Performance-Based Predictive Models and Optimization Methods for Turning Operations and Applications: Part 3—Optimum Cutting Conditions and Selection of Cutting Tools

Wang, X.; Da, Zhou; Balaji, A.K.; Jawahir, I.S.

doi:10.1016/s1526-6125(07)70108-1

Cited by 25 publications

(10 citation statements)

References 22 publications

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“…Early work on optimization of machining operations shows the economic benefits of optimized machining. Recent research highlights the use of advanced computer-aided, fuzzy logic and genetic algorithms (GA) applications for optimization of different machining operations [26,27].…”

Section: Hybrid Predictive Modeling and Optimizationmentioning

confidence: 99%

Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels

Jayal

Badurdeen

Dillon

et al. 2010

CIRP Journal of Manufacturing Science and Technology

Self Cite

790

328

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Section: Hybrid Predictive Modeling and Optimizationmentioning

confidence: 99%

Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels

Jayal

Badurdeen

Dillon

et al. 2010

CIRP Journal of Manufacturing Science and Technology

Self Cite

790

328

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“…According to the [6], roughness is being set as one of the constraints determining cutting conditions and machining system characterisation. Based on roughness data, Wang et al [7] developed methodology for selection of tool inserts for turning operations and database was established by measuring Ra for a set of different cutting conditions. Rogante [8] investigated performance of both uncoated and coated tool inserts within tool condition monitoring.…”

Section: Review Of Relevant Resultsmentioning

confidence: 99%

Roughness measurement methodology for selection of tool inserts

et al. 2013

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“…Many researchers have studied the micro-cutting operations experimentally and theoretically in order to assess the process behavior [1][2][3][4][5][6][7][8][9][10][11]. The goal of these studies was to identify and control the dominating parameters for the best possible performance of the process.…”

Section: Related Workmentioning

confidence: 99%

Simulation and Optimization Study of Ultra-Precision Turning Process

Elsanabary

Elkaseer

Abdrabbo

et al. 2016

Port-Said Engineering Research Journal

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This paper presents a simulation and optimization study of the surface generation process in ultra-precision turning using a formerly proposed model by the authors in preceding work. However, the surface generation model was improved by incorporating the effect of cutting speed into the model considerations. Then, a series of simulation trials were undertaken to predict the generated surface roughness over a wide range of cutting conditions. Particularly, two different dual-phase materials, Brass 6040 and medium carbon steel AISI 1045 were examined under ranges of cutting speeds, feed rates in addition to varying geometries of cutting tools. The study was accomplished by conducting an optimization of the process aiming at identifying the optimal cutting conditions for the best possible performance of the process in terms of minimum roughness average (Ra). In particular, two optimization techniques, Sequential Quadratic Programing (SQP) and Genetic Algorithm (GA), were utilized to tackle the enhanced model. The results demonstrated that although cutting speed influenced the process, cutting edge radius was the dominate parameter and feed rate was found to be the second most effective variable. Also, optimization results confirmed the superiority of the GA technique over SQP method to obtain the optimal cutting conditions for minimum roughness.

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Performance-Based Predictive Models and Optimization Methods for Turning Operations and Applications: Part 3—Optimum Cutting Conditions and Selection of Cutting Tools

Cited by 25 publications

References 22 publications

Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels

Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels

Roughness measurement methodology for selection of tool inserts

Simulation and Optimization Study of Ultra-Precision Turning Process

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