The high-temperature polymers like Acetal homopolymer (Delrin) currently have a wide variety of use. They are quite often utilized in traditional components to reduce weight, cost or meet a specific application requirement, and so on. Some of preferred uses of such polymers include aircraft interiors, wire insulation, wire couplings and fixtures, and so on, particularly at high-temperature applications. The machining process like drilling may affect the near net shape of the final product. This experimental study is done through modeling and optimization for identifying the suitable tool and optimum parameters for drilling of Delrin polymer under dry conditions to achieve high surface finish. The three levels of parameters such as spindle speed ( N), feed rate ( f), and tool point angle ( Θ) are taken as control parameters of the response variable. Two different commercially available tool materials namely high-speed steel drill tool and solid carbide tool are accounted in experiments. L27 orthogonal array is initially taken for the experimentation in CNC turning center with horizontal drilling setup. Artificial neural network is employed to sample, train, and test the input parameters in order to lessen the experimental error and measurement error of response variables. Response surface models are developed and optimal parameters toward the surface quality of the hole are determined through the desirability function approach. It is found that the surface generated under dry mode with speed of 1026 r/min, feed of 0.1 mm/min, point angle of 118° recorded the surface roughness of 0.699 µm, which is considered to be the best for drilling Delrin material.
Magnesium alloys are advanced, light materials used widely in industries and milling is one of the material removal processes that are extensively used. In this present study, the experimental work has been carried out based on a Box-Behnken design by mainly considering three factors, i.e., cutting speed, feed, and depth of cut. The first part of in this study, the effects of Response Surface methodology (RSM) and Artificial Neural Network (ANN) models were evaluated and compared. The RSM and ANN models provide the average error of 2.40 % and 1.52 %, respectively, it recommends that ANN is a more efficient methodology for the prediction of the optimal output response than RSM. The predicted model has been coupled with evolutionary optimization technique genetic algorithm (GA) to determine the optimum cutting parameters to attain the minimal surface roughness with respect to the wide ranges of machining parameters and GA suggest the surface roughness of 1.0926 μm with the optimized machining parameters. The evaluation of these observations proves that the proposed methods are capable of determining the optimum machining parameters for modern materials. Keywords: artificial neural network, genetic algorithm, magnesium alloy, milling, optimization Zlitine na osnovi magnezija (Mg) so napreden lahek material, ki se pogosto uporablja v razli~nih vejah industrije. Njegova mehanska obdelava z rezkanjem je ena od najpogostej{ih metod odstranjevanja materiala za dokon~no oblikovanje razli~nih industrijskih izdelkov. V predstavljeni {tudiji avtorji opisujejo eksperimentalno delo izvedeno na osnovi Box-Behnkenovega dizajna z upo{tevanjem treh faktorjev: rezalne hitrosti, pomika in globine reza. V prvem delu te {tudije so avtorji ovrednotili in primerjali u~inke dveh uporabljenih modelov: metodologije reakcije povr{ine (RSM; angl.: Response Surface Methodology) in umetne nevronske mre`e (ANN, angl: Artificial Neural Network). RSM in ANN modela sta predvidela povpre~no 2,40 % oz. 1,52 % napako. To pomeni, da je ANN u~inkovitej{a methodologija za napoved optimalne (reakcije) storilnosti. Model za prognozo so avtorji zdru`ili z evolucijsko optimizacijsko tehniko genetskih algoritmov (GA) in na ta na~in dolo~ili optimalne rezalne parametre za dosego minimalne povr{inske hrapavosti v obmo~ju dokaj {iroko izbranih parametrov mehanske obdelave. GA napoveduje povr{insko hrapavost 1.0926 μm pri optimiziranih parametrih mehanske obdelave. Ovrednotenje teh opazovanj je potrdilo da, je s predlaganimi metodami mo`no dolo~iti optimalne parametre mehanske obdelave modernih materialov. Klju~ne besede: umetna nevronska mre`a, genetski algoritem, zlitina na osnovi Mg, rezkanje, optimizacija
Teaching learning-based optimization (TLBO) is a popular algorithm used to solve various optimization problems. Nevertheless, conventional TLBO and some improved variants tends to suffer with premature convergence due to rapid loss of population diversity, especially when handling the challenging optimization problems. Furthermore, it is not practical to tackle real-world multiobjective problems using prior approach given the frequent changes of customers' requirements. Motivated by these challenges, an improved variant known as Modified Multi-objective Teaching Learning Based Optimization-Refined Learning Scheme (MMTLBO-RLS) was proposed as a posterior approach to solve challenging multiobjective optimization problems, including the prediction of optimum turning parameters to machine Polyether ether ketone material (PEEK). Substantial modifications were introduced for teacher and learner phases of MMTLBO-RLS to achieve better balancing of exploration and exploitation searches without incurring excessive computational cost. For modified teacher phase of MMTLBO-RLS, each learner was guided by a unique teacher solution and unique mean position to perform searching with better diversity. Meanwhile, two new learning strategies are incorporated into the modified learner phase of MMTLBO-RLS, enabling all learners to enhance their knowledge more efficiently based on their learning preferences. A systematic approach was followed to develop modelling equations required for optimization. The developed algorithm was then employed in single objective optimization as well as multiobjective optimization to cater its performances in any real-world environment. The prediction model reports that surface roughness of 1.1042μm and material removal rate of 22.8991 cm 3 /minute can be achieved. The predicted results differ from validation results by less than 2.69% in any case of optimization. A benchmarking on the performance of MMTLBO-RLS in solving CEC 2009 multiobjective benchmark functions was further carried out with other seven meta-heuristic algorithms. The superior performance of MMTLBO-RLS proves that it is not only suitable to be used in industries to produce the parts of PEEK with supportive quality and quantity, but it is also able to solve other multiobjective optimization problems with competitive performances.
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