Development of a framework to automate process planning functions and to determine machining parameters

Narang, Ramesh V.; Fischer, Glenn A.

doi:10.1080/00207549308956832

Cited by 29 publications

(13 citation statements)

References 17 publications

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“…The weight of a YT15 tool insert was 13 g and the nose radius was 0.8 mm. Narang and Fischer (1993) proved that feed rate and nose radius had the most influential effects on surface roughness, and the relationship between them can be expressed as follows: where R a and r ε are the surface roughness and nose radius, respectively. Let R max denote the maximum allowable surface roughness (Table 3).…”

Section: Regression Modellingmentioning

confidence: 99%

Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

Lin

Wang

et al. 2014

Engineering Optimization

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2014): Multi-objective teaching-learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations, Engineering Optimization, In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts.All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching-learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method.

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Section: Regression Modellingmentioning

confidence: 99%

Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

Lin

Wang

et al. 2014

Engineering Optimization

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“…The stable cutting region constraint of (11) and the surface finish constraint of (17) follow the formulation of Narang and Fischer (1993) [27]:…”

Section: Objective Functionmentioning

confidence: 99%

Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

Jabri

Barkany

Khalfi

2017

Modelling and Simulation in Engineering

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For years, there has been increasing attention placed on the metal removal processes such as turning and milling operations; researchers from different areas focused on cutting conditions optimization. Cutting conditions optimization is a crucial step in Computer Aided Process Planning (CAPP); it aims to select optimal cutting parameters (such as cutting speed, feed rate, depth of cut, and number of passes) since these parameters affect production cost as well as production deadline. This paper deals with multipass turning operation optimization using a proposed Hybrid Genetic Simulated Annealing Algorithm (HSAGA). The SA-based local search is properly embedded into a GA search mechanism in order to move the GA away from being closed within local optima. The unit production cost is considered in this work as objective function to minimize under different practical and operational constraints. Taguchi method is then used to calibrate the parameters of proposed optimization approach. Finally, different results obtained by various optimization algorithms are compared to the obtained solution and the proposed hybrid evolutionary technique optimization has proved its effectiveness over other algorithms.

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“…Other limitations that will be taken into account are: surface finish constraint [31] and chip-tool interface temperature constraint, [32].…”

Section: Machining Constraintsmentioning

confidence: 99%

Multi-Objective Optimization Using Genetic Algorithms of Multi-Pass Turning Process

Jabri¹,

Barkany²,

Khalfi³

2013

ENG

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In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is cutting cost and the second one is the used tool life time. The proposed model deals multi-pass turning processes where the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic Algorithms method are presented in Pareto frontier graphic; this technique helps us in decision making process. An example is presented to illustrate the procedure of this technique.

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Development of a framework to automate process planning functions and to determine machining parameters

Cited by 29 publications

References 17 publications

Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

Multi-Objective Optimization Using Genetic Algorithms of Multi-Pass Turning Process

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