Selection of Machining Parameters Using a Correlative Study of Cutting Tool Wear in High-Speed Turning of AISI 1045 Steel

González, Luis Felipe Verdeja; Pérez‐Rodríguez, Roberto; Zambrano‐Robledo, Patricia del Carmen; Mata, Martha Patricia Guerrero

doi:10.3390/jmmp2040066

Cited by 10 publications

(14 citation statements)

References 26 publications

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“…(2) Output layer design: The output node is the three wear states of the tool, that is, the output node is 3. (3) Hidden layer design: The number of hidden layer nodes is calculated preliminarily according to k = √ a + b + c, c ∈ [1,10] and then the final number of hidden layer nodes is determined by repeated experiments. After repeated experiments, the number of hidden layers is determined to be 20.…”

Section: Results Analysismentioning

confidence: 99%

“…According to the statistical research, when the automatic production equipment is equipped with a tool state detection system, the downtime will be reduced by 75%, the utilization rate of machine tools will be increased by at least 50%, and the total production efficiency will be increased by 10%-60% [7]. Therefore, the real-time monitoring of tool wear is of great significance to improve product quality and reduce processing costs [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data

Liu

Wang

et al. 2020

Applied Sciences

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The development of industry 4.0 has put forward higher requirements for modern milling technology. Monitoring the degree of milling tool wear during machine tool processing can improve product quality and reduce production losses. In the machining process of machine tools, many kinds of tools are usually used, and the signal characteristics of various sensors of different tools are different. Therefore, before the tool wear assessment, this paper identified the tool type according to the spindle current data. After the tool type recognition, this paper evaluates the tool wear degree according to the tool force data, vibration data, acoustic emission signal, and other multi-sensor data. Firstly, the Elman neural network and Adaboost algorithm are combined to construct the Elman_Adaboost strong predictor. Then, the variance and mean of seven sensor data were selected as the characteristic quantities to input the strong predictor. Finally, three wear quantities were obtained to measure the wear degree of the tool. The method proposed in this paper is implemented by Matlab, and the validity of this method is verified using the competition data provided by PHM (Prognostics and Health Management) Society. The results show that the average evaluation accuracy of the same tool wear is more than 92%, and that of the similar tool wear is more than 85%.

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Section: Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data

Liu

Wang

et al. 2020

Applied Sciences

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“…The composition of AISI 1045 steel is perlite-ferrite at 50%, with a medium carbon percentage, which is the maximum percentage for its group, leading to improved mechanical properties at the cost of making machinability more difficult. The chemical composition and mechanical properties of AISI 1045 are given in Table 1 [31].…”

Section: Sample Characteristicsmentioning

confidence: 99%

“…After the tests, the cutting tools were analyzed using the JEOL Scanning Electron Microscope. Every cutting edge of the cutting tools was previously inspected using the NIKON EPIPHOT optical microscope [31].…”

Section: Flow Stress Data Of the Workpiece And Substrate Properties Omentioning

confidence: 99%

Finite Element Modeling of Temperature Fields on the Cutting Edge in the Dry High-Speed Turning of Aisi 1045 Steel

et al. 2020

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High-speed turning is an advanced and emerging machining technique that, in contrast to the conventional machining, allows the manufacture of the workpiece with high accuracy, efficiency and quality, with lower production costs and with a considerable reduction in the machining times. The cutting tools used for the conventional machining cannot be employed for high-speed machining due to a high temperature induced in machining and a lower tool life. Therefore, it is necessary to study the influence of high cutting speeds on the temperature distribution in different typologies of cutting tools, with the aim of evaluating their behavior. In this paper, a finite element method modeling approach with arbitrary Lagrangian-Eulerian fully coupled thermal-stress analysis is employed. The research presents the results of different cutting tools (two coated carbide tools and uncoated cermet) effects on average surface temperature fields on the cutting edge in the dry high-speed turning of AISI 1045 steel. The numerical experiments were designed based on different cutting tools like input parameters and different temperature field zones like dependent variables in the dry high-speed turning of AISI 1045 steel. The results indicate that the dry high-speed turning of AISI 1045 steel does not influence significantly the temperature field zones when P10, P15 or P25 inserts are used. Therefore, the use of a dry high-speed turning method, which reduces the amount of lubricant and increases productivity, may represent an alternative to turning to the extent here described.

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“…Precision hard turning is considered a profitable and dependable alternative to grinding, with a reduction in machining time as high as 60% [5]. It is worth emphasizing that appropriate cutting conditions have to apply to enable precision hard turning to deliver very fine machined surfaces [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

FEM-Based Study of Precision Hard Turning of Stainless Steel 316L

et al. 2019

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This study aims to investigate chip formation and surface generation during the precision turning of stainless steel 316L samples. A Finite Element Method (FEM) was used to simulate the chipping process of the stainless steel but with only a restricted number of process parameters. A set of turning tests was carried out using tungsten carbide tools under similar cutting conditions to validate the results obtained from the FEM for the chipping process and at the same time to experimentally examine the generated surface roughness. These results helped in the analysis and understanding the chip formation process and the surface generation phenomena during the cutting process, especially on micro scale. Good agreement between experiments and FEM results was found, which confirmed that the cutting process was accurately simulated by the FEM and allowed the identification of the optimum process parameters to ensure high performance. Results obtained from the simulation revealed that, an applied feed equals to 0.75 of edge radius of new cutting tool is the optimal cutting conditions for stainless steel 316L. Moreover, the experimental results demonstrated that in contrast to conventional turning processes, a nonlinear relationship was found between the feed rate and obtainable surface roughness, with a minimum surface roughness obtained when the feed rate laid between 0.75 and 1.25 times the original cutting edge radius, for new and worn tools, respectively.

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Selection of Machining Parameters Using a Correlative Study of Cutting Tool Wear in High-Speed Turning of AISI 1045 Steel

Cited by 10 publications

References 26 publications

A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data

A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data

Finite Element Modeling of Temperature Fields on the Cutting Edge in the Dry High-Speed Turning of Aisi 1045 Steel

FEM-Based Study of Precision Hard Turning of Stainless Steel 316L

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