An approach for condition monitoring of a turning tool

Abstract

Manufacturing has changed markedly in recent years. The trend is for saving on the cost of production because of market pressure. In order to achieve this goal, greater consideration has been given to automation in manufacturing. In this regard, a fundamental step is to know the condition of the cutting tool, which requires a reliable system to monitor the condition of the tool. Experimental investigation of cutting tool wear and a model for tool wear estimation is reported in the current paper. The changes in the values of cutting forces, vibrations, and acoustic emissions with cutting tool wear are recorded and analysed. On the basis of experimental results a model is developed for tool wear estimation in turning operations using an adaptive neuro fuzzy inference system (ANFIS). Acoustic emission (ring down count), vibrations (acceleration), and cutting forces, along with time, have been used to formulate the model. This model is capable of estimating the wear rate of the cutting tool. The wear estimation results obtained by the model are compared with the practical results and are presented. The model performed quite satisfactorily and gave good results with the actual and predicted tool wear values.

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

“…ANFIS parameters have been chosen in accordance to the reported in Sharma et al (2007Sharma et al ( , 2008a. TWNFIS parameters are maintained with respect to Gajate et al (2009) unless the clustering threshold of QTCA.…”

“…However, the use of neuro-fuzzy systems for modeling and monitoring tool wear is scarce (Abellan-Nebot and Romero Subiron 2010). In the case of turning processes, there are only few approaches based on the Adaptive Network Fuzzy Inference System (ANFIS) or in some variation of itself (Dinakaran et al 2009;Li et al 2000Li et al ,2004Sharma et al 2007Sharma et al , 2008a. This paper presents two approaches for tool wear monitoring in turning processes based on neuro-fuzzy models.…”

Residence Time Distribution and Modeling of Mechanical Properties of Extruded Nanocomposite Foams Using Adaptive Neuro‐Fuzzy Inference System

“…Sugeno-type fuzzy inference systems were generated using Genfis2 which utilized subtractive clustering to compute the models for the properties. The purpose of clustering was to identify natural groupings of data from a large data set to produce a concise representation of the behavior of the system [20]. Subtractive clustering has been used in this study for estimating the number of clusters and the cluster centers in a set of data.…”

An Adaptive Neuro‐Fuzzy Inference System for Modeling Mechanical Properties of Tapioca Starch‐Poly(Lactic Acid) Nanocomposite Foams

“…Sugeno-type fuzzy inference systems were generated by using Genfis2 (Matlab fuzzy logic toolbox) which utilized subtractive clustering to compute the models for the product properties. The purpose of clustering was to identify natural groupings of data to produce a concise representation of the behavior of the system [21].…”