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Experimental research on optimization of compression molding process parameters of pineapple rind residue

International Journal of Agricultural and Biological Engineering 2021年第3期14卷 221-227页

作者： Kunpeng Tian Bin Zhang Jicheng Huang Haolu Liu Cheng Shen Xianwang Li Qiaomin Chen Nanjing Institute of Agricultural Mechanization Ministry of Agriculture and Rural AffairsNanjing 210014China School of Mechanical Engineering Southeast UniversityNanjing 211189China College of Mechanical and Electrical Engineering Hohai UniversityChangzhou 213022JiangsuChina Key Laboratory of Modern Agricultural Equipment and Technology Ministry of EducationZhenjiang 212013JiangsuChina

Currently,the process parameters for compression molding of pineapple rind residue are not *** view of this problem,a single die hole compression molding test device was designed in this study,and the force of material in a mold hole was *** the test device,a three-factor three-level orthogonal test was carried out by using the particle size,moisture content,and die hole length-to-diameter ratio of pineapple rind residue as the factors and the particle molding rate,relax density,and specific energy consumption as the *** test results were analyzed by range analysis,variance analysis,and fuzzy comprehensive *** test results show that the main and secondary factors affecting the comprehensive performance of pineapple rind residue compression molding are length-to-diameter ratio,particle size,and moisture *** optimal parameter combination is the material particle size of 6-9 mm,moisture content of 16%,and length-to-diameter ratio of 4:*** best indicators under these conditions are particle molding rate of 97.80%,relax density of 1.32 g/cm,and specific energy consumption of 44.17 J/*** research results can provide a reference for the selection of processing parameters and the design of molding equipment.

关键词： pineapple rind residue compression molding waste utilization pellet forming orthogonal test process parameter optimization

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Design and Parametric optimization of the Injection Molding process Using Statistical Analysis and Numerical Simulation

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processES 2023年第2期11卷 414页

作者： Chen, Jinping Cui, Yanmei Liu, Yuanpeng Cui, Jianfeng Zhengzhou Univ Aeronaut Sch Aerosp Engn Zhengzhou 450046 Peoples R China Shanghai Dianji Univ Sch Mech Engn Shanghai 201306 Peoples R China

Quality control is considered a critical aspect of plastic materials in the injection molding process. Two types of deformations occur during the injection molding process, namely, volumetric shrinkage and warpage. This study aims to optimize the warpage of the polyethylene terephthalate preform (PET) used for the packing of carbonated drinks. PET warpage results in an uneven distribution of material over the wall surface of the preform and causes variation in wall thickness. During the filling operation of carbonated drinks, the preforms are subjected to high pressure at the points where the wall thickness is at a minimum, which induces a high-stress concentration. Under high pressure, the preforms are ruptured at the points where the warpage is at a maximum (stress concentration area), causing wastage of the beverage as well as the preform. In this study, the Taguchi method and analysis of variance (ANOVA) are used to determine the most significant parameters to induce warpage during the molding process. Then, we optimize the process parameters in order to reduce warpage through a numerical approach using SolidWorks Plastics. The result shows that the ambient temperature and melting temperature are the most critical parameters that contribute to the warpage, yielding 42.115% and 41.278%, respectively. Among the 6 parameters considered for this study, the pressure holding time contributes a minimum of 0.5961% to the yielding of the warpage. Overall, by optimizing the process parameters, warpage of the PET preform is minimized by 7.7202%, which helps to reduce wastage of the carbonated drink as well as the rejection rate of the preform during the filling operation. In a nutshell, the quality of the preform is improved.

关键词： PET preform warpage Taguchi method SolidWorks Plastics Minitab process parameter optimization

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Advances and Challenges in Predictive Modeling for Additive Manufacturing of Dissimilar Metals and Complex Alloys

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MATERIALS 2023年第16期16卷 5680-5680页

作者： Adak, Debajyoti Sreeramagiri, Praveen Roy, Somnath Balasubramanian, Ganesh Indian Inst Technol Dept Mech Engn Kharagpur 721302 India Lehigh Univ Dept Mech Engn & Mech Bethlehem PA 18015 USA

We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.

关键词： additive manufacturing microstructure simulation thermal transport melt pool process parameter optimization

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Metal-Metal Bonding process Research Based on Xgboost Machine Learning Algorithm

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POLYMERS 2023年第20期15卷 4085-4085页

作者： Feng, Jingpeng Zhan, Lihua Ma, Bolin Zhou, Hao Xiong, Bang Guo, Jinzhan Xia, Yunni Hui, Shengmeng Cent South Univ State Key Lab Precis Mfg Extreme Serv Performance Changsha 410083 Peoples R China Cent South Univ Light Alloys Res Inst Changsha 410083 Peoples R China

Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused on improving metal-metal bonding performance by applying SLJ experiments, finite element models (FEMs), and the Xgboost machine learning (ML) algorithm. The importance ranking of process parameters on tensile-shear strength (TSS) was evaluated with the interpretation toolkit SHAP (Shapley additive explanations) and it optimized reasonable bonding process parameters. The validity of the FEM was verified using SLJ experiments. The Xgboost models with 70 runs can achieve better prediction results. According to the degree of influence, the process parameters affecting the TSS ranked from high to low are roughness, adhesive layer thickness, and lap length, and the corresponding optimized values were 0.89 mu m, 0.1 mm, and 27 mm, respectively. The experimentally measured TSS values increased by 14% from the optimized process parameters via the Xgboost model. ML methods provide a more accurate and intuitive understanding of process parameters on TSS.

关键词： single-lap joints finite element models Xgboost machine learning algorithm interpretation toolkit SHAP process parameter optimization

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

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SCIENTIFIC REPORTS 2025年第1期15卷 1-9页

作者： Toprak, Inayet Burcu Akdeniz Univ Vocat Sch Tech Sci Antalya Turkiye

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 (R2 = 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： Laser powder bed fusion Hardness optimization Taguchi method Machine learning in additive manufacturing process parameter optimization

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Modeling Method of Small Sample Data and optimization in Ultrasonic Extraction process of Botanicals 33

Modeling Method of Small Sample Data and Optimization in Ult...

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第33届中国控制与决策会议

作者： Qingyu Ma Ruihan Luo Juan Chen College of Information Science and Technology Beijing University of Chemical Technology Systems Engineering Research Insitute

ISBN: (纸本)9781665431293

Aiming at the small sample problem in the dual-frequency ultrasonic extraction process of liquorice,this paper proposes a virtual sample generation method to expand small sample *** method uses Box-Behnken Design(BBD) to design experiments,in order to collect original small sample data for *** on this forecasting model,data is ***,by adding the Firework-Differential Evolution Algorithm with Levy Flight(L-FW-DE),reasonable and effective virtual samples are obtained and selected in the iterative process to expand the sample *** samples,consist of virtual samples and original small samples,are applied to establish a prediction model for dual-frequency ultrasonic extraction of liquiritin from ***,the optimal process parameters are obtained by an optimization ***,the experiment of extracting liquiritin by dual-frequency ultrasound verifies that under these optimized parameters,the extraction rate is the *** of the experiments show that this method can efficiently generate reasonable virtual samples,and the synthetic sample composed of small samples and virtual samples can enhance the accuracy of the prediction *** reliable optimal process parameters which are obtained by the forecasting model can provide guidance for the extraction process under the small sample condition.

关键词： small sample virtual sample synthetic sample plant medicine extraction process parameter optimization

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Anomaly detection in laser powder bed fusion using machine learning: A review

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RESULTS IN ENGINEERING 2023年 17卷

作者： Sahar, Tayyaba Rauf, Muhammad Murtaza, Ahmar Khan, Lehar Asip Ayub, Hasan Jameel, Syed Muslim Ul Ahad, Inam Dawood Univ Engn & Technol Dept Elect Engn Karachi Pakistan Dublin City Univ Adv Mfg Res Ctr I Form Dublin 9 Ireland Dublin City Univ Adv Proc Technol Res Ctr Sch Mech & Mfg Engn Dublin 9 Ireland Dublin City Univ Adv Proc Technol Res Ctr Sch Mech & Mfg Engn Glasnevin Dublin Ireland

Metal Additive Manufacturing (MAM) applications are growing rapidly in high-tech industries such as biomedical and aerospace, and in many other industries including tooling, casting, automotive, oil and gas for production and prototyping. The onset of Laser Powder Bed Fusion (L-PBF) technology proved to be an efficient technique that can convert metal additive manufacturing into a reformed process if anomalies occurred during this process are eliminated. Industrial applications demand high accuracy and risk-free products whereas prototyping using MAM demand lower process and product development time. In order to address these challenges, Machine Learning (ML) experts and researchers are trying to adopt an efficient method for anomaly detection in L-PBF so that the MAM process can be optimized and desired final part properties can be achieved. This review provides an overview of L-PBF and outlines the ML methods used for anomaly detection in L-PBF. The paper also explains how ML methods are being used as a step forward toward enabling the real-time process control of MAM and the process can be optimized for higher accuracy, lower production time, and less material waste. Authors have a strong believe that ML techniques can reform MAM process, whereas research concerned to the anomaly detection using ML techniques is limited and needs attention. This review has been done with a hope that ML experts can easily find a direction and contribute in this field.

关键词： Metal additive manufacturing (MAM) Laser powder bed fusion (L-PBF) Machine learning (ML) process parameter optimization Anomaly detection

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Microstructural evolution of (FeCoNi)85.84Al7.07Ti7.09 high-entropy alloy fabricated by an optimized selective laser melting process

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MATERIALS & DESIGN 2022年 224卷

作者： Ji, Pengcheng Wang, Zhenhui Mu, Yongkun Jia, YanDong Wang, Gang Shanghai Univ Inst Mat Shanghai 200444 Peoples R China Shanghai Univ Zhejiang Inst Adv Mat Jiashan 314100 Peoples R China

High-entropy alloy (HEA) FeCoNiAlTi (FCNAT) systems have been demonstrated to provide high strength while sustaining good ductility, which is of particular interest for the fabrication of structural compo-nents with complex geometries. Meanwhile, selective laser melting (SLM) technology that fabricated metallic material using layer-by-layer melting strategy can meet this demand. However, SLM fabrication technology has not yet been applied in conjunction with FCNAT-HEA systems, and the optimal SLM pro-cessing parameters and the microstructural characteristics of the resulting components remain unknown. The present study addresses this issue by fabricating highly dense (>99.9 %) cubic (FeCoNi)85.84Al7.09Ti7.07 FCNAT-HEA samples. The results demonstrate that the as-fabricated FCNAT-HEA samples possess highly non-equilibrium microstructures that form under the rapid heating and cooling cycles of the SLM process, and include typical overlapping between semi-elliptical melt pools, widely varying grain morphologies with coarse columnar grains and typical equiaxed grains with (001) growth orientation, dislocation network structures with elemental Ti and Al segregation, high -density L21-phase precipitates with sizes on the order of 200-300 nm, and near-spherical nano-sized Al-oxide particles. This research confirms the feasibility of fabricating FCNAT-HEA components by SLM, and therefore provides guidance for fabricating such HEA components with complex geometries. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Selective laser melting High -entropy alloy Dislocation network structure Cellular structure process parameter optimization

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Synergistic optimization of syngas quality and heavy metal immobilization during continuous microwave pyrolysis of sludge: Competitive relationships, reaction mechanisms, and energy efficiency assessment

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JOURNAL OF HAZARDOUS MATERIALS 2022年 438卷 129451页

作者： Lin, Junhao Cui, Chongwei Sun, Shichang Ma, Rui Yang, Weichen Chen, Yi Shenzhen Univ Coll Chem & Environm Engn Shenzhen 518060 Peoples R China Harbin Inst Technol Sch Environm Harbin 150090 Peoples R China Shenzhen Univ Res Ctr Water Sci & Environm Engn Shenzhen 518055 Peoples R China 3688 Nanhai Rd Shenzhen 518060 Peoples R China

To realize the efficient resource utilization of sewage sludge, this work explored the competitive relationship and reaction mechanisms between syngas quality optimization and heavy metals (HMs) immobilization. The results showed that continuous microwave pyrolysis (CMP) technology with an instantaneous temperature increase could shorten the pyrolysis time, and the biogas yield and syngas concentration reached 51.68 wt% and 83.6 vol %, respectively. Although a higher pyrolysis (750 degrees C) temperature could optimize the syngas quality, the HMs immobilization efficiency was reduced due to the deep pyrolysis of the biochar. The moderate pyrolysis temperature (650 degrees C) facilitated the rapid formation of biochar with abundant surface functional groups and pore structure, thus enhancing HMs immobilization. Furthermore, the HMs could also form more stable crystalline compounds with inorganic components (SiO2, Al2O3, inorganic sulfur). By optimizing the process parameters, the risk factor of HMs in the sludge decreased from 117.36 to 62.5 while obtaining high-quality syngas. The energy utilization efficiency of microwave pyrolysis also increased significantly from 11.20% to 82.01%. This work provided new insight into the efficient resource utilization and environmentally friendly treatment of sludge, and demonstrated that CMP technology has significant potential for future industrial applications as an alternative to traditional pyrolysis.

关键词： Sewage sludge Continuous microwave pyrolysis process parameter optimization High-quality syngas Heavy metals immobilization

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Numerical simulations and optimization of foam filled free-curved surface composite part

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MATERIALS RESEARCH EXPRESS 2022年第5期9卷 055602-055602页

作者： Chen, Siyu Jing, Xishuang Xie, Xin Zhang, Chengyang Xie, Fubao Beihang Univ Beijing Peoples R China Beihang Hangzhou Innovat Inst Yuhang Hangzhou Peoples R China Beihang Univ Key Lab Intelligent Mfg Technol Aeronaut Adv Equi Beijing Peoples R China

The purpose of this study is to predict the curing deformation of a foam-filled free-curved surface composite part and reduce the occurrence of curing deformation through process optimization. This research introduces a finite element simulation method and an optimization method for forming parameters for a foam-filled free-curved surface composite part. Meanwhile, sample manufacturing experiments and a comparative analysis between the simulation and actual objects were conducted. The results showed that finite element simulation analysis could effectively predict the curing deformation of the composite part. After the optimization simulation analysis of the molding process parameters, it was found that the curing deformation of the foam-filled free-curved surface composite part could be as small as 2.30 mm.

关键词： composite materials curing deformation finite element analysis compression molding process parameter optimization

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