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process parameters optimization in plastic injection molding using metamodel-based optimization: a comprehensive review

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第11-12期121卷 7117-7145页

作者： Kitayama, Satoshi Kanazawa Univ Kakuma machi Kanazawa Ishikawa 9201192 Japan

This paper comprehensively reviews the process parameters optimization in plastic injection molding (PIM) using the metamodel-based optimization. The PIM is a typical manufacturing technology to produce lightweight and high gloss appearance plastic products. To produce the plastic product, the process parameters such as melt temperature, injection time, packing pressure, packing time, cooling temperature, and cooling time are conventionally adjusted through the trial-and-error method. Computer-aided engineering (CAE) is one of the alternatives in the PIM but the numerical simulation is computationally so expensive that the metamodel-based optimization is widely used to determine the optimal process parameters. First, typical metamodels including the sampling strategy are briefly reviewed. Then, the papers on the process parameters optimization for high product quality such as warpage, shrinkage, and weldline reduction are reviewed. Rapid heat cycle molding (RHCM) that actively controls the mold temperature is a novel PIM technology, but the process parameters optimization in RHCM is rarely discussed in the literature. Then, the RHCM is also reviewed in this paper. Not only the process parameters optimization but also the cooling channel plays an important role for high product quality and high productivity. Due to the recent advancement of metal 3D printer, it is possible to produce conformal cooling channel. It is possible to enhance the product quality and productivity by using the conformal cooling channel. Several papers on the process parameters using the conformal cooling channel are also reviewed.

关键词： Plastic injection molding process parameters optimization Metamodel-based optimization Numerical simulation

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process parameters optimization of Plasma Spraying Nanostructured Coating Based on Particle Swarm optimization Algorithm

Process Parameters Optimization of Plasma Spraying Nanostruc...

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International Conference on Application of Materials Science and Environmental Materials (AMSEM)

作者： Yang, Bin Tianjin Univ Technol & Educ Sch Automobile & Transportat Tianjin 300222 Peoples R China

ISBN: (纸本)9783038352709

process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating were optimized based on particle swarm optimization (PSO) algorithm. BP neural network was applied to compute fitness of PSO algorithm. A BP neural network model was built. process parameters of coating were optimized based on PSO algorithm. The results shown that maximal bonding strength was 33.08MPa. process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating were obtained. The results were superior to design of orthogonal optimization. It provided definite reference for selecting the best process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating.

关键词： Plasma Spraying Nanostructured Coating BP Neural Network Particle Swarm optimization Algorithm process parameters optimization

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Hot deformation behavior and process parameters optimization of Ti-6Al-7Nb alloy using constitutive modeling and 3D processing map

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Journal of Iron and Steel Research International 2021年第7期28卷 862-873页

作者： Ming-jun Zhong Ke-lu Wang Shi-qiang Lu Xin Li Xuan Zhou Rui Feng School of Aeronautical Manufacturing Engineering Nanchang Hangkong UniversityNanchang 330063JiangxiChina

The isothermal compression test for Ti-6Al-7Nb alloy was conducted by using Gleeble-3800 thermal *** hot deformation behavior of Ti-6Al-7Nb alloy was investigated in the deformation temperature ranges of 940-1030℃and the strain rate ranges of 0.001-10 s^(-1).Meanwhile,the activation energy of thermal deformation was *** results show that the flow stress of Ti-6Al-7Nb alloy increases with increasing the strain rate and decreasing the deformation *** activation energy of thermal deformation for Ti-6Al-7Nb alloy is much greater than that for self-diffusion ofα-Ti andβ-*** the influence of strain on flow stress,the strain-compensated Arrhenius constitutive model of Ti-6Al-7Nb alloy was *** error analysis shows that the model has higher accuracy,and the correlation coefficient r and average absolute relative error are 0.9879 and 4.11%,*** processing map(PM)of Ti-6Al-7Nb alloy was constructed by the dynamic materials model and Prasad instability *** to PM and microstructural observation,it is found that the main form of instability zone is local flow,and the deformation mechanisms of the stable zone are mainly superplasticity and dynamic *** optimal processing parameters of Ti-6Al-7Nb alloy are determined as follows:960-995℃/0.01-0.18 s^(-1)and 1000-1030℃/0.001-0.01 s^(-1).

关键词： Ti-6Al-7Nb alloy Hot deformation behavior Strain-compensated Arrhenius constitutive model processing map process parameters optimization

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Numerical and experimental investigation of process parameters optimization in plastic injection molding using multi-criteria decision making

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SIMULATION MODELLING PRACTICE AND THEORY 2018年 85卷 95-105页

作者： Kitayama, Satoshi Yamazaki, Yusuke Takano, Masahiro Aiba, Shuji Kanazawa Univ Kakuma Machi Kanazawa Ishikawa 9201192 Japan Kanazawa Univ Grad Sch Nat Sci & Technol Kakuma Machi Kanazawa Ishikawa 9201192 Japan Ind Res Inst Ishikawa Kanazawa Kuratsuki 9208203 Japan Sodick Co Ltd Ka-1-1Miya Machi Kaga Ishikawa 9220595 Japan

In plastic injection molding (PIM), the process parameters such as packing pressure, melt temperature, and cooling time should be adjusted and optimized for high product quality and high productivity. Warpage is one of the major defects in the PIM and should be minimized for high quality. Cycle time should be minimized for the high productivity. In addition to the warpage and the cycle time, clamping force affects the product both the quality and the productivity. Therefore, for the high product quality and the high productivity, it is important to minimize the warpage, the cycle time and the clamping force simultaneously. In this paper, a multi-objective optimization of process parameters in PIM for minimizing three objectives is performed. Numerical simulation in the PIM is generally so intensive that a sequential approximate optimization using radial basis function network is adopted to determine the optimal process parameters. The radar chart is used to perform the trade-offanalysis among three objective functions. In addition, the better and worse solution are newly introduced for the trade-offanalysis. It is found through the trade-offanalysis that all objective functions of the conformal cooling channel are well improved in comparison with the ones of the conventional cooling channel. Therefore, 43% warpage reduction, 1,7% clamping force reduction, and 47% cycle time reduction can successfully be achieved using the conformal cooling channel. Based on the numerical result, the experiment using PIM machine (GL30-LP, Sodick) is carried out. The conformal cooling channel is developed by the metal 3D printer (OPM250L, Sodick). Through the numerical and experimental result, the validity of the proposed approach is examined. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Plastic injection molding process parameters optimization Multi-objective optimization Sequential approximate optimization Trade-off analysis

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Numerical investigation and process parameters optimization in three-dimensional multi-stage hot forging for minimizing flash and equivalent strain

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第11-12期126卷 5409-5420页

作者： Kitayama, Satoshi Saito, Kohei Wang, Tao Furuta, Satoshi Aono, Eri Amano, Masaharu Kanazawa Univ Kakuma Machi Kanazawa 9201192 Japan Kanazawa Univ Grad Sch Nat Sci & Technol Kakuma Machi Kanazawa 9201192 Japan Komatsu Ltd 3 1 1Ueno Hirakata Osaka 5731011 Japan

Multi-stage hot forging is widely used to produce complex forged products in inFdustry. Flash after the forging should always be minimized for the material saving, and it is important to determine the preform shape minimizing the flash. The process parameters in multi-stage hot forging such as the billet temperature and the stroke have also an influence on the product quality as well as the flash, but they are still determined by a trial-and-error method. In this paper, multi-objective design optimization in three-dimensional multi-stage hot forging is numerically performed so as to minimize both the flash and the distribution of equivalent strain. The preform shape and the process parameters such as the billet temperature, the die temperature and the stroke are optimized. The three-dimensional numerical simulation is computationally so expensive that sequential approximate optimization that response surface is repeatedly constructed and optimized is adopted to identify the pareto-frontier between the flash and the distribution of equivalent strain. It is found from the numerical result that 60% reduction of flash can be achieved, compared to the conventional preform shape. In addition, the total forging energy considering the multi-stage hot forging is reduced. It is confirmed through the numerical result that the proposed approach is valid to determine the optimal preform shape and process parameters in multi-stage hot forging.

关键词： Multi-stage hot forging Preform optimization process parameters optimization Multi-objective optimization Numerical simulation

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Multi-objective process parameters optimization of SLM using the ensemble of metamodels

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JOURNAL OF MANUFACTURING processES 2021年第PartA期68卷 198-209页

作者： Li, Jingchang Hu, Jiexiang Cao, Longchao Wang, Shengyi Liu, Huaping Zhou, Qi Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mat Sci & Engn Wuhan 430074 Peoples R China

Selective laser melting (SLM) is one of the most common additive manufacturing (AM) technologies that shows great potential for intelligent and clean manufacturing. The process parameters involved in the SLM process have a significant impact on the part quality and energy consumption during the printing process. Since the relationships between the process parameters and various part quality characteristics cannot be expressed explicitly, it is impractical to decide the optimal process parameters intuitively. In this work, a hybrid multi objective optimization approach by combining the ensemble of metamodels (EM) and non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to generate optimal process parameters to improve the energy consumption, the tensile strength, and the surface roughness of the as-built parts. First, the Taguchi experiment design is adopted and the corresponding SLM experiments are conducted to obtain the experimental results. Second, the correlations between the process parameters (i.e., laser power, layer thickness, scanning speed) and the three responses are fitted using the proposed EM. The comparative results show that the prediction ability of the EM outperforms the stand-alone metamodels. Then, the NSGA-II is used to search for multi-objective Pareto optimal solutions based on the constructed EM. Finally, the verification experiments were conducted to verify the optimal results obtained by the proposed multi-objective optimization approach. Results indicate that the optimal process parameters are effective and reliable. Besides, the main effects of process parameters on the responses are analyzed. Overall, the proposed hybrid multi-objective optimization method exhibits great ability to improve the effectiveness of SLM.

关键词： Selective laser melting process parameters optimization Ensemble of metamodels NSGA-II

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Multi-objective process parameters optimization of hot-wire laser welding using ensemble of metamodels and NSGA-II

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2018年 53卷 141-152页

作者： Yang Yang Cao Longchao Wang Chaochao Zhou Qi Jiang Ping Huazhong Agr Univ Coll Engn Wuhan 430074 Hubei Peoples R China Minist Agr Key Lab Agr Equipment Midlower Yangtze River Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Hot-wire laser welding (HLW) can reduce the dispersion of laser energy and improve deposition efficiency by preheating filler wire during laser welding process. In order to obtain sound welding results, it is crucial to select appropriate process parameters. In this study, an optimization methodology based on ensemble metamodels (EM) which take the advantages of the prediction ability of stand-alone metamodels (Kriging, RBF and SVR), and Non-dominated sorting genetic algorithm (NSGA-II) are presented to obtain optimum process parameters during stainless steel 316L hot-wire laser welding. Firstly, EMs are developed through minimizing the generalized mean square Leave-one-out (LOO) errors to find the optimum weight factors of the used stand-alone Kriging, RBF and SVR metamodels. And then the EMs are applied to establish the relationships between process parameters (i,e., laser power (LP), welding speed (WS) and hot-wire current (I)) and welding results (i,e., welding depth-to-width ratio (DW), welding reinforcement (BR) and tensile strength (TS)). During optimization process, NSGA-II is employed to search for multi-objective Pareto optimal solutions based on EMs. In addition, the main effects of multiple process parameters on welding results are analyzed. The verification tests indicate that the obtained optimal process parameters are effective and reliable for producing expected welding results (maximized DW, maximized TS and desired BR value). In general, the proposed optimization method can provide a reliable guidance for HLW in engineering practice.

关键词： Hot-wire Laser welding process parameters optimization Ensemble of metamodels NSGA-II

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A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2009年第9-10期45卷 859-866页

作者： Deng, Z. H. Zhang, X. H. Liu, W. Cao, H. Hunan Univ Natl Engn Res Ctr High Efficiency Grinding Changsha 410082 Hunan Peoples R China Hunan Univ Huda Haijie Mfg Technol Co Ltd Changsha 410082 Hunan Peoples R China

Camshaft grinding is more complex comparing with the ordinary cylindrical grinding. Since its quality is mostly influenced by more factors, how to select process parameters quickly and accurately becomes the key to improve its quality and processing efficiency. In this paper, a hybrid artificial neural network (ANN) and genetic algorithm (GA) model is proposed to optimize the process parameters. In this method, a BP neural network model is developed to map the complex nonlinear relationship between process parameters and processing requirements, and a GA is used in order to improve the accuracy and speed based on the ANN model. The results show that the hybrid ANN/GA model is an effective tool for the process parameters optimization in NC camshaft grinding.

关键词： Genetic algorithm Neural network Camshaft grinding Uniform design process parameters optimization

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process parameters optimization of friction stir processed Al 1050 aluminum alloy by response surface methodology (RSM)

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MATERIALS RESEARCH EXPRESS 2019年第2期6卷

作者： Maboud, Ahmed Abdel Ghaffar Abdel El-Mahallawy, Nahed A. Zoalfakar, Said H. Ain Shams Univ Fac Engn Dept Design & Prod Engn Cairo Egypt HTI Mech Engn Dept 10th Ramadan City Egypt

Aluminum alloys are extensively used in many applications such as aerospace and defense industries because of the high strength-to weight ratio and good ductility. Friction stir processing (FSP) is a technique based on the principle of friction stir welding (FSW) to improve the mechanical properties. This work presents the formulation of a mathematical model with process parameters (rotational speed, feed rate, number of passes and tool shape) to predict the response or the mechanical properties of friction stir processed of 1050 aluminum alloy (yield strength, ultimate tensile strength, percentage elongation and microhardness). A central composite design with four factor, each factor with five levels was used and a response surface methodology was applied to develop the regression models to predict the responses. Method of analysis of variance ANOVA was applied to figureout the significant process parameters that have effect on the responses. These results point out that the friction stir processing of 1050 aluminum alloy with 1500 rpm rotational speed, 116 mm m(-1) feed rate, 3 FSP passes and square tool shape, have the maximum predicted responses using response surface methodology (RSM).

关键词： friction stir processing response surface methodology aluminum alloy process parameters optimization ANOVA mechanical properties

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Grinding process parameters optimization and surface performance analysis of spiral bevel gear based on the orthogonal test

Grinding process parameters optimization and surface perform...

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3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012)

作者： Ming, Xingzu Yan, Hongzhi He, Guoqi Kong, Xianghan Hunnan Ind Univ Zhuzhou City Hunan Peoples R China Cent S Univ Changsha city Hunan Peoples R China

ISBN: (纸本)9783037853726

Based on the experimental conditions of generation grinding of spiral bevel gear, the grinding orthogonal test design of L-16(4(5)) is made, and the results of grinding orthogonal test are obtained. On this basis, the range and variance analyses are carried out by using an integrated balance method, and the optimizing configuration of grinding process parameters of spiral bevel gear is obtained. The test regression models of three grinding evaluating indicators of spiral bevel gear, such as the grinding surface roughness R-a, metamorphic layer depth h and removal rate Z(w), are set up by using a method of multivariate nonlinear least square. The maximal absolute value of relative error between computing values and testing ones is 18.41 percent by test validation, and this shows that the regression models are more effective. Through the test optimization of grinding surface performance, the value of grinding surface roughness R-a is more less 0.1 mu m than one of the normal grinding process, and other comprehensive performances are better. The pre-control measures of grinding process optimization of spiral bevel gear are also put forward. These provide a basis for the improvement of grinding surface quality of spiral bevel gear.

关键词： Grinding Spiral bevel gear Orthogonal test process parameters optimization Surface performance

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