3D Printing and Additive Manufacturing最新文献_第3页

Deep Learning-Based Automated Optical Inspection System for the Additive Manufacturing of Diamond Tools 基于深度学习的金刚石工具增材制造自动光学检测系统

IF 3.1 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-22 DOI: 10.1089/3dp.2023.0208

Zenghui Feng, Chenyao Dong, Xiangxi Xu, Yibo Liu, Shuangxi Wang

引用次数: 0

Magnetic Stimulation for Programmed Shape Morphing: Review of Four-Dimensional Printing, Challenges and Opportunities 磁刺激编程塑形：回顾四维打印、挑战与机遇

IF 3.1 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-21 DOI: 10.1089/3dp.2023.0198

Vera G. Kortman, Ellen de Vries, J. Jovanova, A. Sakes

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Effects of Acetyl Tributyl Citrate on the Mechanical Properties, Abrasion Resistance, and Cytotoxicity of the Light-Cured 3D Printing Polyurethane Resins 柠檬酸乙酰三丁酯对光固化 3D 打印聚氨酯树脂的机械性能、耐磨性和细胞毒性的影响

IF 3.1 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-14 DOI: 10.1089/3dp.2023.0161

Hsuan Chen, Chih-Hsin Lin, Shu-Wen Hung, Shyh-Yuan Lee, Yuan-Min Lin

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The Effect of Thermal Cycle on Hot Cracking Evolution and Formation Mechanism in Thin Wall, Single Layer, and Cubic Samples of High-Strength Al-Cu-Mg-Mn Alloys Fabricated by Laser Powder Bed Fusion 热循环对激光粉末床熔融法制造的高强度铝-铜-镁-锰合金薄壁、单层和立方体样品中热裂纹演变及形成机理的影响

IF 3.1 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-13 DOI: 10.1089/3dp.2023.0167

Xiaojia Nie, Fei Peng, Zhiheng Hu, Yang Qi, Haihong Zhu, Hu Zhang

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Surface Structure Modification in Fused Filament Fabrication (FFF) Multi-Material Printing for Medical Applications: Printing of a Hand Prosthesis 用于医疗应用的熔融丝制造（FFF）多材料打印中的表面结构改性：手部假肢的打印

IF 3.1 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-12 DOI: 10.1089/3dp.2023.0210

E. Brancewicz-Steinmetz, Natalia Słabęcka, Patryk Śniarowski, Katarzyna Wybrzak, Jacek Sawicki

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Detecting Selective Laser Melting Beam Power from Ultrasonic Temporal and Spectral Responses of Phononic Crystal Artifacts Toward In-Situ Real-Time Quality Monitoring 从超声波的时相和频谱响应检测选择性激光熔化光束功率，实现原位实时质量监测

IF 3.1 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-12 DOI: 10.1089/3dp.2023.0063

E. H. Rozin, Tipu Sultan, Hossein Taheri, Cetin Cetinkaya

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Energy Consumption Prediction of Additive Manufactured Tensile Strength Parts Using Artificial Intelligence 利用人工智能预测增材制造拉伸强度部件的能耗

IF 3.1 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-11 DOI: 10.1089/3dp.2023.0189

O. Ulkir, Mehmet Said Bayraklilar, M. Kuncan

引用次数: 0

Additive Manufacturing and Composite Materials for Marine Energy: Case of Tidal Turbine. 用于海洋能源的快速成型制造和复合材料：潮汐涡轮机案例。

IF 2.3 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-01 Epub Date: 2023-12-11 DOI: 10.1089/3dp.2021.0194

Marwane Rouway, Mostapha Tarfaoui, Nabil Chakhchaoui, Lhaj El Hachemi Omari, Fouzia Fraija, Omar Cherkaoui

{"title":"Additive Manufacturing and Composite Materials for Marine Energy: Case of Tidal Turbine.","authors":"Marwane Rouway, Mostapha Tarfaoui, Nabil Chakhchaoui, Lhaj El Hachemi Omari, Fouzia Fraija, Omar Cherkaoui","doi":"10.1089/3dp.2021.0194","DOIUrl":"10.1089/3dp.2021.0194","url":null,"abstract":"The global trend in additive manufacturing is the technology of three-dimensional (3D) printing with a high potential to avoid some of the weaknesses of conventional fabrication techniques. This new technology has been used to manufacture small tidal and wind turbines. In isolated areas, small turbines can be manufactured and assembled on-site for green energy production. The purpose of this document is to evaluate the thermomechanical behavior of a printed tidal turbine using Digimat-AM (Additive Manufacturing) with fused filament fabrication method. The finite element computes the mechanical deflection, temperature, residual stresses, and warpage fields of the printed part. The composites used during printing are thermoplastic polymers (acrylonitrile butadiene styrene, polyamide 6 [PA6], polyamide 12 [PA12], and polyetherimide [PEI]) reinforced with carbon and glass fillers in the form of fibers and beads (CF/GF and CB/GB). Through the simulation, one could show that the blade printed with PEI-CB/CF has excellent mechanical performance of low mechanical deflection and warpage, compared to PA6-CB/CF. In addition, the fiber-shaped fillers are better than the bead-shaped ones for the 3D printing process. In general, this study has shown the potential and feasibility of 3D printing as an excellent opportunity in the fabrication of small blades in the future, but more studies are required to understand this potential.","PeriodicalId":54341,"journal":{"name":"3D Printing and Additive Manufacturing","volume":"10 6","pages":"1309-1319"},"PeriodicalIF":2.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10726194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138809876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finding Ideal Parameters for Recycled Material Fused Particle Fabrication-Based 3D Printing Using an Open Source Software Implementation of Particle Swarm Optimization. 利用粒子群优化的开源软件实现，为基于回收材料熔融粒子制造的三维打印找到理想参数。

IF 3.1 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-01 Epub Date: 2023-12-11 DOI: 10.1089/3dp.2022.0012

Shane Oberloier, Nicholas G Whisman, Joshua M Pearce

{"title":"Finding Ideal Parameters for Recycled Material Fused Particle Fabrication-Based 3D Printing Using an Open Source Software Implementation of Particle Swarm Optimization.","authors":"Shane Oberloier, Nicholas G Whisman, Joshua M Pearce","doi":"10.1089/3dp.2022.0012","DOIUrl":"10.1089/3dp.2022.0012","url":null,"abstract":"As additive manufacturing rapidly expands the number of materials including waste plastics and composites, there is an urgent need to reduce the experimental time needed to identify optimized printing parameters for novel materials. Computational intelligence (CI) in general and particle swarm optimization (PSO) algorithms in particular have been shown to accelerate finding optimal printing parameters. Unfortunately, the implementation of CI has been prohibitively complex for noncomputer scientists. To overcome these limitations, this article develops, tests, and validates PSO Experimenter, an easy-to-use open-source platform based around the PSO algorithm and applies it to optimizing recycled materials. Specifically, PSO Experimenter is used to find optimal printing parameters for a relatively unexplored potential distributed recycling and additive manufacturing (DRAM) material that is widely available: low-density polyethylene (LDPE). LDPE has been used to make filament, but in this study for the first time it was used in the open source fused particle fabrication/fused granular fabrication system. PSO Experimenter successfully identified functional printing parameters for this challenging-to-print waste plastic. The results indicate that PSO Experimenter can provide 97% reduction in research time for 3D printing parameter optimization. It is concluded that the PSO Experimenter is a user-friendly and effective free software for finding ideal parameters for the burgeoning challenge of DRAM as well as a wide range of other fields and processes.","PeriodicalId":54341,"journal":{"name":"3D Printing and Additive Manufacturing","volume":"10 6","pages":"1287-1300"},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10726196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138809884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning-Based Operational State Recognition and Compressive Property Prediction in Fused Filament Fabrication. 基于机器学习的熔丝制造中的运行状态识别和压缩特性预测。

IF 2.3 4区工程技术

3D Printing and Additive Manufacturing Pub Date : 2023-12-01 Epub Date: 2023-12-11 DOI: 10.1089/3dp.2021.0185

Yongxiang Li, Guoning Xu, Wei Zhao, Tongcai Wang, Haochen Li, Yifei Liu, Gong Wang

{"title":"Machine Learning-Based Operational State Recognition and Compressive Property Prediction in Fused Filament Fabrication.","authors":"Yongxiang Li, Guoning Xu, Wei Zhao, Tongcai Wang, Haochen Li, Yifei Liu, Gong Wang","doi":"10.1089/3dp.2021.0185","DOIUrl":"10.1089/3dp.2021.0185","url":null,"abstract":"3D printing has exhibited significant potential in outer space and medical implants. To use this technology in the specific high-value scenarios, 3D-printed parts need to satisfy quality-related requirements. In this article, the influence of the filament feeder operating states of 3D printer on the compressive properties of 3D-printed parts is studied in the fused filament fabrication process. A machine learning approach, back-propagation neural network with a genetic algorithm (GA-BPNN) optimized by k-fold cross-validation, is proposed to monitor the operating states and predict the compressive properties. Vibration and current sensors are used in situ to monitor the operating states of the filament feeder, and a set of features are extracted and selected from raw sensor data in time and frequency domains. Results show that the operating states of the filament feeder significantly affected the compressive properties of the fabricated samples, the operating states were accurately recognized with 96.3% rate, and compressive properties were successfully predicted by the GA-BPNN. This proposed method has the potential for use in industrial applications after 3D printing without requiring any further quality control.","PeriodicalId":54341,"journal":{"name":"3D Printing and Additive Manufacturing","volume":"1 1","pages":"1347-1360"},"PeriodicalIF":2.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10726200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60697273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0