Optimization of CNC isoscallop free form surface machining using a genetic algorithm

Agrawal, Rajneesh Kumar; Pratihar, Dilip Kumar; Choudhury, Asimava Roy

doi:10.1016/j.ijmachtools.2005.07.028

Cited by 52 publications

(25 citation statements)

References 21 publications

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“…5,6 A lot of research work is focused on cutting parameter optimization, which used Taguchi method, response surface methodology, neural network, genetic algorithm (GA), and so on. [6][7][8][9][10][11][12][13] Cutting performance is closely related with the static and dynamic characteristics of CNC machine tool. The levels of requirements for these characteristics are higher along with the increment of cutting performance.…”

Section: -4mentioning

confidence: 99%

An integrated multidisciplinary design optimization method for computer numerical control machine tool development

Zhang

Song

Liu

et al. 2015

Advances in Mechanical Engineering

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Computer numerical control machine tool is a typical complex product related with multidisciplinary fields, complex structure, and high-performance requirements. It is difficult to identify the overall optimal solution of the machine tool structure for their multiple objectives. A new integrated multidisciplinary design optimization method is then proposed by using a Latin hypercube sampling, a Kriging approximate model, and a multi-objective genetic algorithm. Design space and parametric model are built by choosing appropriate design variables and their value ranges. Samples in design space are generated by optimal Latin hypercube method, and design variable contributions for design performance are discussed for aiding the designer's judgments. The Kriging model is built by using polynomial approximation according to the response outputs of these samples. The multidisciplinary design model is established based on three optimization objectives, that is, setting mass, optimum deformation, and first-order natural frequency, and two constraints, that is, second-order natural frequency and third-order natural frequency. The optimal solution is identified by using a multi-objective genetic algorithm. The proposed method is applied in a multidisciplinary optimization case study for a typical computer numerical control machine tool. In the optimal solution, the mass decreases by 3.35% and the first-order natural frequency increases by 4.34% in contrast to the original solution.

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Section: -4mentioning

confidence: 99%

An integrated multidisciplinary design optimization method for computer numerical control machine tool development

Zhang

Song

Liu

et al. 2015

Advances in Mechanical Engineering

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“…It is important to note that, GA and PSO determines optimal process variable combinations for the extreme values of the responses more quickly through the competitive solutions among the potential populations. In recent past, PSO and GA has been implemented successfully for multi-objective optimization of different manufacturing related problems, such as surface grinding [32], squeeze casting [22], electro chemical machining [33], turning [34] abrasive flow machining [35], surface machining [36] etc. The parameters of evolutionary algorithms are to be modified suitably to successfully handle the multiple objective functions, which are conflict in nature [37].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

Patel

Krishna

Vundavilli

et al. 2016

Archives of Foundry Engineering

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The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO) and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.

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“…Agrawal et al [13] used a genetic algorithm (GA) to minimise machining distance in iso-scallop machining of parametric surfaces. They were using the GA to find the globally optimal master cutting path from which the rest of the machining passes were derived.…”

Section: Introductionmentioning

confidence: 99%

“…Common Lisp function for evaluating the fitness of an individual 1 ;; Fitness evaluation for individual programs 2 (defun evaluate-standard-fitness (program) 3 "Evaluates a single program (argument) and reports its 4 fitness, hits, and number of moves." (squares-to-hit *number-of-squares-to-cut*)) 10 (initialise) 11 (catch :terminate-fitness-evaluation 12 (dotimes (index *maximum-number-of-moves*) 13 (when (or (>= hits squares-to-hit) 14 (>= moves-tally *maximum-number-of-moves*)) …”

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confidence: 99%

Generating Milling Tool Paths for Prismatic Parts Using Genetic Programming

2015

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The automatic generation of milling tool paths traditionally relies on applying complex tool path generation algorithms to a geometric model of the desired part. For parts with unusual geometries or intricate intersections between sculpted surfaces, manual intervention is often required when normal tool path generation methods fail to produce efficient tool paths. In this paper, a simplified model of the machining process is used to create a domain-specific language that enables tool paths to be generated and optimised through an evolutionary process -formulated, in this case, as a genetic programming system. The driving force behind the optimisation is a fitness function that promotes tool paths whose result matches the desired part geometry and favours those that reach their goal in fewer steps. Consequently, the system is not reliant on tool path generation algorithms, but instead requires a description of the desired characteristics of a good solution, which can then be used to measure and evaluate the relative performance of the candidate solutions that are generated. The performance of the system is less sensitive to different geometries of the desired part and doesn't require any additional rules to deal with changes to the initial stock (e.g. when rest roughing). The method is initially demonstrated on a number of simple test components and the genetic programming process is shown to positively influence the outcome. Further tests and extensions to the work are presented.

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Optimization of CNC isoscallop free form surface machining using a genetic algorithm

Cited by 52 publications

References 21 publications

An integrated multidisciplinary design optimization method for computer numerical control machine tool development

An integrated multidisciplinary design optimization method for computer numerical control machine tool development

Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

Generating Milling Tool Paths for Prismatic Parts Using Genetic Programming

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