan, Babudevasenapati, & Ramachandran, 2009). Several machine learning algorithms like a best first tree (Jegadeeshwaran, & Sugumaran, 2013, proximal support vector machines (Saimurugan, Ramachandran, Sugumaran, and Sakthivel, 2011), were reported for achieving better results in various condition monitoring study. In this study, the carbide insert condition monitoring has been performed using the machine learning algorithms like decision tree and random tree. The results show the effectiveness of the features that were extracted features from the acquired vibration signals
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