Optimizing Plastic Extrusion Process via Grey Wolf Optimizer Algorithm and Regression Analysis

34-41One of the most widely used methods in the production of plastic products is the extrusion process. There are many factors that affect the product quality throughout the extrusion process. Examining the effects of these factors and determining the optimum process parameters which will provide the desired product characteristics; is important for reducing costs and increasing competitiveness. This study is performed in a manufacturer that produces plastic cups. The aim is to optimize extrusion process parameters of this company in order to achieve 1.15 mm thickness at the produced plastic sheets. For this reason, in order to be able to model the problem as an optimization problem through regression modelling, the thicknesses of the sheet generated with different process parameters were observed during the production processes. Then, considering the desired 1.15 mm sheet thickness, the established model is optimized by running the grey wolf optimizer (GWO) algorithm through the model

Optimizing Plastic Extrusion Process via Grey Wolf Optimizer Algorithm and Regression Analysis

One of the most widely used methods in the production of plastic products is the extrusion process. There are many factors that affect the product quality throughout the extrusion process. Examining the effects of these factors and determining the optimum process parameters which will provide the desired product characteristics; is important for reducing costs and increasing competitiveness. This study is performed in a manufacturer that produces plastic cups. The aim is to optimize extrusion process parameters of this company in order to achieve 1.15 mm thickness at the produced plastic sheets. For this purpose, the thicknesses of the sheet produced with different process parameters were observed throughout the production processes to be able to model the problem as an optimization problem by means of the regression modelling. Then, the developed model is optimized via the grey wolf optimizer (GWO) algorithm considering the desired 1.15 mm sheet thickness

Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Copyright © 2013 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 09 June 2013, available online: http://www.tandfonline.com/10.1080/00207543.2013.784411Genetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I)

Optimizing Plastic Injection Process Using Whale Optimization Algorithm in Automotive Lighting Parts Manufacturing

In this study, using the whale optimization algorithm (WOA), one of the recent optimization algorithms inspired by nature, the plastic injection process parameters of an automotive sub-industry company were tried to be optimized. For this purpose, we tried to provide the maximum weight criterion for the “356 MCA Plastic Housing” (which is an automotive lighting part) produced by plastic injection method. The decrease in the weight of the product indicates that the material injected into the mold is missing and naturally indicates that there will be quality problems. In order to achieve this aim, the best factor levels were tried to be determined for the mold temperature (°C), injection speed (m/s), injection pressure (bar), holding time (s), and injection time (s), which are the controllable parameters of injection process. Factors and factor levels addressed using WOA have not been studied for this type of problem before and this is the novelty aspect of this research. Experiments performed to confirm the findings for optimum process parameters proved that the WOA method can be successfully applied to improve plastic injection process parameters. This study contains information for practicing researchers in terms of showing how the nature-inspired algorithm WOA can be applied in practical field studies

Optimizing Plastic Injection Process Using Whale Optimization Algorithm in Automotive Lighting Parts Manufacturing

360-368In this study, using the whale optimization algorithm (WOA), one of the recent optimization algorithms inspired by nature, the plastic injection process parameters of an automotive sub-industry company were tried to be optimized. For this purpose, we tried to provide the maximum weight criterion for the “356 MCA Plastic Housing” (which is an automotive lighting part) produced by plastic injection method. The decrease in the weight of the product indicates that the material injected into the mold is missing and naturally indicates that there will be quality problems. In order to achieve this aim, the best factor levels were tried to be determined for the mold temperature (°C), injection speed (m/s), injection pressure (bar), holding time (s), and injection time (s), which are the controllable parameters of injection process. Factors and factor levels addressed using WOA have not been studied for this type of problem before and this is the novelty aspect of this research. Experiments performed to confirm the findings for optimum process parameters proved that the WOA method can be successfully applied to improve plastic injection process parameters. This study contains information for practicing researchers in terms of showing how the nature-inspired algorithm WOA can be applied in practical field studies

Design Optimization of 18-Poled High-Speed Permanent Magnet Synchronous Generator

The aim of this research is to optimize the design of an 18-poled 8000 rpm 7 kVA high-speed permanent magnet synchronous generator. The goal is to find the best factor levels for the design parameters, namely magnet thickness (MH), offset, and embrace (EMB) to optimize the responses namely efficiency (%), rated torque (N.m), air-gap flux density (Tesla), armature current density (A/mm2), armature thermal load (A2/mm3). The aim is to keep the air-gap flux density at 1 tesla while maximizing efficiency and minimizing the rest of the responses. Optimization was carried out with one sample algorithm selected from each of the commonly used optimization algorithm classifications. For this purpose, different class of well-known optimization techniques such as response surface methodology (gradient-based methods), genetic algorithm (evolutionary-based algorithms), particle swarm optimization algorithm (swarm-based optimization algorithms), and modified social group optimization algorithm (human-based optimization algorithms) are selected. In the Ansys Maxwell environment, numerical simulations are carried out. Mathematical modeling and optimizations are performed by using Minitab and Matlab, respectively. Confirmations are also performed. Results of the comparisons show that modified social group optimization and particle swarm optimization algorithms a bit outperform the response surface methodology and genetic algorithm, for this design problem

Optimizing of Wear Performance on Elevated Temperature of ZrO2 Reinforced AMCs Using Weighted Superposition Attraction Algorithm

462-474In the current study, the zirconium oxide (ZrO2) reinforced Aluminium Matrix Composites (AMCs) was designed as a brake lining and produced by mechanical alloying (MA) method. Wear tests of AMCs were performed according to ASTM G-99 at different sliding distance, operating temperatures and load in the range of 53 to 94 m, 20 to 340℃ and 10 to 30 Nrespectively. Optimum wear performance parameters were determined using the Weighted Superposition Attraction (WSA)algorithm. Firstly, to formulize the problem as an optimization problem through the guidance of the regression modelling, anexperimental design has been constructed, and the wear tests have been done at different reinforced rates, operatingtemperature and loads. Secondly, WSA algorithm has been adapted to tackle the formulated optimization problem.According to the results of WSA algorithm, the optimum rate of zirkonium oxide (ZrO2), load and operating temperaturewas determined as 12%, 206.33°C and 21.20 N respectively while keeping the friction coefficient between 0.15–0.24

Design Optimization of a 4-Poled 1500 rpm 25 kVA SG to Obtain the Desired Magnetic Flux Density Distributions by using RSM

84-93In this study design optimization for 4-poled 1500 rpm 25 kVA synchronous generator (SG) is performed. The aim is to determine the optimum factor levels for the design parameters namely slot opening width (Bs0), height, and width to keep the responses namely ‘pole-body flux density’ and ‘air-gap flux density’ distributions in a desired range. The target values are determined as 1.75 Tesla and 0.9 Tesla for the ‘pole-body flux density’ and ‘air-gap flux density’ respectively. For this purpose, Response Surface Methodology (RSM) is used for optimization. Numerical simulations are performed in the Maxwell environment and the optimization by RSM is performed by Minitab statistical package. Desired goals were achieved and optimum factor levels were determined with RSM. Then the results of RSM are compared by Genetic Algorithm (GA), Particle Swarm Optimization algorithm (PSO), and Modified Social Group Optimization (MSGO) algorithm. These methods are evaluated together in terms of advantages and disadvantages. The comparisons indicate that using RSM provides acceptable results without performing coding effort and also provides users to understand the relations visually between the factors and the responses by the aid of ‘Minitab Response Optimizer Module’

Design Optimization of a 4-Poled 1500 rpm 25 kVA SG to Obtain the Desired Magnetic Flux Density Distributions by using RSM

In this study design optimization for 4-poled 1500 rpm 25 kVA synchronous generator (SG) is performed. The aim is to determine the optimum factor levels for the design parameters namely slot opening width (Bs0), height, and width to keep the responses namely ‘pole-body flux density’ and ‘air-gap flux density’ distributions in a desired range. The target values are determined as 1.75 Tesla and 0.9 Tesla for the ‘pole-body flux density’ and ‘air-gap flux density’ respectively. For this purpose, Response Surface Methodology (RSM) is used for optimization. Numerical simulations are performed in the Maxwell environment and the optimization by RSM is performed by Minitab statistical package. Desired goals were achieved and optimum factor levels were determined with RSM. Then the results of RSM are compared by Genetic Algorithm (GA), Particle Swarm Optimization algorithm (PSO), and Modified Social Group Optimization (MSGO) algorithm. These methods are evaluated together in terms of advantages and disadvantages. The comparisons indicate that using RSM provides acceptable results without performing coding effort and also provides users to understand the relations visually between the factors and the responses by the aid of ‘Minitab Response Optimizer Module’

A regression control chart for autocorrelated processes

In this study, we present a new regression control chart which is able to detect the mean shift in a production process. This chart is designed for autocorrelated process observations having a linearly increasing trend. Existing approaches may individually cope with autocorrelated and trending data. The proposed chart requires the identification of trend stationary first order autoregressive (trend AR(1)) model as a suitable time series model for process observations. For a wide range of possible shifts and autocorrelation coefficients, performance of the proposed chart is evaluated by simulation experiments. Average correct signal rate and average run length are used as performance criteria. © 2014 Inderscience Enterprises Ltd