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Double pulse gas metal arc welding process parameter optimization and weld performance analysis based on response surface method

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MATERIALS RESEARCH EXPRESS 2023年第10期10卷 106503页

作者： Bin, Kun Yao, Ping Xu, Min Lin, Qian Gu, Yuqing Guangdong Polytech Normal Univ Coll Electromech Engn Guangzhou 510635 Peoples R China

Double pulse gas metal arc welding (DP-GMAW) is a low-frequency modulated high-frequency pulse welding, which can fully stir the molten pool, and improve the forming and performance of the weld. In this study, a mathematical model was developed using the response surface method (RSM) with three main process parameters (welding current, peak-to-base ratio, welding speed) as input values with three key geometric characteristics parameters, and the mechanical properties of the resulting welds were investigated. The results showed that the parameter model developed in this study had less error and could optimize the process parameters better;the optimal process parameters (welding current, peak-to-base ratio, welding speed) were 160 A, 39.93%, and 83.11 cm min-1, respectively. The improved welding parameters result in better mechanical properties and better weld profile, and more stable welding process. As a result, this experiment provides a new perspective for process parameter optimization and mechanical properties research for weld manufacturing.

关键词： double pulse gas metal arc welding response surface methodology process parameter optimization mechanical properties

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Preparation and process parameter optimization of Continuous Carbon Fiber-Reinforced Polycarbonate Prepreg Filament

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POLYMERS 2023年第3期15卷 607-607页

作者： Chen, Xun Wang, Yesong Liu, Manxian Qu, Sheng Zhang, Qing Chen, Shuguang Jiangsu Univ Sci & Technol Coll Automation Zhenjiang 212000 Peoples R China Jiangsu Univ Sci & Technol Sch Mech Engn Zhenjiang 212000 Peoples R China Tongji Univ Sch Aerosp Engn & Appl Mech Shanghai 200092 Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China

Continuous fiber-reinforced composite 3D printing (CFRC 3DP) has become a hot topic of interest for many experts and scholars. Continuous fiber-reinforced prepreg filament (CFRPF) for printing needs to be prepared in advance. In this paper, on the basis of the resin fusion impregnation theory, a fabrication device was designed for continuous carbon fiber-reinforced polycarbonate prepreg filament (CCFRPF). Then, according to the orthogonal test and the TOPSIS entropy weight optimization theory, the optimization method for CFRPF/PC preparation process parameters was proposed, and the relationship between the preparation process parameters and the performance indexes was discussed. The results show that when preparing CCFRPF/PC, the weight of diameter performance index is the largest, about 0.75. The optimal combination of process parameters for CCFRPF/PC is, respectively, 285 degrees C for the outlet mold temperature, 305 degrees C for the impregnation mold temperature, and 1 m/min for the winding speed. In this case, the diameter, roundness, minimum curvature radius and tensile strength of 0.375 mm, 29.4 mu m, 9.775 mm and 1298 MPa were achieved, respectively.

关键词： continuous carbon fiber reinforced polycarbonate prepreg filament continuous fiber-reinforced composite 3D printing process parameter optimization

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Surface process and parameter optimization for flat-top laser cleaning of anodized 7050 aluminum alloy

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Journal of Laser Applications 2025年第2期37卷

作者： Jiang, Yuanmao Wang, Shouren Wang, Gaoqi Xiao, Zhen Li, Peining Zhang, Luyu Shandong Key Laboratory of Surface Engineering and Intelligent Equipment for Key Metal Components University of Jinan Jinan 250022 China School of Mechanical Engineering University of Jinan Jinan 250022 China

Flat-top pulsed fiber laser is used to clean the surface of 7050 aluminum alloy after anodizing. The optimal parameters for removing the anodized film are explored by changing the laser power, and various properties of the surface after laser cleaning with different parameters are studied. Additionally, combining formula analysis, two possible hypotheses are proposed to explain the reasons of the phenomena of grain size ratio variation. The results indicate that the optimal parameter for laser cleaning the anodic oxide film on 7050 aluminum alloy is at 195 W by scanning twice. Under appropriate laser cleaning parameters, various properties of the material such as roughness, wear resistance, hardness, corrosion resistance, and so on have been improved. The mechanism of removing the anodic oxide film by flat-top laser is predominantly based on ablation mechanism, which is different from that of Gaussian laser that primarily rely on the vibration mechanism. Laser cleaning can refine the grains and exhibit obvious anisotropy. The changes in the size ratio are related to the magnitude of the impact force and the heat absorbed by the surface, which may be related to the incomplete exposure of the areas covered by residual oxide films and to temperature and shock waves during incomplete cleaning. Additionally, there may be unknown effects involved in this process. © 2025 Author(s).

关键词： aluminum alloy anodic oxide film laser cleaning process parameter optimization

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process parameter selection and optimization of laser powder bed fusion for 316L stainless steel: A review

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JOURNAL OF MANUFACTURING processES 2022年 75卷 415-434页

作者： Ahmed, N. Barsoum, I. Haidemenopoulos, G. Abu Al-Rub, R. K. Khalifa Univ Adv Digital & Addit Mfg Ctr Abu Dhabi U Arab Emirates Khalifa Univ Dept Mech Engn Abu Dhabi U Arab Emirates Royal Inst Technol KTH Dept Engn Mech Stockholm Sweden Univ Thessaly Dept Mech Engn Volos Greece

Stainless steel 316L has been an extensively investigated metallic material for laser powder bed fusion (L-PBF) in the past few decades due to its high corrosion resistance. However, there are challenges related to producing LPBF parts with minimal defects, attaining mechanical properties comparable with traditional process and dependency on time consuming post process treatments. The selection of L-PBF process parameters is crucial to overcome these challenges. This paper reviews the research carried out on L-PBF process parameter optimization for fabrication of 316L steel components for maximizing part densifications and attaining desired microstructure morphologies in parts. A brief work on numerical simulation approach for process parameter optimization for high densifications is also included in this paper.

关键词： Selective Laser Melting process parameter optimization SS316L steel Densification Microstructure

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Digital twin-driven surface roughness prediction and process parameter adaptive optimization

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ADVANCED ENGINEERING INFORMATICS 2022年 51卷

作者： Liu, Lilan Zhang, Xiangyu Wan, Xiang Zhou, Shuaichang Gao, Zenggui Shanghai Univ Sch Mech Engn & Automat Shanghai 200444 Peoples R China Shanghai Univ Shanghai Key Lab Intelligent Mfg & Robot Shanghai 200444 Peoples R China

In the process of parts machining, the real-time state of equipment such as tool wear will change dynamically with the cutting process, and then affect the surface roughness of parts. The traditional process parameter optimization method is difficult to take into account the uncertain factors in the machining process, and cannot meet the requirements of real-time and predictability of process parameter optimization in intelligent manufacturing. To solve this problem, a digital twin-driven surface roughness prediction and process parameter adaptive optimization method is proposed. Firstly, a digital twin containing machining elements is constructed to monitor the machining process in real-time and serve as a data source for process parameter optimization;Then IPSO-GRNN (Improved Particle Swarm optimization-Generalized Regression Neural Networks) prediction model is constructed to realize tool wear prediction and surface roughness prediction based on data;Finally, when the surface roughness predicted based on the real-time data fails to meet the processing requirements, the digital twin system will warn and perform adaptive optimization of cutting parameters based on the currently predicted tool wear. Through the development of a process-optimized digital twin system and a large number of cutting tests, the effectiveness and advancement of the method proposed in this paper are verified. The organic combination of real-time monitoring, accurate prediction, and optimization decision-making in the machining process is realized which solves the problem of inconsistency between quality and efficiency of the machining process.

关键词： Digital twin process parameter optimization Surface roughness prediction Tool wear prediction IPSO-GRNN

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parameter optimization of the sheet metal forming process using an iterative parallel Kriging algorithm

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2005年第6期29卷 498-507页

作者： Jakumeit, J Herdy, M Nitsche, M Access eV D-52072 Aachen Germany Fraunhofer Inst Algorithms & Sci Comp D-53754 St Augustin Germany Fahrzeugind mbH INPRO Innovat Gesell Fortgeschrittene Prod Syst D-10587 Berlin Germany

Different numerical optimization strategies were used to find an optimized parameter setting for the sheet metal forming process. A parameterization of a time-dependent blank-holder force was used to control the deep-drawing simulation. Besides the already well-established gradient and direct search algorithms and the response surface method the novel Kriging approach was used as an optimization strategy. Results for two analytical and two sheet metal forming test problems reveal that the new Kriging approach leads to a fast and stable convergence of the optimization process. Parallel simulation is perfectly supported by this method.

关键词： Kriging parallel simulation process parameter optimization sheet metal forming

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Multi-objective multi-parameter optimization of the industrial LBW process using a new optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2012年第B6期226卷 1018-1025页

作者： Rao, Ravipudi V. Kalyankar, Vivek D. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

The concept of automation has been brought into the industries in order to increase the production rate and at the same time to minimize the production cost. The LBW process is widely replacing manual welding processes in many fabrication industries owing to the high level of automation. In the present work, an attempt is made to achieve conflicting objectives by finding optimum parameter settings for the LBW process. A recently developed advanced optimization algorithm is applied for parameter optimization of the LBW process. Two different multi-objective optimization examples are considered and significant improvement is obtained by the proposed optimization algorithm as compared with the earlier works.

关键词： Industrial laser beam welding process parameter optimization teaching-learning-based optimization algorithm

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AI-Powered Very-High-Cycle Fatigue Control: Optimizing Microstructural Design for Selective Laser Melted Ti-6Al-4V

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MATERIALS 2025年第7期18卷 1472-1472页

作者： Awd, Mustafa Walther, Frank Bremen City Univ Appl Sci HSB Inst Informat & Automat IIA Flughafenallee 10 D-28199 Bremen Germany Testia GmbH Airbus Grp Cornelius Edzard Str 15 D-28199 Bremen Germany TU Dortmund Univ Fac Mech Engn Chair Mat Test Engn WPT Baroper Str 303 D-44227 Dortmund Germany

Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue tests where very high cycle fatigue properties were assessed up to 1x1010 cycles. Machine learning models predicted critical fatigue thresholds, optimized process parameters, and reduced design iteration cycles by over 50%, leading to faster production of safer, more durable components. By refining grain orientation and phase uniformity, fatigue crack propagation resistance improved by 20-30%, significantly enhancing fatigue life and reliability for mission-critical aerospace components, such as turbine blades and structural airframe parts, in an industry where failure is not an option. Additionally, the machine learning-driven design of metamaterials enabled structures with a 15% weight reduction and improved yield strength, demonstrating the feasibility of bioinspired geometries for lightweight applications in space exploration, medical implants, and high-performance automotive components. In the area of titanium and aluminum alloys, machine learning identified key process parameters such as temperature gradients and cooling rates, which govern microstructural evolution and enable fatigue-resistant designs tailored for high-stress environments in aircraft, biomedical prosthetics, and high-speed transportation. Combining theoretical insights and experimental validations, this research highlights the potential of machine learning to refine microstructural properties and establish intelligent, adaptive manufacturing systems, ensuring enhanced reliability, performance, and efficiency in cutting-edge engineering applications.

关键词： machine learning additive manufacturing very high cycle fatigue (VHCF) resistance microstructural optimization process parameter optimization

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Surrogate-based optimization framework for enhancing SMT process quality and productivity in electronics manufacturing services

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第11-12期136卷 5103-5122页

作者： Lee, Ching-Hung Lin, Kai-Xiang Chou, Che-Wei Natl Yang Ming Chiao Tung Univ Inst Elect & Control Engn Hsinchu Taiwan Chung Yuan Christian Univ Dept Elect Engn Taoyuan Taiwan Feng Chia Univ Dept Ind Engn & Syst Management Taichung Taiwan

The quality of the surface mount technology (SMT) process directly impacts product efficiency and reliability. Solder paste printing and reflow soldering processes are vital for assembling high-quality electronic components. Effectively optimizing these process parameters to ensure product consistency and reliability has become a critical issue in the electronics manufacturing services industry. Motivated by realistic needs to enhance the quality of the SMT process, this study integrates a surrogate-based optimization framework combining neural networks and particle swarm optimization (PSO) techniques to minimize experiment counts in SMT processes. Furthermore, a defect detection system utilizing YOLOv4 achieves real-time solder joint quality classification, significantly reducing production downtime and costs. This study proposes a surrogate-based optimization framework to improve the quality and productivity of the SMT production line. It encompasses five stages: domain knowledge, design of experiment, data collection and analysis, modeling, and optimization. Statistical correlation analysis and experimental design are used to reduce experiment counts. Then, neural networks and optimization algorithms are utilized to identify the optimal process parameters in the solder paste printing process. Moreover, this study proposes transfer learning methods for cross-product and line parameter optimization, which not only reduces production changeover time but also offers valuable insights for developing the solder paste printing process. A heat transfer model derived from a single experiment is used to identify parameters for reflow soldering. The target function is then optimized to find the optimal reflow recipe. Additionally, a solder joint defect detection system is established using deep learning and image processing techniques, capable of real-time detection and classification of solder joint defects. To evaluate the validity of the proposed framework, the surrogate

关键词： Surface mount technology Surrogate model process parameter optimization Particle swarm optimization Neural network

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Optimizing process parameters for enhanced mechanical performance in 3D printed impellers using graphene-reinforced polylactic acid (G-PLA) filament

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2025年第3期39卷 1387-1397页

作者： Subramani, Raja Chennai Inst Technol Ctr Adv Multidisciplinary Res & Innovat Chennai Tamil Nadu India

Additive manufacturing, especially 3D printing, has changed the production landscape, minimizing waste while allowing layers to build up. The selection of optimal process parameters will be done to achieve higher mechanical properties and quick production. This article aims to find the optimal parameters for creating impellers using G-PLA filament during 3D printing using spice logic AHP in decision making. The optimal parameters found are: 70 % infill density, 0.15 mm layer height, 60 mm/s print speed, 95 degrees C heat bed, 245 degrees C extruder, and 95 mm/s travel speed. These results prove that the selection of proper parameter values strongly enhances the performance of printed parts significantly, especially for critical applications demanding high mechanical strength as well as precision.

关键词： Additive manufacturing process parameter optimization Spice logic AHP Composite thermoplastic polymer Impeller application

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