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

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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process parameter optimization FOR ADDITIVELY MANUFACTURED STAINLESS STEEL 316L PARTS BY SELECTIVE ELECTRON BEAM MELTING 3

PROCESS PARAMETER OPTIMIZATION FOR ADDITIVELY MANUFACTURED S...

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3rd International Conference on Progress in Additive Manufacturing (Pro-AM)

作者： Wang, Chengcheng Tan, Xipeng Liu, Erjia Tor, Shu Beng Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Ctr 3D Printing 50 Nanyang Ave Singapore 639798 Singapore

An experimental study of process parameter optimization for stainless steel 316L (SS316L) parts additively manufactured by selective electron beam melting (SEBM) was carried out. The process parameters for different stages, particularly in the in-fill hatch melting stage, were optimized in this study. Near-fully dense (>99%) SS316L parts have been successfully fabricated with well-melted surfaces. Microstructural characterization was performed on the as-SEBM-built SS316L parts with the optimal process parameters. It revealed that near-equiaxed grains were formed, which is distinctive from the counterparts additively manufactured by other SEBM processes. The mechanism for the formation of near-equiaxed grains was discussed in detail. This paper provides an insight into fabricating SS316L parts with high density and desirable microstructure via SEBM process.

关键词： additive manufacturing selective electron beam melting stainless steel 316L process parameter optimization near-equiaxed grain structure

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A split-optimization approach for obtaining multiple solutions in single-objective process parameter optimization

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SPRINGERPLUS 2016年第1期5卷 1页

作者： Rajora, Manik Zou, Pan Yang, Yao Guang Fan, Zhi Wen Chen, Hung Yi Wu, Wen Chieh Li, Beizhi Liang, Steven Y. Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Donghua Univ Mech Engn Coll Shanghai 201620 Peoples R China Met Ind Res & Dev Ctr Reg R&D Serv Dept Taichung 407 Taiwan

It can be observed from the experimental data of different processes that different process parameter combinations can lead to the same performance indicators, but during the optimization of process parameters, using current techniques, only one of these combinations can be found when a given objective function is specified. The combination of process parameters obtained after optimization may not always be applicable in actual production or may lead to undesired experimental conditions. In this paper, a split-optimization approach is proposed for obtaining multiple solutions in a single-objective process parameter optimization problem. This is accomplished by splitting the original search space into smaller sub-search spaces and using GA in each sub-search space to optimize the process parameters. Two different methods, i.e., cluster centers and hill and valley splitting strategy, were used to split the original search space, and their efficiency was measured against a method in which the original search space is split into equal smaller sub-search spaces. The proposed approach was used to obtain multiple optimal process parameter combinations for electrochemical micromachining. The result obtained from the case study showed that the cluster centers and hill and valley splitting strategies were more efficient in splitting the original search space than the method in which the original search space is divided into smaller equal sub-search spaces.

关键词： Split-optimization Multiple solutions process parameter optimization Genetic algorithm (GA) Electrochemical micro-machining (EMM)

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process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第1-4期73卷 87-100页

作者： Peng, Anhua Xiao, Xingming Yue, Rui China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China Huaihai Inst Technol Engn Training Ctr Lianyungang 222005 Jiangsu Peoples R China Jiangsu Coll Informat Technol Dept Mechatron Engn Wuxi 214153 Jiangsu Peoples R China

Fused deposition modeling (FDM) is gaining distinct advantages because of its ability to fabricate the 3D physical prototypes without the restrictions of geometric complexities, while when it comes to accuracy and efficiency, the advantages of FDM is not distinct, and so how to improve them is worthy of study. Focusing on process parameter optimization, such parameters as line width compensation, extrusion velocity, filling velocity, and layer thickness are selected as control factors, input variables, and dimensional error, warp deformation, and built time are selected as output responses, evaluation indexes. Experiment design is assigned according to uniform experiment design, and then the three output responses are converted with fuzzy inference system to a single comprehensive response. The relation between the comprehensive response and the four input variables is derived with second-order response surface methodology, the correctness of which is further validated with artificial neural network. Fitness function is created using penalty function and is solved with genetic algorithm toolbox in Matlab software. With confirmation test, the results are obtained preferring to the results of the experiment 1 with the best comprehensive response among the 17 experiment runs, which confirms that the proposed approach in this study can effectively improve accuracy and efficiency in the FDM process.

关键词： Fused deposition modeling (FDM) process parameter optimization Response surface methodology (RSM) Fuzzy inference system (FIS) Artificial neural network (ANN) Genetic algorithm (GA)

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Carbon Emissions Reducing Mechanisms on Integrated process parameter optimization and Scheduling Problem Considering Makespan and Carbon Emissions

Carbon Emissions Reducing Mechanisms on Integrated Process P...

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3rd International Conference on Energy, Environment and Sustainable Development (EESD 2013)

作者： Lin, Wenwen Yu, D. Y. Zhang, Chaoyong Wang, S. Xie, Zhanpeng Liu, Shengqiang Tian, Yuhui Huazhong Univ Sci & Technol Sch Mech Sci & Engineer Wuhan 430074 Peoples R China Southeast Missouri State Univ Dept Ind Engn Technol Cape Girardeau MO USA China Natl Heavy Duty Truck Grp Co Ltd Jinan PR Peoples R China Dong Hoteam Software Co Ltd Jinan PR Peoples R China

ISBN: (纸本)9783037859759

This paper develops an integrated process parameter optimization and scheduling problem, where process parameter optimization and flow shop scheduling are considered simultaneously. Two objectives are taken into account: minimize makespan and carbon emissions. Non-dominated sorting genetic algorithm is adopted to handle such a problem. Then, the researchers propose two carbon emission reducing mechanisms to optimize the scheduling results: postponing mechanism, and process parameter preliminary optimization (PPPOM) mechanism. There are four cases depending on whether or not mechanisms are employed. The effects of those mechanisms on minimum objective functions, number of non-dominated solutions and quality of non-dominated solutions are studied. The results indicate that those mechanisms have significant influence on the optimization results. Better non-dominated solutions are produced when more mechanisms are employed.

关键词： process parameter optimization flow shop scheduling NSGA-Pi green manufacturing

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Research on process parameter optimization Of the High-neck Flange Closed Rolling Based on the Orthogonal Experimental Design

Research on Process Parameter Optimization Of the High-neck ...

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International Symposium on Vehicle, Mechanical, and Electrical Engineering (ISVMEE 2013)

作者： Sun, Baoshou Hong, Zhe Xu, Longqing Shu, Xuedao Gu, Boqin Huang, Xiaodiao Nanjing Univ Technol Coll Mech & Power Engn Nanjing 210009 Peoples R China Ningbo Univ Fac Mech Engn & Mech Ningbo 315211 Zhejiang Peoples R China

ISBN: (纸本)9783038350033

This paper simulates the process of the high-neck flange closed rolling on DEFORM-3D and optimizes the rolling process parameter by analyzing the results based on the orthogonal experimental design. For the high-neck flange, the results show that the effects on ellipticity are in the order of the mandrel feed speed, the main roll rotational speed and initial blank temperature. The former two factors show the significance while the initial blank temperature does not show that.

关键词： High-neck Flange Closed Rolling Orthogonal Experimental Design process parameter optimization

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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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Multi-objective process optimization of gear carburization and quenching based on ELM-MOPSO neural network

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MATERIALS TODAY COMMUNICATIONS 2025年 43卷

作者： Wang, Jiangang Song, Tianyu Wen, Tengyang Yang, Shuai Ju, Dongying Zhang, Xin Hebei Univ Sci & Technol Coll Mat Sci & Engn Shijiazhuang 050018 Peoples R China Hebei Univ Sci & Technol Key Lab Near Net Forming Mat Hebei Prov Shijiazhuang 050018 Peoples R China Shijiazhuang Liangcun Thermal Power Co Ltd Shijiazhuang 050018 Hebei Peoples R China TBEA Xinjiang Transformer Co Xinjiang 831100 Peoples R China Saitama Inst Technol Dept Mat Sci & Engn Fukaya 3690293 Japan

This study addresses heat treatment deformation in transmission gears used in new energy vehicles, focusing on the evolution of microstructure and properties during the carburizing and quenching process. Using the finite element software COSMAP, the heat treatment process was numerically simulated to obtain the temperature field, surface carbon concentration, martensite distribution, hardness distribution, and deformation profile of the gears under various process conditions. An ELM neural network algorithm was subsequently applied to train a predictive model on a small sample dataset, linking gear hardness distribution, deformation behavior, and process parameters across different heat treatment scenarios. This model was then optimized using the MOPSO algorithm to identify the optimal combination of parameters for carburizing and quenching. Experimental validation indicated that the optimized process reduced the total helix deviation of the gears after treatment to approximately 10 mu m, with experimental results closely aligning with theoretical predictions. These findings suggest that the simulation and optimization methods developed in this study offer valuable insights for practical production applications.

关键词： Transmission gear Carburization and quenching Numerical simulation Deformation prediction process parameter optimization

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