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Influence of pulsed current GTAW-WAAM process parameters on the single layer bead geometry and multi bead multi-layer deposition of a nickel-based superalloy

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MATERIALS TODAY COMMUNICATIONS 2024年 39卷

作者： Kumar, M. D. Barath Arivazhagan, N. Tofil, Szymon Andersson, Joel Kozak, Jindrich Manikandan, M. Vellore Inst Technol Sch Mech Engn Vellore 632014 India Kielce Univ Technol Fac Mechatron & Mech Engn Laser Res Ctr Ave Tysiaclecia Panstwa Polskiego 7 PL-25314 Kielce Poland Univ West Dept Engn Sci S-46186 Trollhattan Sweden VSB Tech Univ Ostrava Fac Mech Engn Ostrava Czech Republic

Wire + arc additive manufacturing (WAAM) is a state-of-the-art and highly efficient technique utilized to produce near net-shaped products on a large scale, employing a layer-by-layer approach. This research used pulsed current mode welding for single-layer bead-on-plate experiments to optimise the process parameters for WAAM of Hastelloy C-276. The effect of process parameters including pulsed current, pulsed frequency, and pulse duty cycle was systematically investigated on the weld appearance, depth of penetration, layer width, and layer height. The optimisation of single layer experimental runs was conducted using Box-Behnken designs (BBD) and the response surface method to construct several regression models. An analysis of variance was employed to validate the accuracy of both the measured and generated models. The BBD results indicate that interactions have a more significant impact on the peak current parameter than the resulting impact of pulse duty cycle and frequency. Validation tests were performed on the model with the optimal process variables that were identified, and its mechanical and metallurgical properties were analysed. Macrostructure and microstructural analysis of the single layer showed that the specified process parameters led to acceptable base metal fusion and bead is free from cracking. There was a considerable decrease in elemental segregation while using the pulse mode technique. Finer grain structure and reduced microsegregation enhance the hardness. Further residual stress (RS) at weld bead and base plate was 213 MPa and -240 MPa. Nonhomogeneous heat transfer during welding affects RS compressive and tensile characteristics. WAAM printing quality requires precise control of LH, LW, and DOP. This research aimed to propose suitable parameter values for manufacturing WAAM component for usage in chemical processing, nuclear, marine, and industrial settings by using unique pulsing features.

关键词： Wire + arc additive manufacturing process parameter optimization Layer geometry Nickel based superalloy Microstructure Residual stress

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optimization of heat treatment deformation control process parameters for face-hobbed hypoid gear using FEA-PSO-BP method

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JOURNAL OF MANUFACTURING processES 2024年 117卷 40-58页

作者： Sun, Beier Liu, Huaming Tang, Jinyuan Rong, Shifeng Liu, Yashuai Jiang, Weizhe Cent South Univ State Key Lab Precis Mfg Extreme Serv Performance Changsha 410017 Peoples R China Cent South Univ Coll Mech & Elect Engn Changsha 410017 Peoples R China

The face-hobbed hypoid gears are manufactured from low-carbon alloy steel and possess a complex geometric profile. Typically, carburizing and quenching are employed to enhance the surface hardness of the gear teeth. However, deformation unavoidably occurs during the processes of carburizing, quenching, and tempering. This deformation leads to a reduction in the contact precision of the gear pair and an increase in noise during meshing. To effectively control the thermal deformation of face-hobbed hypoid gear and ensure their accuracy, a computational model for carburizing, quenching, and low-temperature tempering processes of face-hobbed hypoid gear was established using the finite element method (FEM) in this study. The computational model simulated the temperature field, phase field, and stress-strain field during the heat treatment process, and the accuracy of hardness, phase transformation structure, and deformation in the model was confirmed by experimental verification. The investigation, conducted through single-factor experiments, aimed to study the impact of process parameters such as carburizing potential, quenching temperature, oil temperature, and tempering temperature on heat treatment deformation. The study results demonstrated that the quenching temperature has a relatively significant influence on the tooth form error. To control and reduce the thermal deformation error on the tooth surface, a back propagation (BP) neural network was constructed using single factor experiments and orthogonal experiments. The optimal process parameters were obtained through particle swarm optimization (PSO) algorithm. Under the optimal process parameters, the average tooth form error of the working surface was reduced by 2.3 mu m (14 %), and the range was reduced by 6.2 mu m (13 %). The FEA-PSO-BP method for optimizing process parameters to reduce thermal deformation in heat treatment offers a set of parameters that minimize the thermal deformation. This method can be

关键词： Computer simulation Face-hobbed hypoid gear Heat treatment deformation Multi -field coupling Deformation prediction process parameter optimization

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Optimizing economics of machining for LM25Al/VC composite material using analytical modeling, deep neural network and GRA coupled with RSM

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SCIENTIFIC REPORTS 2025年第1期15卷 1-22页

作者： Tolcha, Mesay Alemu Lemu, Hirpa Gelgele Adugna, Yosef Wakjira Jimma Univ Jimma Inst Technol Jimma Ethiopia Univ Stavanger Dept Mech & Struct Engn N-4036 Stavanger Norway

This study investigates the machinability of a novel LM25 aluminum alloy reinforced with vanadium carbide composite material (LM25Al/VC) using computer numerical control (CNC) lathe operation. By optimizing CNC lathe process parameters such as depth of cut, feed rate, and cutting speed, the aim is to maximize material removal rate, minimize surface roughness, reduce power consumption, and optimize costs. The study employs analytical modeling, deep neural networks (DNN), and grey relational grade (GRA) coupled with response surface methodology (RSM) for performance evaluation. The effectiveness of these methods was compared using four objective verification mechanisms. In this case, the DNN technique delivered superior results among the methods considered. Additionally, new analytical models and DNN programming were developed in this work to predict machining costs, power consumption, material removal rate, and surface finish quality. These findings contribute to creating energy-efficient, cost-effective machining techniques and promote sustainable practices in the manufacturing industry.

关键词： Analytical modeling Deep neural network Vanadium carbide composite process parameter optimization

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Prediction and optimization method for welding quality of components in ship construction

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Liu, Jinfeng Cheng, Yifa Jing, Xuwen Liu, Xiaojun Chen, Yu Jiangsu Univ Sci & Technol Zhenjiang 212100 Jiangsu Peoples R China Southeast Univ Nanjing 211189 Peoples R China

Welding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. The elements of welding process monitoring are also determined based on the feature dimensionality reduction method. To improve the accuracy of welding quality prediction, the prediction model is constructed by fusing the adaptive simulated annealing, the particle swarm optimization, and the back propagation neural network algorithms. Finally, the effectiveness of the prediction method is verified through 74 sets of plate welding experiments, the prediction accuracy reaches over 90%.

关键词： Quality prediction Components welding Welding quality process parameter optimization

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Optimizing additive manufacturing parameters for graphene-reinforced PETG impeller production: A fuzzy AHP-TOPSIS approach

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RESULTS IN ENGINEERING 2024年 24卷

作者： Raja, S. Praveenkumar, V Rusho, Maher Ali Yishak, Simon Chennai Inst Technol Ctr sustainable Mat & Surface Metamorphosis Chennai 600069 Tamilnadu India Amrita Vishwa Vidyapeetham Dept Mech Engn Amritapuri 690525 India Univ Colorado Engn Engn Management Lockheed Matin Engn Management Boulder CO 80308 USA Arba Minch Univ Coll Engn & Agroind Technol Sawla Campus Arba Minch 40003 Ethiopia

Additive manufacturing, particularly 3D printing, has transformed production by enabling precise, layer-by-layer construction with minimal material waste. This study aims to optimize the mechanical properties and production efficiency of impellers manufactured using Graphene-Reinforced Polyethylene Terephthalate Glycol (G-PETG) filament. By employing the Fuzzy Analytic Hierarchy process (FAHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), we identified optimal 3D printing parameters. The results showed that a 65 % infill density, 0.20 mm layer height, 50 mm/s printing speed, 90 degrees C platform temperature, 240 degrees C extruder temperature, and 90 mm/s traverse speed led to a 15 % improvement in tensile strength and a 12 % reduction in production time compared to baseline settings. Additionally, the impellers produced demonstrated superior surface finish and structural integrity, making them suitable for high-performance applications. These findings underscore the importance of parameter optimization in enhancing the performance of 3D-printed components, particularly for applications requiring high mechanical strength and precision.

关键词： Additive manufacturing process parameter optimization Fuzzy AHP-TOPSIS Composite thermoplastic polymer Sustainable materials Mechanical property Fused deposition modeling Material performance Impeller application

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

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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

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Design workflow to facilitate support removal in Metal Powder Bed Fusion

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第11-12期129卷 5303-5317页

作者： Liu, Jun Lim, Keng Hui Liu, Zhong Hong Chan, Soon Mei Tan, Kai Lee ASTAR Inst High Performance Comp IHPC 1 Fusionopolis Way16-16 Connexis Singapore 138632 Singapore ASTM Int Singapore Singapore Temasek Polytech Singapore Singapore ASTAR Sci & Engn Res Council SERC 1 Fusionopolis Way Singapore 138632 Singapore Duke NUS Med Sch Natl Univ Singapore Singapore Singapore

In this work, we introduce a novel technique that optimizes the design and properties of support structures for Powder Bed Fusion-Laser-Based Metal (PBF-LB/M) processes. The goal is to significantly ease support removal during post-processing without compromising print quality. We propose an additive manufacturing (AM) workflow that streamlines the end-to-end AM process through the creation and integration of innovative support systems, modeling software, and efficient support removal techniques. This approach enhances productivity and minimizes trial and error between design and 3D printing. Our key concept revolves around developing a "soft" support system, which can be easily removed using sandblasting. Compared to manual or machining removal, this method substantially reduces cost and time, particularly in areas with limited or no access. For locations with steep overhangs and large cross-sectional areas, the traditional strong support system can be implemented as a "hard" support. The proposed workflow allows for the development of a hybrid system combining hard and soft supports, effectively manufacturing parts with minimal design constraints. The workflow begins from a standard operating procedure (SOP) for evaluating the appropriate printing parameters to achieve a support that can be soft enough to be sandblasted for removal, yet sufficient strong to support the overhang part structure and prevent distortion. A simulation platform is developed to assess the ideal placement and combination of the hybrid support design, achieving a balance between post-processing cost and effort, and print quality. The development of this solution truly reflects the advantage of metal 3D printing technology, i.e., design freedom.

关键词： Additive manufacturing Hybrid support structure process parameter optimization Inherent strain FEM simulation

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Parametric optimization for dimensional correctness of 3D printed part using masked stereolithography: Taguchi method

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RAPID PROTOTYPING JOURNAL 2023年第1期29卷 166-184页

作者： Borra, N. Dhanunjayarao Neigapula, Venkata Swamy Naidu Natl Inst Technol Raipur Dept Mech Engn Raipur Madhya Pradesh India Vignans Inst Informat Technol A Dept Mech Engn Visakhapatnam Andhra Pradesh India

Purpose Masked stereolithography (MSLA) or resin three-dimensional (3D) printing is one of the most extensively used high-resolution additive manufacturing technologies. Even though, the quality of 3D printing is determined by several factors, including the equipment, materials and slicer. Besides, the layer height, print orientation and exposure time are important processing parameters in determining the quality of the 3D printed green state specimen. The purpose of the paper is to optimize the printing parameters of the Masked Stereolithography apparatus for its dimensional correctness of 3D printed parts using the Taguchi method. Design/methodology/approach The acrylate-based photopolymer resin is used to produce the parts using liquid crystal display (LCD)-type resin 3D printer. This study is mainly focused on optimizing the processing parameters by using Taguchi analysis, L-9 orthogonal array in Minitab software. Analysis of variance (ANOVA) was performed to determine the most influencing factors, and a regression equation was built to predict the best potential outcomes for the given set of parameters and levels. The signal-to-noise ratios were calculated by using the smaller the better characteristic as the deviations from the nominal value should be minimum. The optimal levels for each factor were determined with the help of mean plots. Findings Based on the findings of ANOVA, it was observed that exposure time plays an important role in most of the output measures. The model's goodness was tested using a confirmation test and the findings were found to be within the confidence limit. Also, a similar specimen was printed using the fused filament fabrication (FFF) technique;it was compared with the quality and features of MSLA 3D printing technology. Practical implications The study presents the statistical analysis of experimental results of MSLA and made a comparison with FFF in terms of dimensional accuracy and print quality. Originality/value Many previou

关键词： process parameter optimization Design of experiments Three-dimensional printing Taguchi Regression analysis Masked stereolithography

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Modeling and optimizing the specific cutting energy of medium density fiberboard during the helical up-milling process

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WOOD MATERIAL SCIENCE & ENGINEERING 2023年第2期18卷 464-471页

作者： Li, Rongrong He, Chujun Xu, Wei Wang, Xiaodong (Alice) Nanjing Forestry Univ Coinnovat Ctr Efficient Proc & Utilizat Forest Re Nanjing 210037 Jiangsu Peoples R China Laval Univ Dept Wood & Forest Sci Quebec City PQ Canada

Due to the flexible motion characteristics, helical milling could achieve high surface quality and cutting stability. The effects of input parameters on specific cutting energy (SCE) during the medium density fiberboard (MDF) helical up-milling process were studied. Results of analysis of variance showed that the helical angle and depth of milling had extremely significant effects on SCE. SCE increased with increased helical angle, but decreased with increased milling depth. The impact of the rotation speed of the main shaft was non-significant. Due to the highest R-2 value, a quadratic model was selected to establish the relationship between input parameters and SCE. The relative errors between predicting results and confirmatory test results were minimal, which meant that the model had high predicting accuracy. Under the selected input parameters, the optimized parameters were 54 degrees, 5500 r/min, 1.5 mm for helical angle, the rotation speed of the main shaft, depth of milling, respectively. Although the arithmetic average of absolute roughness (Ra) and mean peak-to-valley height (Rz) increased about 58.3% and 46.2%, respectively, under the optimal milling parameters, the optimization was feasible at the initial rough machining stage. These results will be beneficial in guiding the selection of processing parameters to achieve reducing SCE.

关键词： Power consumption specific cutting energy helical up-milling medium density fiberboard response surface methodology process parameter optimization

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optimization Method for the PVB Resin process parameters Based on the Quality Index Prediction Model

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2025年第1期58卷

作者： Xu, Tongming Zhang, Haiming Li, Bozhao Zhao, Guobo Lu, Haiyang Li, Weilong Wang, Siyuan Shen, Zhaoran Inspur Genersoft Co Ltd Jinan 250101 Peoples R China Shandong Univ Jinan 250100 Peoples R China

This paper presents an optimization method for the parameters of the synthesis process of polyvinyl butyral (PVB) resin based on a quality index prediction model. First, a PVB resin quality index prediction model is established based on a backpropagation (BP) neural network. The model uses 17 input parameters and predicts 2 output parameters (the melt index and viscosity). Moreover, considering the reaction mechanism, 6 key input parameters are analyzed, and their weights are improved in the model. Then, based on the constructed quality index prediction model, an optimization method for the PVB resin process parameters is developed using the nondominated sorting genetic algorithm II (NSGA-II). Finally, experiments are performed to verify the effectiveness of the proposed method. The results show that under a similar melt index (with a deviation of 3.8%), the viscosity increased by 15.4% after optimization, and under a similar viscosity (with a deviation of 3.0%), the melt index increased by 88.6% after *** proposed method can be beneficial for industrial production of PVB resins that can meet more requirements.

关键词： process parameter optimization Quality index prediction BP neural network Reaction mechanism PVB resin

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