Selection of Cutting Regime for Self-Propelled Rotary Cutting Tools

Chang, Xu; Chen, W; Pang, Xiaolin; Zhi, Gu

doi:10.1243/pime_proc_1995_209_054_02

Cited by 5 publications

(2 citation statements)

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“…The turning method with a rotary cutting tool is described with the ability of a round insert to rotate about its own axis. 1 This method can significantly reduce tool wear and cutting heat thanks to its self-cooling ability and tendency to reduce cutting forces. 2 Moreover, this method is suitable for machining high hardness materials.…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

Bien

2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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This present paper deals with the results of setting up and evaluating the quality of surface roughness (SR) prediction models through hard turning with self-driven rotary cutting tools for 40X steel shaft with hardness 45HRC. The cutting parameters are considered to establish the SR prediction model include angle tilt of cutting tool axis, depth of cut, feed rate, and cutting speed. The predictive models (PM) are established utilizing Multi-variables Regression Analysis (MRA), Artificial Neural Network (ANN), and Genetic Programming (GP) methods. In this regard, four MRA, four ANN, and three GP structures are considered to select the most suitable model. The criteria to estimate the PM quality include Coefficient of Determination ( R2), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE). Whereby, two data sets were collected to construct regression models (RM) and to serve verification. That dataset was originated from 63 experiments (Ep) including 54 Ep for establishing PM and 9 extensive experiments (EEp) for testing PM. The prediction criteria of the MRA3 model gave the best results in four MRA models with R2 of 0.99, MSE of 0.042, and MAPE of 8.087%. The ANN1 gives the most reliable assessment criteria in four ANN models and the GP3 model gave the best in three GP models. The results of predicting the SR value between the selected MRA and ANN and GP models will be assessed in detail. Accordingly, the evaluation criteria of the ANN1 model are the best with the smallest MSE (0.032) and MAPE (7.207%). The MRA3 and GP3 have a lower confidence predictive criteria value than the ANN1 model.

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Section: Introductionmentioning

confidence: 99%

Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

Bien

2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…This causes conduction of a minimum amount of heat energy into the machined component, consequently minimizing the structural defects associated with the cutting process. SPRTs can be used at much higher speeds and feeds than conventional cutting tools without signi®cant adverse eVect on tool life owing to the lower cutting temperature generated [6]. Tool life and surface abuse due to the concentration of cutting temperature at the tool±workpiece interface are key machinability considerations when machining aerospace materials such as titanium alloys.…”

Section: Introductionmentioning

confidence: 99%

Wear evaluation of a self-propelled rotary tool when machining titanium alloy IMI 318

Ezugwu

Olajire

Wang

2002

Proceedings of the Institution of Mechanical Engineers, Part B:

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This paper presents results of a self-propelled rotary tool (SPRT) tipped with round uncoated carbide (WC-Co) inserts in the finish turning of titanium alloy IMI 318. SPRT inserts exhibited superior wear resistance to conventional round and rhomboid-shaped carbides as well as to the rhomboid-shaped physical vapour deposition (PVD) TiN-coated carbide inserts owing to the distribution of the cutting energy along the entire tool edge. The SPRT-tipped inserts suppressed thermally related wear mechanisms at higher speeds beyond the capability of conventional tools. Chipping was the dominant failure mode of the SPRT inserts, resulting from cyclically fluctuating thermal and mechanical shocks induced by the continuous shifting of the cutting edge during machining. A fitted exponential wear model revealed that about 97 per cent of the average flank wear rate of SPRT inserts can be attributed to the joint effect of speed, feed and inclination angle, ε. Tool performance was improved by increasing the inclination angle ε which resulted in a lower machining temperature on account of a higher rotary speed and a lower relative cutting speed during machining. Lower component forces were recorded with SPRT inserts because of the reduced amount of work done in chip formation and lower friction at the rake face.

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Environmentally Conscious Machining

Kishawy

Hosseini

2018

Materials Forming, Machining and Tribology

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Selection of Cutting Regime for Self-Propelled Rotary Cutting Tools

Cited by 5 publications

References 0 publications

Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

Wear evaluation of a self-propelled rotary tool when machining titanium alloy IMI 318

Environmentally Conscious Machining

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