检索结果-内蒙古大学图书馆

Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches

引用

Scientific reports 2025年第1期15卷 17966页

作者： İnayet Burcu Toprak Vocational School of Technical Sciences Akdeniz University Antalya Turkey. ibmutlu@akdeniz.edu.tr.

The primary objective of this study is to evaluate the influence of critical process parameters on the hardness of AlSi10Mg alloy fabricated via the Laser Powder Bed Fusion (LPBF) technique. To optimize these parameters, a Taguchi-based signal-to-noise (S/N) ratio analysis was employed. Furthermore, Analysis of Variance (ANOVA) was conducted to quantify the statistical significance and contribution ratio of each parameter to the observed variation in hardness. In addition, Machine Learning algorithms were applied to predict hardness values. A multidisciplinary approach was adopted to optimize production processes, reduce manufacturing costs, and shorten processing times. Taguchi and ANOVA analyses were performed using Minitab 21, while Machine Learning implementations were carried out in R statistical software. The applied Machine Learning techniques include Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Multiple Linear Regression (MLR). The results indicate that scan speed exerts the most significant influence on hardness, followed by laser power. Notably, the highest hardness was achieved using a laser power of 200 W, a hatch distance of 0.20 mm, and a scan speed of 2000 mm/s. Among the models, SVM achieved the highest coefficient of determination (R = 0.73). This study highlights the importance of integrating Machine Learning and statistical analysis methods for the effective modeling and optimization of LPBF processes. The findings contribute significantly to the literature and serve as a valuable reference for future research aimed at improving LPBF process efficiency and performance.

关键词： Hardness optimization Laser powder bed fusion Machine learning in additive manufacturing process parameter optimization Taguchi method

来源：评论

学校读者我要写书评

暂无评论

Multi-objective quality control method for cold-rolled products oriented to customized requirements

引用

International Journal of Minerals,Metallurgy and Materials 2021年第8期28卷 1332-1342页

作者： Yi-fan Yan Zhi-min Lü Collaborative Innovation Center of Steel Technology University of Science and Technology BeijingBeijing 100083China

To deal with the increasing demand for low-volume customization of the mechanical properties of cold-rolled products, a two-way control method based on mechanical property prediction and process parameter optimization(PPO) has become an effective solution. Aiming at the multi-objective quality control problem of a company's cold-rolled products, based on industrial production data, we proposed a process parameter design and optimization method that combined multi-objective quality prediction and PPO. This method used the multi-output support vector regression(MSVR) method to simultaneously predict multiple quality indices. The MSVR prediction model was used as the effect verification model of the PPO results. It performed multi-process parameter collaborative design and realized the optimization of production process parameters for customized multi-objective quality requirements. The experimental results showed that, compared with the traditional single-objective quality prediction model based on support vector regression(SVR), the multi-objective prediction model could better take into account the coupling effect between process parameters and quality index, the MSVR model prediction accuracy was higher than that of the SVR, and the optimized process parameters were more capable and reflected the influence of metallurgical mechanism on the quality index,which were more in line with actual production process requirements.

关键词： customized production quality control multi-objective prediction multi-output support vector regression process parameter optimization

来源：评论

学校读者我要写书评

暂无评论

process parameters optimization of Laser Cladding for HT200 with 316L Coating Based on Response Surface Method

引用

Journal of Wuhan University of Technology(Materials Science) 2024年第6期39卷 1569-1579页

作者： KONG Huaye ZHU Xijing LI Zejun ZHANG Jinzhe LI Zuoxiu School of Mechanical Engineering North University of ChinaTaiyuan 030051China

In order to improve the sealing surface performance of gray cast iron gas gate valves and achieve precise molding control of the cladding layer,as well as to reveal the influence of laser cladding process parameters on the morphology and structure of the cladding layer,we prepared the 316L coating on HT 200 by using Design-Expert software central composite design(CCD)based on response surface *** built a regression prediction model and analyzed the ANOVA with the inspection *** a target cladding layer width of 3.5 mm and height of 1.3 mm,the process parameters were optimized to obtain the best combination of process *** microstructure,phases,and hardness variations of the cladding layer from experiments with optimal parameters were analyzed by the metallographic microscope,confocal microscope,and microhardness *** experimental results indicate that laser power has a significant impact on the cladding layer width,followed by powder feed rate;scan speed has a significant impact on the cladding layer height,followed by powder feed *** HT200 substrate and 316L can metallurgically bond well,and the cladding layer structure consists of dendritic crystals,columnar crystals,and equiaxed crystals in *** optimal process parameter combination satisfying the morphology requirements is laser power(A)of 1993 W,scan speed(B)of 8.949 mm/s,powder feed rate(C)of 1.408 r/min,with a maximum hardness of 1564.3 HV0.5,significantly higher than the hardness of the HT200 substrate.

关键词： HT200 laser cladding 316L stainless steel response surface methodology process parameter optimization

来源：评论

学校读者我要写书评

暂无评论

Study on parameter optimization of laser cladding Fe60 based on GA-BP neural network

引用

JOURNAL OF ADHESION SCIENCE AND TECHNOLOGY 2023年第18期37卷 2556-2586页

作者： Li, Chang Jia, Tenghui Han, Xing Jiang, Xiansheng Univ Sci & Technol Liaoning Sch Mech Engn & Automat Anshan Peoples R China

Laser cladding is an emerging surface modification technology, which can effectively improve the wear and corrosion resistance of the workpiece surface. It is widely used in aviation, aerospace, iron and steel metallurgy and other fields. The parameters of the cladding process directly affect the performance and quality of the cladding layer. Optimizing process parameters is significant to prepare high-quality cladding layers. In this study, a three-dimensional numerical model of C45 laser cladding Fe60 powder was established, and the transient evolution law of the cladding temperature, flow rate and thermal stress was revealed. On this basis, the parameters of the cladding process were changed to perform multiple sets of numerical calculations and obtain sample data. A GA-BP neural network model was established to determine the optimal parameters of the cladding process. The cladding temperature was tracked and observed with the thermal imager, and the cladding morphology was observed by the scanning electron microscope, which verified the effectiveness of the numerical model. Calculations show that the maximum temperature of the molten pool reaches 3240 K at 2.5 s, and the isotherm shows a 'comet tail' distribution. The maximum flow rate of the molten pool is 0.33 m/s, and the thermal stress in cladding reaches 367 MPa. Based on the optimized process parameters determined by the GA-BP neural network, the laser power is 1076.41 W, the spot radius is 2.61 mm, and the scanning speed is 6.05 mm/s. This research provides a significant theoretical basis for effectively improving the cladding quality.

关键词： Laser cladding Fe60 GA-BP neural network process parameter optimization

来源：评论

学校读者我要写书评

暂无评论

Hot cracking during the fabrication of Inconel 625/stainless steel 308 L functionally graded material by dual-wire arc additive manufacturing

引用

JOURNAL OF MANUFACTURING processES 2022年 82卷 461-473页

作者： Li, Tianxu Wang, Zhijiang Hu, Shengsun Yang, Zhenwen Wang, Ying Tianjin Univ Sch Mat Sci & Engn Tianjin Key Lab Adv Joining Technol Tianjin 300354 Peoples R China Tianjin Univ Sch Mat Sci & Engn Tianjin 300354 Peoples R China

In the present work, dual-wire arc additive manufacturing was used to fabricate the functionally graded material (FGM) with a smooth composition transition from 100 wt% Inconel 625 (IN625) to 100 wt% stainless steel 308 L, whose composition transition was along the torch-travelling direction. With such a composition transition, po-sition with the weakest mechanical properties could be clearly and easily located in the composition gradient path, allowing the issue to be fully exposed. During the fabrication, cracks were found near the position with IN625 of 20 wt%. By analyzing the fracture morphology and crack location, they were judged as liquation cracks, which belong to hot cracks. The segregation and aggregation of carbides at the grain boundary formed the low -melting-temperature eutectic phase, which is liquefied into the liquid film by the heating of subsequent layers and is then pulled apart by residual stress. According to the experimental results and the calculations of phase diagrams, the cracks always appeared near the position with IN625 of 20 wt%, where the FGM has the highest crack sensitivity. Therefore, it was determined as the weakest position of the FGM and its weakening mechanism could be clarified easier owing to the composition transition, which provides a high possibility to get better mechanical properties if a more targeted optimization method is applied. process parameter optimization re-duces the heat input per unit height to thin dendrites and to alleviate cracking, but there is still a cracking tendency in the weak composition range. The composition gradient path optimization, by removal of the cor-responding composition range, can solve the cracking issue more thoroughly, and the tensile strength and elongation increased by 39.5 % and 221.7 %, respectively, which greatly improved the mechanical properties of the FGM.

关键词： Hot cracking Composition gradient path optimization process parameter optimization Dual-wire arc additive manufacturing Functionally graded material

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

μMORE: A microfluidic magnetic oscillation reactor for accelerated parameter optimization in biocatalysis

引用

JOURNAL OF BIOTECHNOLOGY 2016年 231卷 174-182页

作者： Jussen, Daniel Soltner, Helmut Stute, Birgit Wiechert, Wolfgang von Lieres, Eric Pohl, Martina Forschungszentrum Julich IBG Biotechnol 1 D-52425 Julich Germany Forschungszentrum Julich ZEA Engn & Technol 1 D-52425 Julich Germany

Enzymatic parameter determination is an essential step in biocatalytic process development. Therefore higher throughput in miniaturized devices is urgently needed. An ideal microfluidic device should combine easy immobilization and retention of a minimal amount of biocatalyst with a well-mixed reaction volume. Together, all criteria are hardly met by current tools. Here we describe a microfluidic reactor (mu MORE) which employs magnetic particles for both enzyme immobilization and efficient mixing using two permanent magnets placed in rotating cylinders next to the a glass chip reactor. The chip geometry and agitation speed was optimized by investigation of the mixing and retention characteristics using simulation and dye distribution analysis. Subsequently, the mu MORE was successfully applied to determine critical biocatalytic process parameters in a parallelized manner for the carboligation of benzaldehyde and acetaldehyde to (S)-2-hydroxy-1-phenylpropan-1-one with less than 5 mu g of benzoylformate decarboxylase from Pseudomonas putida immobilized on magnetic beads. Here, one run of the device in six parallelized glass reactors took only 2-3 h for an immobilized enzyme with very low activity (similar to 2 U/mg). The optimized parameter set was finally tested in a 10 mL enzyme membrane reactor, demonstrating that the mu MORE provides a solid data base for biocatalytic process optimization. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Biocatalytic process process parameter optimization Microfluidic reactor Magnetic beads C-C bond formation Immobilized enzyme

来源：评论

学校读者我要写书评

暂无评论

Effects of process parameters on the quality of PLA products fabricated by fused deposition modeling (FDM): surface roughness and tensile strength

引用

MATERIALS TESTING 2018年第5期60卷 471-477页

作者： Altan, Mirigul Eryildiz, Meltem Gumus, Beril Kahraman, Yusuf Yildiz Tech Univ Istanbul Turkey Yildiz Tech Univ Mech Testing Istanbul Turkey

This study investigates the effects of process parameters on the quality of products fabricated by fused deposition modeling (FDM), such as surface roughness and tensile strength. Polylactic acid (PLA) samples were built on a FDM machine at various layer thicknesses, nozzle temperature and deposition head velocity. The effect of cooling the samples during the process was also considered. The experimental study was performed according to a mixed type Taguchi L16 orthogonal array. The effectiveness of each parameter was also discussed by an analysis of variance (ANOVA). The tensile strength results were compatible with the optical images of the fracture surfaces while the surface roughness results were compatible with the surface topography of the parts along the thickness. The two dominant quality characteristics were found to be layer thickness and deposition head velocity. Lower layer thickness values yielded higher tensile strength and lower surface roughness. Use of a cooling fan and nozzle temperature were found to be the least effective parameters. Finally, the results indicated that tensile strength and surface quality of the FDM samples improved about 25%, and 12 %, respectively at optimal process conditions.

关键词： Fused deposition modeling part quality surface roughness tensile strength process parameter optimization

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：