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Comprehensive optimization of In625 laser cladding: from process parameters to path parameters

WELDING IN THE WORLD 2025年第6期69卷 1529-1546页

作者： Zhang, Yingying Sun, Jiayu Wang, Yiqi Sun, Zhengyu Gao, Yanchong Yu, Tianbiao Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

This study aims to optimize the single-track process parameters (laser power P, scanning speed V, powder feed rate F) and multi-track, multi-layer path parameters (overlap distance L, Z-axis increment Delta Z) in In625 laser cladding. The optimization objectives include clad width W, height H, melt pool area S, and dilution rate D. A Taguchi experimental design was employed, utilizing bubble plots and surface plots to visually present the influence trends of process parameters on the optimization objectives. Analysis of variance (ANOVA) and signal-to-noise ratio (S/N) analysis were conducted to assess the significance and impact of the process parameters on the optimization objectives. Using the entropy-weight TOPSIS method, the optimal parameter combination (P = 450W, V = 9 mm/s, F = 10.21 g/min) was determined based on the C value of the comprehensive evaluation index. The path parameters were optimized using a combination of theoretical analysis and experimentation to obtain the optimal overlap distance (L = 730 mu m) and Z-axis increment (Delta Z = 180 mu m). The optimized parameters were validated through multi-track, multi-layer experiments, and the results show that the fusion cladding layer prepared with the optimized parameters has a uniform and dense organization, uniform hardness distribution, small fluctuation of friction coefficient, and stable performance of the fusion cladding layer. The optimized parameters contribute to the effective implementation of multi-track, multi-layer laser cladding processes, providing a reliable foundation for surface repair and modification in practical applications.

关键词： Laser cladding In625 alloy process parameter optimization Entropy-weight TOPSIS Path parameter optimization Multi-track multi-layer cladding

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Development of additive printing equipment and process parameters optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年第3期239卷 821-835页

作者： Liu, Yuegang Zhao, Xiuhua Xu, Liyun Wang, Jinhui Zheng, Changsheng Weifang Univ Sci & Technol Coll Intelligent Mfg Weifang Shandong Peoples R China Qilu Univ Technol Fac Mech Engn 3501 Daxue Rd Jinan 250353 Shandong Peoples R China Nantong Univ Coll Text & Clothing Nantong Jiangsu Peoples R China

The additive printing of warp-knitted vamp is a manufacturing process forming the complex three-dimensional pattern with a thickness of 0.8mm or more through dozens of overprinting and superimposition-printing on the warp-knitted spacer fabrics. It is conducted by hands in most enterprises, and it is difficult to meet the demand for high-end products. This paper aims at building up automatic printing equipment, promoting the shoemaking technology and automation level to. Four key mechanisms such as printing mechanism, alignment platform, locating mechanism, and transport mechanism are designed to assemble additive printing equipment. Subsequently, Failure Mode and Effect Analysis (FMEA) method are used to analyze the reliability of additive printing equipment to obtain the failure mode and risk priority number, which provided a theoretical basis for equipment optimization and maintenance. Furthermore, this paper proposes to fuse orthogonal experiments, Support Vector Machine for Regression (SVR) and Genetic Algorithm (GA) to establish the optimizing model for predicting the optimal process parameters to solve problem of artificial experience dependence. The developed equipment and matched process parameters are used to produce warp-knitted vamp that fully meets the requirements of tensile strength, flexometer and hydrolysis testes.

关键词： Additive printing warp-knitted vamp FMEA method process parameter optimization

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Data-driven online prediction and control method for injection molding product quality

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JOURNAL OF MANUFACTURING processES 2025年 145卷 252-273页

作者： Cheng, Youkang Zhan, Hongfei Yu, Junhe Wang, Rui Ningbo Univ Fac Mech Engn & Mech Ningbo 315211 Peoples R China

Injection molding is a complex, non-linear production process where product quality depends on variable and interacting process parameters. In continuous mass production, fluctuations in process parameters make it impossible to ensure the stability of product quality. Existing quality control mainly relies on historical experience for manual adjustments, often leading to high scrap rates, reduced productivity, and resource consumption. Therefore, this paper proposes a data-driven quality control method for injection molded products, which uses a feature prediction model to forecast the process parameters for the next production cycle. An optimization algorithm based on the quality prediction model is used to fine-tune the process parameters, providing operators with a reasonable parameter scheme in advance. First, a time series feature prediction model is proposed based on the multiscale retention module in the Retentive Network (RetNet) model. This model integrates Empirical Mode Decomposition (EMD) and a Mixed Domain Attention Module (MDAM). The model extends features across different time dimensions using EMD to explore the time-series relationships. Additionally, a new MDAM is designed to identify key process features adaptively. Second, a quality prediction model based on the Extreme Gradient Boosting (XGBoost) algorithm is built on the feature prediction model. The output of the prediction model is used to calculate fitness. At the same time, the Dung Beetle optimization algorithm is employed for efficient reverse search to adjust the process parameters precisely. Finally, the effectiveness of the proposed method in injection product quality control is validated through the computational analysis of two injection molding datasets, providing strong support and solutions for real-time quality management in this field.

关键词： Injection molding Product quality prediction process parameter optimization Quality control Prior control

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Predictive modelling and process optimisation of high-speed laser cladding FeCrNiMo0.75 alloy coating on 45# steel

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MATERIALS TODAY COMMUNICATIONS 2025年 44卷

作者： Duan, Bingyan Zhao, Haichao Lin, Naiming Zhou, Li Wang, Xin Ma, Guozheng Wang, Haidou Wu, Yucheng Taiyuan Univ Technol Coll Mat Sci & Engn Taiyuan 030024 Shanxi Peoples R China Army Acad Armored Forces Natl Engn Res Ctr Remfg Beijing Peoples R China Army Acad Armored Forces Natl Key Lab Remfg Beijing 100072 Peoples R China Fuzhou Univ Sch Mech Engn & Automation Fuzhou 350116 Fujian Peoples R China Hefei Univ Technol Met & Proc Technol Natl Local Joint Engn Res Ctr Nonferrous Hefei 230009 Peoples R China

Optimal process parameters are the foundation for obtaining high-quality cladding coatings. However, the optimization of process parameters and their interactions affecting the coating has always been a challenge in the application of High-speed Laser Cladding (HLC) technology. This study, utilizing experimental data from Central Composite Design (CCD), the application of response surface methodology (RSM) and non-dominated sorting genetic II (NSGA-II) algorithms for process parameter optimization in high-speed laser cladding of FeCrNiMo0.75 coating was thoroughly investigated, and comparative experimental validation of the optimization results was performed. The results showed that the aspect ratio of the coating and microhardness was a mutually restrictive relationship. Both methods improved the response targets after optimization, but the focus of the optimization results differed. The aspect ratio optimization result of RSM was better than that of NSGA-II, while the micro- hardness optimization result of NSGA-II was superior to RSM. Cladding trials using process parameters optimized by RSM showed that the actual coating hardness was 261.7 HV0.1, with a prediction error of 3.54 %, and the actual aspect ratio was 8.1945, with a prediction error of 2.84 %. Additionally, cladding trials using process parameters optimized by NSGA-II showed an actual coating hardness of 318.8 HV0.1, with a prediction error of 2.70 %, and an actual aspect ratio of 6.9452, with a prediction error of 1.68 %. This offers valuable insights for optimizing process parameters and enhances the practical application of high-speed laser cladding.

关键词： High-speed laser cladding process parameter optimization RSM and NSGA-II

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Constraint Active Search in process Window optimization for Powder Feed Directed Energy Deposition

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INTEGRATING MATERIALS AND MANUFACTURING INNOVATION 2025年第1期14卷 106-114页

作者： Wang, Xiaoxiao Loli, Jose A. Ulissi, Zachary W. de Boer, Maarten P. Webler, Bryan A. Kurchin, Rachel C. Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Mech Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Mat Sci & Engn 5000 Forbes Ave Pittsburgh PA 15213 USA

Optimizing process parameters for directed energy deposition is crucial to achieve high-quality printed parts. However, this optimization process often entails significant time and cost investments. An initial investigation into the process window can be conducted through the examination of single tracks. In this work, we investigate the utility of constraint active search (CAS) to efficiently identify process window that yield 4340 low-alloy steel single tracks with desired geometrical features. The effectiveness of the CAS method was assessed through experiments with physical and interpolated measurement. Fifty single tracks from randomly sampled process parameter combinations with different power, scan velocity, and laser spot size and ten single tracks from CAS-generated parameters were produced and analyzed. The results demonstrate that our search method outperforms random search, with 80% of parameter sets identified as desirable compared to only 4% in the case of random search. Moreover, an interpolated ground truth in input spaces of various dimensionalities was built in order to assess repeatability without excessive experimental cost. The results indicate that the CAS achieves higher precision compared to grid search and random search, especially in higher-dimensional process parameter spaces.

关键词： Directed energy deposition Active learning process parameter optimization Constraint active search

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parameter optimization for accurate and repeatable strut width in the 3D printing of composite bone scaffolds

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JOURNAL OF MANUFACTURING processES 2024年 131卷 1631-1641页

作者： Bappy, Mahathir Mohammad Van Epps, Emma Priddy, Lauren B. Tian, Wenmeng Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA Mississippi State Univ Dept Agr & Biol Engn Mississippi State MS 39762 USA

Critically sized bone defects present a significant challenge to orthopedic surgeons due to the limited availability of autograft bone tissue, which is the current gold-standard treatment. As an alternative, 3D printed porous scaffolds can be designed to mimic bone's mechanical and biochemical properties to support tissue regeneration. However, 3D scaffold printing with high geometric accuracy and repeatability can be challenging, especially when printing new composite materials and geometries. Therefore, the objective of this study was to optimize the extrusion-based 3D bioprinting process parameters for composite polymer-ceramic scaffolds. Bone scaffolds composed of a polylactic-co-glycolic acid (PLGA) and 5 % nano-hydroxyapatite (nHA) composite were printed and analyzed to evaluate their geometric accuracy, which is primarily determined by process parameters. This empirical study investigated the effects of different process parameters, particularly, nozzle temperature, pressure, and printing speed, on the geometric accuracy (i.e., strut width) of the printed scaffolds. Starting with a full factorial design of experiments, in-situ layer-wise optical images were captured, which were analyzed using image processing for strut width characterization. Subsequently, a new iterative process optimization method was proposed that involves regression modeling and bound constraint-based minimization. A case study on printing a two-layer scaffold was used to demonstrate the effectiveness of the proposed method. Overall, the geometric accuracy of the printed scaffolds improved significantly, maintaining a range of +/- 5 % from the nominal strut width as iterative experiments were conducted, which demonstrates the significant potential of the proposed method in bioprinting process parameter optimization.

关键词： 3D bioprinting Composite material Bone scaffold process parameter optimization Tissue engineering

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optimization of process parameters for gas-powder flow behavior in the coaxial nozzle during laser direct metal deposition based on numerical simulation

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第7-8期130卷 3967-3982页

作者： Zhao, Kai Yang, Kun Chen, Mingzhi Wang, Zhandong Wu, Erke Sun, Guifang Southeast Univ Sch Mech Engn Nanjing 211189 Jiangsu Peoples R China Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Peoples R China

Laser direct metal deposition (DMD) can supply a new method in the fields of surface modification and near-net forming. The powder flow behavior and its convergence characteristics play a crucial role in the deposition quality during the DMD process. In this research, the k-epsilon turbulence model based on the computational fluid dynamics (CFD) modeling method was innovatively employed to establish the numerical model of the gas-powder flow. Then, the dense discrete phase model (DDPM) was utilized in this gas-powder coupling model to accurately calculate the collision between particles as well as between particles and inner wall of the nozzle. Afterward, the response surface method (RSM) was carried out to design the numerical simulation scheme, analyze a series of simulation results, explore the correlation between the process parameters and the responses, and establish the prediction model of powder convergence characteristics. Furthermore, the process parameters were optimized by considering the influence of defocusing amount, with smaller powder spot diameter and higher maximum powder mass concentration as optimization objectives. It was found that the prediction model of powder convergence characteristics demonstrated a high degree of accuracy and reliability. The single-deposition track exhibited better deposition quality fabricated with the optimized process parameters. The research method and results mentioned in the present study were expected to provide significant theoretical guidance for the selection and application of process parameters during the laser direct metal deposition process.

关键词： Laser direct metal deposition Coaxial nozzle Numerical simulation Prediction model process parameter optimization

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A fast ramp-up framework for wafer yield improvement in semiconductor manufacturing systems

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 76卷 222-233页

作者： Xu, Hong-Wei Zhang, Qi-Hua Sun, Yan-Ning Chen, Qun-Long Qin, Wei Lv, You-Long Zhang, Jie Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn &Management Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Artificial Intelligence Shanghai 200240 Peoples R China Hong Qi Integrated Circuit Zhu Hai Corp Zhu Hai 519000 Peoples R China Donghua Univ Inst Artificial Intelligence Shanghai 201620 Peoples R China

Wafer yield is crucial for assessing semiconductor fabrication enterprises' stability and technological maturity. Quickly achieving the yield ramp-up of new products and timely feedback of failure analysis results to the design company are prerequisites for wafer fabrication factories to achieve stable mass production. However, the traditional wafer yield ramp-up process is time-consuming, and there is a high level of uncertainty in yield improvement even after parameter optimization. Therefore, a fast ramp-up framework for wafer yield improvement in semiconductor manufacturing systems is proposed to address both the aspect of temporal and the stability of yield enhancement. The Learning Cycle (LC) for yield acquisition, including fault detection, has been refined and optimized in the temporal dimension. Specifically, methods for multi-batch yield prediction and interpretability of defect traceability have been used to increase the LC efficiency. The yield enhancement dimension introduces a parameter optimization approach based on virtual metrology. This approach utilizes predictive control strategies to perform trial production verification with process parameters set to theoretical optima, thereby mitigating yield aberrant fluctuations in uncertainties inherent in the fabrication process and ensuring a consistent upward trajectory in yield levels with each iteration. The framework's efficacy is demonstrated using a case study focusing on yield control and enhancement within a specific wafer fabrication line. Applying the proposed framework shortened the ramp-up time from process design and pilot production to stable mass production, exhibiting an efficiency increase of nearly 17.6 %.

关键词： Yield ramp-up Yield learning curves Yield prediction model Fault detection Virtual metrology process parameter optimization

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Integrated approach to Wire Arc Additive Manufacturing (WAAM) optimization: Harnessing the synergy of process parameters and deposition strategies

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2024年 30卷 2478-2499页

作者： Mansor, Muhammad Safwan Mohd Raja, Sufian Yusof, Farazila Muhamad, Mohd Ridha Manurung, Yupiter HP. Adenan, Mohd Shahriman Hussein, Nur Izan Syahriah Ren, James Univ Malaya Fac Engn Dept Mech Engn Kuala Lumpur 50603 Malaysia Univ Malaya Ctr Adv Mfg & Mat Proc AMMP Ctr Kuala Lumpur 50603 Malaysia Univ Malaya Ctr Fdn Studies Sci Kuala Lumpur 50603 Malaysia MARA Univ Technol UITM Smart Mfg Res Inst SMRI Shah Alam 40450 Selangor Malaysia Tech Univ Malaysia UTeM Fac Mfg Engn Durian Tunggal 76100 Melaka Malaysia Liverpool John Moores Univ Fac Engn & Technol James Parsons Bldg3 Byrom St Liverpool L3 3AF England

The flexibility of Additive Manufacturing (AM) technologies in the metal 3D printing process has gained significant attention in research and industry, which allows for fabricating complicated and intricate Near-NetShape (NNS) geometry designs. The achievement of desired characteristics in Wire-Arc Additive Manufactured (WAAM) components is primarily contingent upon the careful selection and precise control of significant processing variables, including bead deposition strategy, wire materials, type of heat source, wire feed speed, and the application of shielding gas. As a result, optimizing these most significant process parameters has improved, producing higher-quality WAAM-manufactured components. Consequently, this has contributed to the overall rise in the method's popularity and many applications. This article aims to provide an overview of the wire deposition strategy and the optimization of process parameters in WAAM. The optimization of numerous wire deposition techniques and process parameters in the WAAM method, which is required to manufacture highquality additively manufactured metal parts, is summarised. The WAAM optimization algorithm, in addition to anticipate technological developments, has been proposed. Subsequently, a discussion ensues regarding the potential for WAAM optimization within the swiftly growing domain of WAAM. In the end, conclusions have been derived from the reviewed research work.

关键词： Wire-Arc Additive Manufacturing process parameter optimization Near-net-shape WAAM deposition strategy Statistical analysis tool

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Thermo-mechanical coupled three-dimensional finite element simulation analysis of drilling thermoplastic braided carbon fiber composite and optimization of process parameters

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THIN-WALLED STRUCTURES 2024年 204卷

作者： Liu, Yong Huang, Jiadong Wang, Tianyu Sun, Pan Zhou, Honggen Jiangsu Univ Sci & Technol Coll Mech Engn Zhenjiang 212100 Peoples R China Dalian Univ Technol Key Lab High Performance Precis Mfg Dalian 116024 Peoples R China Jiangsu Hengbo Composite Mat Co Ltd Zhenjiang 212332 Peoples R China

Optimizing the process parameters of thermoplastic composites during drilling is paramount for mitigating tearing, delamination, and other forms of damage, while simultaneously enhancing the tensile strength and extending the long-term service life of the overall structure. This study focuses on exploring a thermalmechanical coupling method for predicting dynamic mechanical progressive failure and determining optimal process parameters during the drilling of Braided Carbon Fiber Reinforced Polyether Ether Ketone (BCF/PEEK). Initially, a thermal conduction constitutive model of BCF/PEEK was developed based on the proposed thermal distribution ratio calculation method. Concurrently, a user-defined material subroutine VUMAT and a bilinear cohesive element model, were implemented on the ABAQUS/Explicit platform to simulate the delamination behaviors of drilling BCF/PEEK using a tapered drill-reamer. Subsequently, a comprehensive BCF/PEEK drilling experiment platform was constructed, and the simulation accuracy of the drilling finite element (FE) model was verified through temperature, thrust force, and hole-wall morphology analyses. Finally, response surface regression models were established for process parameters, and the optimal parameters were predicted and validated. The results indicate that minimum thrust force and temperature can be achieved using the tapered drill-reamer with a spindle speed set at 4878.79/3000 r/min and a feed speed of 34.04/30 mm/min, respectively, with a maximum error of only 8.78 %.

关键词： BCF/PEEK Drilling Thermo-mechanical coupled process parameter optimization

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