303 research outputs found

    Introductory Chapter: Innovations in Orthodontics

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    Miniscrew Applications in Orthodontics

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    Modeling of grinding process mechanics

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    Grinding process is one of the most common methods to manufacture parts that require precision ground surfaces, either to a critical size or for the surface finish. In abrasive machining, abrasive tool consists of randomly oriented, positioned and shaped abrasive grits which act as cutting edges and remove material from the workpiece individually to produce the final workpiece surface. Hence it is almost impossible to achieve optimum process parameters and a repeatable process by experience or practical knowledge. In order to overcome these issues and predict the outcomes of the operation beforehand, modeling of the process is crucial. The main aim of this thesis is to develop semi-analytical or analytical models in order to represent the true mechanics and thermal behavior of metals during abrasive machining processes, especially grinding operations. Abrasive wheel surface topography identification, surface roughness, thermomechanical and semi-analytical force models and two dimensional moving heat source temperature model are proposed. These models are used to simulate the grinding process accurately. The proposed models are more sophisticated than previous ones as they require less calibration experiments and cover wider range of possible cutting conditions. Once the wheel topography and abrasive grit properties are identified, uncut chip thickness per grain and final workpiece surface profile can be predicted. A novel thermo-mechanical model at primary shear zone with sticking and sliding contact zones on the rake face of the abrasive grit was established to predict cutting forces by assuming each of the abrasive grit similar to a micro milling tool tooth. Knowing the force and total process energy, by using two dimensional moving heat source theory, process temperatures are predicted. Moreover, an initial approach and experimental results are proposed in order to investigate and model dynamics and stability dynamics of the grinding process. All proposed models are verified by experiments and overall good agreement is observed

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

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    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 Extrusion Process via Grey Wolf Optimizer Algorithm and Regression Analysis

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    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

    Get PDF
    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

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

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

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    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

    Get PDF
    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
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