Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

Abstract

This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9,000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator’s knowledge and competence.

Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy

“…The CNN algorithm is a subset of deep neural networks and deep learning paradigms [ 22 ], and has proven its effectiveness as an image, speech recognition, face detection, futures extraction algorithm. The novel research confirms that CNNs have advantages in series forecasting, a data-driven approach for diagnostic and fault classification of various industrial processes and applications [ 23 , 24 , 25 , 26 , 27 ].…”

“…The comparisons with support vector machine, naive Bayes, classification tree, and discriminant analysis [ 24 , 25 , 26 ] are presented in Table 7 and Figure 14 . All classification algorithms SVM, NB, CT, and DA used 1D data.…”

“…The number of kernels, size and stride needs to be selected in the design of the CNN structure. The CNN parameters’ selection and validation is a complex optimization procedure that is extremely time-consuming and requires many iterations and analyses [ 26 ]. Four preprocessing methods are used in the presented work.…”

Tool Condition Monitoring of the Cutting Capability of a Turning Tool Based on Thermography

“…The wear of inserts was determined before the experiment was executed, i.e., based on two methods: Empirically (an expert’s diagnosis backed by their knowledge and experience) and Niakiev’s method [ 26 ] (indirect tool wear estimation by measuring the workpiece’s diameter). Both methods are discussed in detail in the paper [ 25 ]. Criteria for workpiece’s diameter deviation by Niaki’s method ( Table 1 ) was determined regarding to the dimensional requirements, the diameter of the workpiece, and the type of cutting insert, considering that toolmaking is characterized by narrow tolerances.…”

“…The method used is based on Machine Vision, Convolutional Neural Network (CNN), and Transfer Learning (TL). Brili et al [ 25 ] were able to use a thermal IR (infrared) camera to monitor the cutting process successfully. Furthermore, thermographic images of chips were analysed with the CNN, a well-known Machine Vision model.…”