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Improving the Mechanical Property of Dissimilar Al/Mg Zn-Added Ultrasound-Assisted Friction Stir Lap Welding Joint by Back Propagation Neural Network-Gray Wolf optimization Algorithm

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ADVANCED ENGINEERING MATERIALS 2019年第12期21卷 1900973-1900973页

作者： Song, Qi Ren, Zhaoxu Ji, Shude Niu, Shiyu Qi, Weiwei Chen, Mingfei Shenyang Aerosp Univ Coll Aerosp Engn Shenyang 110136 Liaoning Peoples R China Liaoning Zhuanglong UAV Technol Co Ltd Zhuanglong Ind Grade Multirotor Uav Profess Techn Shenyang 110136 Liaoning Peoples R China

process parameters of rotating velocity, welding speed, Zn interlayer thickness, and ultrasound power are optimized by the hybrid of back propagation neural network (BPNN) and gray wolf optimization algorithm (GWOA) to obtain a high-quality Zn-added ultrasound-assisted friction stir lap welding joint of 7075-T6 Al/AZ31B Mg dissimilar alloys. The results state that the prediction accuracy of the trained BPNN model is acceptable. The optimal process parameters combination is obtained by the GWOA which is combined with the trained BPNN. The verification tests are performed under the executable optimal solution, which consists of the rotating velocity of 1054 rpm, the welding speed of 54 mm min(-1), the Zn interlayer thickness of 0.05 mm, and the ultrasound power of 1568 W. The tensile shear load of the joint reaches 9.05 kN, and the strength is 11.8% larger than that of the reported optimal joint. The artificial intelligence optimization method of GWOA combined with BPNN can accurately predict and optimize the joint strength, which has great time and economic advantages.

关键词： Al Mg dissimilar alloys back propagation neural network friction stir lap welding gray wolf optimization algorithm process parameters optimization

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Preparation of a fly ash-based porous geopolymer material for copper ion removal

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MATERIALS SCIENCE AND TECHNOLOGY 2024年

作者： Li, Jianmin Li, Ye Guo, Liwen Zhang, Jiayong North China Univ Sci & Technol Coll Min Engn Tangshan Hebei Peoples R China North China Univ Sci & Technol Coll Emergency Management & Safety Engn Tangshan 063210 Hebei Peoples R China Key Lab Min Dev & Safety Technol Hebei Prov Tangshan Peoples R China

The pursuit of novel and efficient adsorbents is vital in potentially toxic elements wastewater treatment. A block-like geopolymer, utilizing 92 wt.% of fly ash, was synthesized to serve as a cost-effective adsorbent for Cu(II). Fly ash-based porous geopolymer (FAPGM) with 80.11% of porosity, 38.080 m2 g-1 of specific surface area and 0.886 MPa of compression strength was obtained through process parameters optimization. FAPGM exhibited excellent adsorption performance, with a maximum capacity of 62.70 mg g-1 for Cu(II). The adsorption process can be described by Langmuir and quasi-second-order kinetic models. The mechanism of Cu(II) adsorption onto FAPGM involves chemisorption, electrostatic interactions, ion exchange chemical precipitation, spontaneous and endothermic. The research indicates blocky porous geopolymer can serve as a promising candidate material for treating copper-containing wastewater.

关键词： adsorption blocky porous geopolymer copper ions fly ash process parameters optimization

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Membrane-based carbon capture for waste-to-energy: process performance, impact, and time-efficient optimization

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ENERGY 2024年 310卷

作者： Pluskal, Jaroslav Zach, Boleslav Kudela, Jakub Somplak, Radovan Syc, Michal Brno Univ Technol VUT Brno Inst Proc Engn Fac Mech Engn Tech 2896-2 Brno 61669 Czech Republic CAS Dept Environm Engn Inst Chem Proc Fundamentals Prague Czech Republic Brno Univ Technol VUT Brno Fac Mech Engn Inst Automat & Comp Sci Tech 2896-2 Brno 61669 Czech Republic

The energy crisis and rising waste production results in the need for more waste-to-energy solutions. However, capturing fossil-based carbon from waste incineration is crucial. The power consumption and overall impact of the carbon capture process are essential for the identification of the most suitable solution. The aim of this paper is to promote a time-efficient optimization, optimize a membrane-based post-combustion carbon capture process, and quantify its impacts on a waste-to-energy plant for various system configurations with different levels of CO2 recovery and purity. The proposed robust evaluation of the system with non-linearities resulted in the utilization of genetic algorithms with subsequent verification. Quantifying power consumption allows the comparison of different carbon capture technologies. The results confirm the importance of process optimization, show the influence of individual parameters, and quantify the disproportionate drop in power consumption with decreasing target CO2 recovery. The power consumption can be as low as 1.14 GJ/tonne of CO2 for CO2 purity of 95 % and recovery of 50 % and 1.66 GJ/tonne of CO2 for CO2 purity of 95 % and recovery of 90 %. The results also suggest that carbon neutrality can be achieved without compromising the R1 efficiency classification of energy recovery.

关键词： Multi-stage membrane separation Carbon dioxide process parameters optimization Genetic algorithms Pareto front Power consumption minimization

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optimization of Laser Based-Powder Bed Fusion parameters for Controlled Porosity in Titanium Alloy Components

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MATERIALS 2024年第22期17卷 5572页

作者： Vaglio, Emanuele Scalzo, Federico Sortino, Marco Totis, Giovanni Cremonese, Roberto Boccia, Massimiliano Danielis, Maila Univ Udine Polytech Dept Engn & Architecture Via Sci 206 I-33100 Udine Italy CTS H2 S r l Viale Lino Zanussi 1 I-33070 Brugnera Italy

Laser based-powder bed fusion (LB-PBF) enables fast, efficient, and cost-effective production of high-performing products. While advanced functionalities are often derived from geometric complexity, the capability to tailor material properties also offers significant opportunities for technical innovation across many fields. This study explores the optimization of the LB-PBF process parameters for producing Ti6Al4V titanium alloy parts with controlled porosity. To this end, cuboid and lamellar samples were fabricated by systematically varying laser power, hatch distance, and layer thickness according to a full factorial Design of Experiments, and the resulting specimens were thoroughly characterized by analyzing envelope porosity, surface roughness and waviness, surface morphology, and surface area. A selection of specimens was further examined using small-angle X-ray scattering (SAXS) and wide-angle X-ray scattering (WAXS) to investigate the atomic structure and nanometric porosity of the material. The results demonstrated the possibility to finely control the porosity and surface characteristics of Ti6Al4V within specific LB-PBF process ranges. The pores were found to be mostly closed even for thin walls, while the surface roughness was recognized as the primary factor impacting the surface area. The lamellar samples obtained by exposing single scan tracks showed nearly an order-of-magnitude increase in both surface area and pore volume, thereby laying the groundwork for the production of parts with optimized porosity.

关键词： laser based-powder bed fusion process parameters optimization titanium alloy controlled porosity surface area

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Prediction modelling and process optimization for forming multi-layer cladding structures with laser directed energy deposition

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OPTICS AND LASER TECHNOLOGY 2021年 134卷 106607-106607页

作者： Guo, Chenguang He, Shunzhi Yue, Haitao Li, Qiang Hao, Guangbo Liaoning Tech Univ Sch Mech Engn Fuxin Peoples R China Univ Coll Cork Dept Elect & Elect Engn Cork Ireland

In the laser Directed Energy Deposition (DED) manufacturing of parts, severe deformation caused by the uneven geometric characteristics of a multi-layer deposition fusion area occurs extremely easily due to the complexity and insufficient understanding of the process. The above results in a significant decline in the overall geometric quality of the parts. To study the influence of the laser DED process parameters on the geometric characteristics of multi-layer cladding, a response surface method was used to carry out an experimental study of the multi-layer cladding structures. Statistical prediction models of multi-layer cladding manufacturing were established among the laser power, scanning speed, powder feeding rate, lap ratio and cladding width, cladding zone area, fusion zone area, and flatness ratio. The accuracy, adaptability and relevance of the models were verified. The influence of each process parameter on the geometric characteristics of the multi-layer cladding structure was analysed, and the laser DED process parameters were optimized. Experimental results show that the surface forming quality of specimens processed with optimized process parameters is good. Relative errors between the experimental and predicted values of the geometric characteristics of specimens are less than 9%;thus, the prediction models can suitably predict and control the geometric characteristics of a multi-layer cladding structure. The proposed optimization of process parameters can significantly improve the geometric quality of parts and has substantial value in engineering applications.

关键词： Laser directed energy deposition (DED) Multi-layer cladding Response surface method (RSM) process parameters optimization

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Parameter optimization of automated fiber placement based on CCD response surface method

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JOURNAL OF REINFORCED PLASTICS AND COMPOSITES 2024年

作者： Lu, Tianyao Li, Junli Zhang, Yilian Liu, Gang Qian, Bo Zhang, Liqiang Mao, Jian Shanghai Univ Engn Sci Sch Automot & Mech Engn 333 Longteng Rd Shanghai 201620 Peoples R China Shanghai Univ Sch Electromech Engn & Automat Shanghai Peoples R China Zhiyuan Adv Mfg Technol Res Ctr Chengdu Peoples R China Key Lab Intelligent Mfg Technol Complex Large Scal Shanghai Peoples R China

Defects in the automatic fiber placement (AFP) directly affect the prepreg bonding strength, resulting in a less strong product than expected. Improper process parameter selection is one of the major factors contributing to layup defects. However, there is a significant degree of uncertainty associated with the parameters that affect the lay-up quality and currently few studies that use response surfaces to consider the interaction of these parameters. In general, it is necessary to obtain a more accurate combination of process parameters through experiment, which is time-consuming and consumable. Moreover, it is difficult to investigate the interrelation between process parameters. Therefore, this paper presents a method of placement parameter optimization based on lap shear strength (LSS) to achieve high-quality and high-strength AFP process control. Firstly, this article establishes a comprehensive quality evaluation system by analyzing the causes of defects in AFP process. Secondly, the central composite design of experiments (CCD) response surface methodology was used to effectively optimize the process parameters and to understand the interactions between the various factors. A prediction model with quadratic polynomial regression equations for four process parameters and laydown quality was developed. Based on the regression equations, the process parameters with optimal lay-up quality were obtained. Then, using 30 sets of process parameters from the response surface experiment, lap samples were made for tensile testing. The lap shear strength of tows under various process conditions was studied, and the experimental results were consistent with the response surface analysis results. Finally, by analyzing the quality evaluation curve and load-time curve, it was found that the trend of AFP quality evaluation score and LSS was practically the same. Data analysis shows that placement using optimized parameters corresponding to higher scoring samples has higher l

关键词： Automated fiber placement central composite design response surface method layup quality process parameters optimization

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process Parameter optimization for Hybrid Manufacturing of PLA Components with Improved Surface Quality

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POLYMERS 2023年第17期15卷 3610-3610页

作者： Pascu, Sergiu Balc, Nicolae Tech Univ Cluj Napoca Fac Ind Engn Robot & Prod Management Dept Mfg Engn Memorandumului 28 Cluj Napoca 400114 Romania

This paper presents a new method of process parameter optimization, adequate for 3D printing of PLA (Polylactic Acid) components. The authors developed a new piece of Hybrid Manufacturing Equipment (HME), suitable for producing complex parts made from a biodegradable thermoplastic polymer, to promote environmental sustainability. Our new HME equipment produces PLA parts by both additive and subtractive techniques, with the aim of obtaining accurate PLA components with good surface quality. A design of experiments has been applied for optimization purposes. The following manufacturing parameters were analyzed: rotation of the spindle, cutting depth, feed rate, layer thickness, nozzle speed, and surface roughness. Linear regression models and neural network models were developed to improve and predict the surface roughness of the manufactured parts. A new test part was designed and manufactured from PLA to validate the new mathematical models, which can now be applied for producing complex parts made from polymer materials. The neural network modeling (NNM) allowed us to obtain much better precision in predicting the final surface roughness (Ra), as compared to the conventional linear regression models (LNM). Based on these modelling methods, the authors developed a practical methodology to optimize the process parameters in order to improve the surface quality of the 3D-printed components and to predict the actual roughness values. The main advantages of the results proposed for hybrid manufacturing using polymer materials like PLA are the optimized process parameters for both 3D printing and milling. A case study has been undertaken by the authors, who designed a specific test part for their new hybrid manufacturing equipment (HME), in order to test the new methodology of optimizing the process parameters, to validate the capability of the new HME. At the same time, this new methodology could be replicated by other researchers and is useful as a guideline on how to

关键词： hybrid manufacturing biodegradable thermoplastic polymer PLA components process parameters optimization roughness prediction neural network modeling

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Morphology analysis and process optimization of μ-SLA 3D manufactured micro-nano conic structure

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Journal of Intelligent Manufacturing and Special Equipment 2024年第1期5卷 203-220页

作者： Chongjun Wu Yutian Chen Xinyi Wei Junhao Xu Dongliu Li College of Mechanical Engineering Donghua University Shanghai China Saic Volkswagen Automobile Co. LTD Shanghai China Donghua University Shanghai China Sicher Elevator Co. Ltd. Huzhou China

Purpose This paper is devoted to prepare micro-cone structure with variable cross-section size by Stereo Lithography Appearance (SLA)-based 3D additive manufacturing technology. It is mainly focused on analyzing the forming mechanism of equipment and factors affecting the forming quality and accuracy, investigating the influence of forming process parameters on the printing quality and optimization of the printing quality. This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming. Design/methodology/approach The µ-SLA process is optimized based on the variable cross-section micro-cone structure printing. Multi-index analysis method was used to analyze the influence of process parameters. The process parameter influencing order is determined and validated with flawless micro array structure. Findings After the optimization analysis of the top diameter size, the bottom diameter size and the overall height, the influence order of the printing process parameters on the quality of the micro-cone forming is: exposure time (B), print layer thickness (A) and number of vibrations (C). The optimal scheme is A1B3C1, that is, the layer thickness of 5 µm, the exposure time of 3000 ms and the vibration of 64x. At this time, the cone structure with the bottom diameter of 50 µm and the cone angle of 5° could obtain a better surface structure. Originality/value This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

关键词： Micro-cone Micro-nano additive manufacturing Orthogonal experimental design process parameters optimization

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optimization Methodologies in Intelligent Machining Systems - A Review

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IFAC-PapersOnLine 2019年第10期52卷 282-287页

作者： M. Imad A. Hosseini H.A. Kishawy Machining Research Laboratory Faculty of Engineering and Applied Science Ontario Tech University Oshawa Ontario Canada

Manufacturing industries are always pressured by market demands to produce low-cost, high-quality products. Market demands tend to be complex, and full of challenges especially in fast-paced industrial settings. In order to satisfy rising demands, traditional manufacturing techniques are forced to develop and adapt new practices and methods of planning, control, and parameter optimization. The research and development of optimization methodologies have transformed traditional manufacturing methods into intelligent manufacturing techniques that are able to manufacture high quality products, at a lower cost, and a faster production rate. This work’s aim is to review and discuss recent trends and methods of optimization techniques in the emerging field of intelligent machining. The main discussion includes the application of optimization techniques in three areas: the intelligent process planning and scheduling, intelligent process control/management, and process parameter selection.

关键词： intelligent manufacturing intelligent machining intelligent process planning intelligent process management process parameters optimization optimization

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New optimization strategy for Chemical Mechanical Polishing process

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JSME INTERNATIONAL JOURNAL SERIES C-MECHANICAL SYSTEMS MACHINE ELEMENTS AND MANUFACTURING 2001年第2期44卷 534-543页

作者： Wang, GJ Chen, JL Hwang, JY Natl Chunghsing Univ Dept Mech Engn Taichung 402 Taiwan

In this study, a systematic approach to achieve a globally optimal Chemical Mechanical Polishing ( CMP) process is carried out. In this new approach, the orthogonal array technique adopted from the Taguchi method is used to realize an efficiently experimental design. The RBFNF neural fuzzy network is then applied to model the complex CMP process. The signal-to noise ratio (S/N) analysis (ANOVA) technique used in the conventional Taguchi method is also implemented to obtain the local optimum process parameters. The globally of optimal parameters are successively acquired in terms of the trained RBFNF network. In order to increase CMP throughput, a two-stage optimal strategy is also proposed. Experimental results demonstrate that the two-stage strategy performs better than the original approach even though the total processing time is reduced by 1/6.

关键词： Chemical Mechanical Polishing (CMP) Taguchi method RBFNF network process parameters optimization

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