Determining tool/chip temperatures from thermography measurements in metal cutting

Saez-de-Buruaga, M.; Soler, D.; Aristimuño, Patxi; Esnaola, J.A.; Arrazola, P.J.

doi:10.1016/j.applthermaleng.2018.09.051

Cited by 60 publications

(27 citation statements)

References 31 publications

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“…An important influencing factor on the cutting tool wear is a tool’s thermal load. Finding correlations between machining parameters (turning, milling) and the cutting temperature is frequently an object of research [ 11 , 12 , 13 , 14 , 15 ]. There are similar conclusions: higher temperature causes greater tool wear, and cutting speed has the largest effect on the temperature.…”

Section: Introductionmentioning

confidence: 99%

“…The IR camera measurements are ordinarily carried out laterally, i.e., perpendicular to the direction of the cutting speed, so a model was developed that calculates temperatures at the contact point between a tool and a chip. This is done based on the given process parameters (cutting force, chip thickness, tool-chip contact length) and a lateral thermal image of the tool [ 13 , 14 ]. It was concluded that a two-fold increase in the cutting speed (from 100 m/min to 200 m/min) causes a 20% increase in the tool temperature.…”

Section: Introductionmentioning

confidence: 99%

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

Brili

Ficko

Klančnik

2021

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

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“…Saez-de-Buruaga et al [ 7 ] proposed a methodology that determined the influence of cutting conditions on the developed cutting temperatures that was compared to the 2D simulated results of the tool/chip contact temperatures. Ye et al [ 8 ] studied high speed cuttings of various metallic materials over wide ranges of cutting speeds.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Nose Radius on the Machining Forces Induced during AISI-4140 Hard Turning: A CAD-Based and 3D FEM Approach

et al. 2020

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The present study investigated the performance of three ceramic inserts in terms of the micro-geometry (nose radius and cutting edge type) with the aid of a 3D finite element (FE) model. A set of nine simulation runs was performed according to three levels of cutting speed and feed rate with respect to a predefined depth of cut and tool nose radius. The yielded results were compared to the experimental values that were acquired at identical cutting conditions as the simulated ones for verification purposes. Consequently, two more sets of nine simulations each were carried out so that a total of 27 turning simulation runs would adduce. The two extra sets corresponded to the same cutting conditions, but to different cutting tools (with varied nose radius). Moreover, a prediction model was established based on statistical methodologies such as the response surface methodology (RSM) and the analysis of variance (ANOVA), further investigating the relationship between the critical parameters (cutting speed, feed rate, and nose radius) and their influence on the generated turning force components. The comparison between the experimental values of the cutting force components and the simulated ones demonstrated an increased correlation that exceeded 89%. Similarly, the values derived from the statistical model were in compliance with the equivalent FE model values due to the verified adequacy.

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“…Various methods by many researchers have been used to study chip geometry. Some of them analyzed chip geometry from an analytical point of view, resulting in the formulation of some theoretical models [26][27][28]. However, in these cases, many simplifications had to be made with regard to the complexity of the chip formation process and, hence, some of the real, practically obtained results have not correspond to the predictions of various parameters to set up a chip geometry, and the results were inaccurate [29][30][31].…”

Section: State Of the Artmentioning

confidence: 99%

Comparative Study of Chip Formation in Orthogonal and Oblique Slow-Rate Machining of EN 16MnCr5 Steel

Monková

Monka

Sekerakova

et al. 2019

Metals

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In today's unmanned productions systems, it is very important that the manufacturing processes are carried out efficiently and smoothly. Therefore, controlling chip formation becomes an essential issue to be dealt with. It can be said that the material removal from a workpiece using machining is based on the degradation of material cohesion made in a controlled manner. The aim of the study was to understand the chip formation mechanisms that can, during uncontrolled processes, result in the formation and propagation of microcracks on the machined surface and, as such, cause failure of a component during its operation. This article addresses some aspects of chip formation in the orthogonal and oblique slow-rate machining of EN 16MnCr5 steel. In order to avoid chip root deformation and its thermal influence on sample acquisition, that could cause the changes in the microstructure of material, a new reliable method for sample acquisition has been developed in this research. The results of the experiments have been statistically processed. The obtained dependencies have uncovered how the cutting tool geometry and cutting conditions influence a chip shape, temperature in cutting area, or microhardness according to Vickers in the area of shear angle.

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Determining tool/chip temperatures from thermography measurements in metal cutting

Cited by 60 publications

References 31 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

Influence of the Nose Radius on the Machining Forces Induced during AISI-4140 Hard Turning: A CAD-Based and 3D FEM Approach

Comparative Study of Chip Formation in Orthogonal and Oblique Slow-Rate Machining of EN 16MnCr5 Steel

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