Development of family of artificial neural networks for the prediction of cutting tool condition

Spaić, Obrad; Krivokapic, Zdravko; Kramar, Davorin

doi:10.14743/apem2020.2.356

Cited by 10 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many authors considered the possibilities of automatic cutting tool condition control. They found that the most suitable approach for modelling non-linear dependencies are Artificial Intelligence methods, namely artificial neural networks (ANN), fuzzy logic systems, or a hybrid of both [ 9 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

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

Brili

Ficko

Klančnik

2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Brili

Ficko

Klančnik

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Some researchers even try to control the surface texture with process parameters [ 3 ]. Many traditional modeling techniques, such as regression analysis, do not provide satisfactory results, especially when the relationship between the target function and the influencing parameters is non-linear, as is usually the case in complex phenomena (such as AWJ machining); this indicates the appropriateness of artificial neural network (ANN) based methods for overall cutting process modeling [ 4 ]. For modeling and optimization of any machining processes, ANNs [ 5 ], an adaptive neuro-fuzzy inference system (ANFIS) [ 6 , 7 ], and other intelligent techniques [ 8 ] are common.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

et al. 2021

View full text Add to dashboard Cite

The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using k-fold cross-validation. A lowest test root mean squared error (RMSE) of 0.2084 was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a 95% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation.

show abstract

“…The input to a Neural Network is a Table, where each row represents one input data with multiple parameters/variables (columns). Typically, 3 to 8 input variables are used [ 29 ], for example: number of revolutions, machining time, and cutting force [ 30 ]; material of the tool, the sharpening mode, the nominal diameter, the number of revolutions, the feed rate and the drilling length [ 31 ], depth of cut, cutting speed, and feed to the tooth [ 32 ]. A study using 20 input variables to predict tool life has shown that slightly more inputs are also possible, even if Fully Connected Neural Networks are used [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

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

Brili

Ficko

Klančnik

2021

Sensors

View full text Add to dashboard Cite

In turning, the wear control of a cutting tool benefits product quality enhancement, tool-related costs‘ optimisation, and assists in avoiding undesired events. In small series and individual production, the machine operator is the one who determines when to change a cutting tool, based upon their experience. Bad decisions can often lead to greater costs, production downtime, and scrap. In this paper, a Tool Condition Monitoring (TCM) system is presented that automatically classifies tool wear of turning tools into four classes (no, low, medium, high wear). A cutting tool was monitored with infrared (IR) camera immediately after the cut and in the following 60 s. The Convolutional Neural Network Inception V3 was used to analyse and classify the thermographic images, which were divided into different groups depending on the time of acquisition. Based on classification result, one gets information about the cutting capability of the tool for further machining. The proposed model, combining Infrared Thermography, Computer Vision, and Deep Learning, proved to be a suitable method with results of more than 96% accuracy. The most appropriate time of image acquisition is 6–12 s after the cut is finished. While existing temperature based TCM systems focus on measuring a cutting tool absolute temperature, the proposed system analyses a temperature distribution (relative temperatures) on the whole image based on image features.

show abstract

Development of family of artificial neural networks for the prediction of cutting tool condition

Cited by 10 publications

References 11 publications

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

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

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

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

Contact Info

Product

Resources

About