Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network

Rao, K. Venkata; Murthy, B. S.; Rao, N. Mohan

doi:10.1016/j.measurement.2014.01.024

Cited by 121 publications

(22 citation statements)

References 19 publications

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“…In boring operation, boring bar plays a vital role and it affects the surface roughness, tool wear and influences the cutting forces due to the boring bar vibrations or deflections. In boring operation, vibration is the main factor, which effects the surface roughness, tool wear and cutting forces [1]. Mourad et al [2] indicated that identification of chatter in machining processes is critical part for enhancing the surface quality and eliminating the noise and cutting tool wear.…”

Section: Introductionmentioning

confidence: 99%

“…Development of statistical and mathematical models are important to optimal cutting conditions in order to achieve quality products with minimal production cost, time. Machining variables optimization and evolution of the statistical models are vital role in production process [1]. Hosseini et al [11] made a review on optimization methods and techniques, they reported the use of optimization problems in the field of manufacturing and also they concluded that many of the authors are extensively used optimization of input processing variables.…”

Section: Introductionmentioning

confidence: 99%

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Experimental studies of machining parameters on surface roughness, flank wear, cutting forces and work piece vibration in boring of AISI 4340 steels: modelling and optimization approach

et al. 2018

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The statistical mathematical models are developed to investigate the influence of cutting parameters on surface roughness, tool wear, cutting force, tangential force and the work piece vibration in boring of AISI 4340 steels. A full factorial design of experiments is used to conduct 27 experiments on AISI 4340 as the work piece material and TiCN-Al 2 O 3-TiN multi-layered coated carbide inserts. Online data acquisition of cutting forces on the cutting tool and the work piece vibrations are measured by using piezo-electric dynamometer and laser Doppler vibrometer, respectively. This paper proposes optimization method using grey relational analysis (GRA) and predictive models like support vector machine and response surface method are used to predict the GRG values and optimize the machining parameters. The GRA is used for converting multi response optimization problem into optimization of single objective of grey relational grade (GRG). Finally confirmation test was performed and also optimized the machining parameters to minimize the surface roughness (Ra), tool wear (VB), cutting force (Fx), tangential force (Fz) and work piece vibration (VA).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental studies of machining parameters on surface roughness, flank wear, cutting forces and work piece vibration in boring of AISI 4340 steels: modelling and optimization approach

et al. 2018

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“…Venkatarao et al [1] studied the effect of various input cutting parameters such as cutting speed, feed rate, and tool nose radius on tool life in boring for AISI 1040steel by analyzing surface roughness, amplitude of work piece vibration and volume of metal removed. Venkatarao et al [2] using an artificial neural network to predict the cutting tool wear, surface roughness and vibration of the work piece in boring of AISI 316 steel. Chun et al [3] using the response surface methodology to study the effect of the overhang, feed rate, and the depth of cut on machining errors in boring for AISI4140 steel.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics

Sj¹,

Tsai²

2017

Preprint

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The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for AA7050-T7451 by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of cutting variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is proved that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were performed to make a comparison between the experimental results. Experimental results have shown that machining performance in precision turn-boring process can be improved effectively through this approach.

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Section: Introductionmentioning

confidence: 99%

Multi-response Optimization of Surface Roughness Roundness and MRR in Precision Turn-Boring of 15-5PH Stainless Steel Using Taguchi-Grey Approach

Hwang¹,

Tsai²

2017

Preprint

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Abstract:The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for 15-5PH Stainless steel by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of machining variables such as concentration of cutting fluid , temperature of cutting fluid , feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error and material removal rate, the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is shown that the most significant factor is cutting speed, followed by feed rate, concentration of cutting fluid, radial depth of cut and temperature of cutting fluid. Finally, confirmation tests were carried out to make a comparison between the experimental results and developed model.Experimental results have shown that machining performance in the turn-boring process can be improved effectively through this approach.

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Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network

Cited by 121 publications

References 19 publications

Experimental studies of machining parameters on surface roughness, flank wear, cutting forces and work piece vibration in boring of AISI 4340 steels: modelling and optimization approach

Experimental studies of machining parameters on surface roughness, flank wear, cutting forces and work piece vibration in boring of AISI 4340 steels: modelling and optimization approach

Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics

Multi-response Optimization of Surface Roughness Roundness and MRR in Precision Turn-Boring of 15-5PH Stainless Steel Using Taguchi-Grey Approach

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