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optimization of process parameters for the preparation of continuous glass fiber-reinforced Nylon 6 composites by vacuum bagging process

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JOURNAL OF APPLIED POLYMER SCIENCE 2022年第41期139卷 e53005-e53005页

作者： Dong, Xianwen Chen, Ke Xue, Ping Cui, Yonghui Jia, Mingyin Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing 100029 Peoples R China

Vacuum bagging (VB) technology has become an important method for low-cost preparation of advanced composites materials. In this work, continuous glass fiber-reinforced polyamide-6 (GF/PA-6) composites were successfully manufactured bt VB process, utilizing the anionic polymerization of caprolactam (a monomer of PA-6). The effects of vacuum pressure, curing temperature, curing time on the monomer conversion, crystallinity, and mechanical properties of the composites were investigated by the self-made experimental platform. The results indicated that the number of fiber layers affected the properties of products by affecting the thickness of composites. Vacuum pressure mainly affected the impregnation process of resin on fiber, and the product performance was the best at 1 bar. The flexural strength and flexural modulus of the composites reached the highest when the curing temperature was 150 degrees C and the curing time was 90 min, which were 273.92 MPa and 14.04 GPa respectively, while the interlaminar shear strength (ILSS) was highest at 170 degrees C, up to 42.73 MPa.

关键词： mechanical properties optimization of process parameters thermoplastic composites vacuum bagging

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Multi-objective optimization of process parameters for ultra-narrow gap welding based on Universal Kriging and NSGA Ⅱ

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China Welding 2023年第3期32卷 28-35页

作者：马生明张爱华顾建军漆宇晟马晶王平 College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Department of Electrical and Computer Engineering Dalhousie UniversityHalifaxNSB3J 2X4 Canada

The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base *** welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(Non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process.

关键词： ultra-narrow gap optimization of process parameters non-dominated sorting genetic algorithm II the sidewall fusion depth

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Intelligent optimization of process parameters to corn straw pretreatment 34

Intelligent optimization of process parameters to corn straw...

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34th Chinese Control and Decision Conference (CCDC)

作者： Guan, Yalin Chen, Juan Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781665478960

For the optimization of pretreatment process parameters of com straw, an intelligent optimization method is used, which combines the prediction model and the optimization algorithm. The support vector machine (SVM) prediction model for biogas production is constructed. And the optimal process parameters are obtained by grasshopper optimization algorithm (GOA) on the prediction model. Due to the difficulty of obtaining experimental data, the problem of small samples during the model training exists. So, the virtual samples are generated by uniform interpolation combined with GOA to improve the model prediction accuracy. Through the experiment of dual-frequency ultrasound combined with alkali pretreatment of corn stover fermentation, the effectiveness of virtual sample generation and the feasibility of intelligent process parameter search are verified.

关键词： Corn Straw optimization of process parameters Virtual Sample Grasshopper optimization Algorithm

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Prediction of Surface Roughness and optimization of process parameters for Slow Tool Servo Turning

MANUFACTURING TECHNOLOGY

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MANUFACTURING TECHNOLOGY 2021年第5期21卷 616-626页

作者： Guo, Hangyan Kang, Min Zhou, Wei Nanjing Agr Univ Coll Engn 40 Dianjiangtai Rd Nanjing 210031 Peoples R China

Surface roughness is an important index to evaluate the quality of a machined surface. In order to accurately predict the surface roughness for slow tool servo (STS) turning, taking toric surface as an example, response surface methodology (RSM) was used to perform the process test. The second-order response surface prediction model was established and the variance analysis and reliability test were carried out. The results showed that the average prediction error was 7.6%. In order to obtain the best process parameters, standard particle swarm optimization (PSO) was used. The results showed that the global optimization ability of standard PSO was poor. In order to solve the problem, compression factor was introduced and particle swarm optimization with compression factor (WCF-PSO) was constructed, which enhanced the convergence of PSO effectively. WCF-PSO was used to optimize the process parameters and the results obtained were R-t=0.87mm, a(f)=0.01mm/r, a(p)=0.05mm, Delta theta=8.70 degrees, with a corresponding surface roughness of Ra=0.0486 mu m. The results of the verification test showed that the actual value was Ra=0.0520 mu m, and the error was only 7.0%, indicating that WCF-PSO had a better optimization effect.

关键词： Slow tool servo Response surface methodology Prediction model Particle swarm optimization optimization of process parameters

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Application of Machine Learning for optimization of HVOF process parameters

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JOURNAL OF THERMAL SPRAY TECHNOLOGY 2024年第2-3期33卷 504-514页

作者： Gerner, Daniel Azarmi, Fardad Mcdonnell, Martin Okeke, Uchechi North Dakota State Univ Dept Mech Engn Fargo ND 58108 USA DEVCOM Ground Vehicle Syst Ctr Warren MI 48397 USA

A variety of process parameters affect the properties of the deposited coatings in the High Velocity Oxygen Fuel (HVOF) spraying process. In fact, the quality of coatings can be improved without changing feedstock or deposition technology by the application of optimized spraying process parameters. In this study, a large set of data "Big Data" is used to create a variety of machine learning models for prediction of porosity content and hardness values of HVOF deposited coatings. A set of process parameters was selected as validation run, and actual HVOF coating was deposited using those parameters. The porosity level and hardness were measured and compared to those predicted by models. The models differ based on the number of neurons utilized in each layer for the calculations. A model with six neurons could predict closest porosity level and the one with three was the best in prediction of hardness. A final model could be obtained by running data through both models. Through this study, a robust machine learning model for the optimization of HVOF process parameters will be developed that could be used for other coatings and thermal spraying techniques.

关键词： HVOF machine learning optimization of process parameters tungsten carbide wear resistant coatings

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Intelligent optimization of process parameters to corn straw pretreatment

Intelligent optimization of process parameters to corn straw...

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第34届中国控制与决策会议

作者： Yalin Guan Juan Chen College of Information Science and Technology Beijing University of Chemical Technology

For the optimization of pretreatment process parameters of corn straw,an intelligent optimization method is used,which combines the prediction model and the optimization *** support vector machine(SVM)prediction model for biogas production is *** the optimal process parameters are obtained by grasshopper optimization algorithm(GOA) on the prediction *** to the difficulty of obtaining experimental data,the problem of small samples during the model training ***,the virtual samples are generated by uniform interpolation combined with GOA to improve the model prediction *** the experiment of dual-frequency ultrasound combined with alkali pretreatment of corn stover fermentation,the effectiveness of virtual sample generation and the feasibility of intelligent process parameter search are verified.

关键词： Corn Straw optimization of process parameters Virtual Sample Grasshopper optimization Algorithm

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Multi-objective optimization of laser welding process parameters of steel/Al based on BO-RF and MOJS

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第2期38卷 861-871页

作者： Zhang, Xinran Guo, Yonghuan Fan, Xiying Zhao, Jinyue Yan, Bo Zhang, Lanfeng Jiangsu Normal Univ Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China

The welding of steel/aluminum has great significance for vehicle light weight. The quality of the welding joint largely depends on the selection of process parameters. In this paper, the random forest (RF) model was established to describe the nonlinear relationship between welding process parameters and optimization objectives. We used Bayesian optimization (BO) to adjust the hyperparameters of the RF model, and the BO-RF model as the objective function of the multi-objective jellyfish search algorithm to find the optimal welding process parameters and verify them through simulation and practical experiments. The results show that due to the appropriate selection of process parameters, the maximum tensile force of the welded joints was increased by 64 %, the maximum deformation was reduced by 32 %, and the welding cost was reduced by 12 %. What is more, the depth-to-width ratio of steel/aluminum welded joints was increased by 60.7 %, the thickness of brittle intermetallic compounds layer was reduced by about 47.2 % while the tensile fracture surface changed from cleavage fracture to quasi-cleavage fracture.

关键词： BO-RF model MOJS Laser welding optimization of process parameters Dissimilar metal welding

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Multi-objective optimization and Decision-Making Method for Laser Polishing process parameters of D2 Die Steel Based on NSGA-II and TOPSIS-EWM

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JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE 2024年 1-15页

作者： Liang, Qiang Xu, Yonghang Li, Ping Xu, Binyuan Chongqing Technol & Business Univ Coll Mech Engn Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Chongqing Key Lab Green Design & Mfg Intelligent E Chongqing 400067 Peoples R China

To enhance the surface quality of D2 die steel after laser polishing, a multi-objective optimization and decision-making method for laser polishing process parameters was proposed. Three crucial process variables were laser power, scanning speed, and overlap rate. Three optimization targets were surface roughness, microhardness, and polishing depth. To establish a model with good prediction to describe the relationship between three process parameters and optimization targets, the prediction accuracies of four methods were compared. Among them, the response surface model (RSM) displayed the best prediction accuracy and was adopted as the optimized predictive model. Then, to obtain the optimal combination of process parameters, non-dominated sorting genetic algorithm II (NSGA-II) has been combined with the entropy-weight method technique for order preference by similarity to an ideal solution (EWM-TOPSIS). This approach aims to retain the maximum microhardness achievable while reducing the surface roughness. Following the optimization process, the optimal process parameter combinations were determined, represented as follows: laser power of 551 W, scanning speed of 9.0 mm s-1, and overlap rate of 0.43. The optimized surface roughness is Ra 1.351 mu m, which is 73.96% lower than the original surface roughness. The microhardness reached 512.1 HV0.5, which is 5.39% lower than the original microhardness. In addition, the polishing depth reached 0.305 mm.

关键词： decision-making D2 die steel laser polishing optimization of process parameters

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optimization of investment casting process parameters to reduce warpage of turbine blade platform in DD6 alloy

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China Foundry 2017年第6期14卷 469-477页

作者： Jia-wei Tian Kun Bu Jin-hui Song Guo-liang Tian Fei Qiu Dan-qing Zhao Zong-li Jin Yang Li Key Laboratory of Contemporary Design and Integrated Manufacturing Technology Northwestern Polytechnical University Xi 'an 710072 Shannxi China

The large warping deformation at platform of turbine blade directly affects the forming precision. In the present research, equivalent warping deformation was firstly presented to describe the extent of deformation at platform. To optimize the process parameters during investment casting to minimize the warping deformation of the platform, based on simulation with Pro CAST, the single factor method, orthogonal test, neural network and genetic algorithm were subsequently used to analyze the influence of pouring temperature, shell mold preheating temperature, furnace temperature and withdrawal velocity on dimensional accuracy of the platform of superalloyDD6 turbine blade. The accuracy of investment casting simulation was verified by measurement of platform at blade casting. The simulation results with the optimal process parameters illustrate that the equivalent warping deformation was dramatically reduced by 21.8% from 0.232295 mm to 0.181698 mm.

关键词： ProCAST optimization of process parameters warping deformation of platform orthogonal test genetic algorithm BP-neural network

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Efficient optimization of Magnetic Properties of Fe-4.5Si Alloy in Laser Powder Bed Fusion (LPBF)

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2025年 1-14页

作者： Song, Byeong Uk Choi, Joon Phil Lee, Pil-Ho Jung, Min-Kyo Shin, Dongwoon Ha, Taeho Lee, Ikjin Korea Adv Inst Sci & Technol Mech Engn Dept 291 Daehak Ro Daejeon 34141 South Korea Korea Inst Machinery & Mat Dept 3D Printing 156 Gajeongbuk Ro Daejeon 34103 South Korea

Existing studies have successfully identified optimal process parameters to enhance relative density and mechanical properties. However, these optimizations often incur high experimental costs due to the need for repetitive experiments. This research introduces an efficient optimization framework using two design of experiment (DoE) methods and machine learning algorithms, aimed at optimizing the magnetic properties of Fe-4.5Si material. The framework consists of three steps: (1) identification of the process parameter region that ensures high relative density using cubic specimens, (2) development of surrogate models to predict magnetic properties, such as maximum relative permeability and core loss, using toroidal specimens, and (3) multi-objective optimization using the surrogate models and a multi-objective genetic algorithm (MOGA) to enhance magnetic properties. The results demonstrate that this method can efficiently optimize process parameters with a limited number of experiments. Furthermore, the versatility of this framework allows its application to other materials in the early stages of research, even without prior knowledge of specific materials.

关键词： Laser powder bed fusion (LPBF) Selective laser melting (SLM) process modeling Fe-Si alloy optimization of process parameters

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