Modelling of Micro-Turning Process Based on Constant Cutting Force

Vukelić, Djordje; Kanović, Željko; Sokac, M.; Santosi, Zeljko; Budak, Igor; Tadić, Branko

doi:10.2507/ijsimm20-1-553

Cited by 4 publications

(3 citation statements)

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“…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

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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.

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Section: Methodsmentioning

confidence: 99%

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

Brili

Ficko

Klančnik

2021

Sensors

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“…They showed that for the TiAlN coated insert, lower surface roughness was observed at lower cutting speeds and higher feeds, while for the TiC coated insert, higher cutting speeds lead to lower surface roughness. Vukelic et al [27] modelled turning process with a constant cutting force using ANN. They reported that the surface roughness first improves and then worsens with the increase in cutting force, cutting time and number of revolutions.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Surface Roughness Based on Turning Parameters and Insert Geometry

Vukelić¹,

Prica²,

Ivanov³

et al. 2022

Int. j. simul. model.

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This study is focused on dry longitudinal turning of AISI steel using CVD coated cutting inserts. The machining was conducted at different levels of cutting speed, feed, depth of cut, corner radius, rake, inclination and approach angles. Surface roughness was measured after each experiment, and statistical analysis was used to derive an empirical, regression model for arithmetical mean surface roughness. The regression model was used to theoretically minimize surface roughness, followed by additional verification experiments. The 95 % confidence interval constructed using ten additional batteries of experiments, contained the theoretically predicted minimum roughness of Ra = 0.238 μm. The mean absolute prediction error of the optimal roughness equals 0.006 μm. The results reveal practical applicability of the developed model.

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“…In the study [29], a mechanical model of cutting forces was developed by considering the trajectory of the cutting edge and the geometric error of the system. The practical prevalence of instantaneous models of tool cutting edge loads in industry limits the time-consuming determination of tangential and radial material coefficients [30]. Experimental determination of material coefficients for multi-layer metallic materials, where machinability varies from layer to layer, represents an additional challenge.…”

Section: Introductionmentioning

confidence: 99%

An ANFIS-Mechanistic Simulator of Tool Loads in Ball-End Milling of Layered Metal Materials

Zuperl¹,

Kovacic²,

Brezocnik³

2022

Int. j. simul. model.

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This article presents a universal simulator of instantaneous loads on the cutting edges of ball-end mills for the processing of layered metal materials. The ANFIS-mechanistic simulator of cutting edge loads is designed for all typical geometries of ball-end mills and for layered materials with different machinability of individual layers. Two ANFIS (Adaptive Neuro Fuzzy Inference System) models are included in the simulator, which, based on the instantaneous radial thickness of the chip and the geometry of the spherical part of the ball-end mill, predict the material coefficients for the material layers. Comparison of the predicted tool loads with experimental data shows that the simulator accurately predicts the direction and magnitude of the instantaneous loads of the ball-end mill cutting edges when milling 3-layer metal material with different machinability of individual layers. The largest observed deviation of simulated load magnitudes for all verification machining tests is 21.7 %.

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Modelling of Micro-Turning Process Based on Constant Cutting Force

Cited by 4 publications

References 0 publications

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

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

Optimization of Surface Roughness Based on Turning Parameters and Insert Geometry

An ANFIS-Mechanistic Simulator of Tool Loads in Ball-End Milling of Layered Metal Materials

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