Manufacturing Letters最新文献_第7页

Characterization of oscillatory magnetic field-assisted finishing of directed energy deposition NASA HR-1 integral channels 振荡磁场辅助整理定向能量沉积 NASA HR-1 积分通道的特征

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.085

Kateland Hutt , Justin Rietberg , Paul Gradl , Hitomi Yamaguchi

{"title":"Characterization of oscillatory magnetic field-assisted finishing of directed energy deposition NASA HR-1 integral channels","authors":"Kateland Hutt , Justin Rietberg , Paul Gradl , Hitomi Yamaguchi","doi":"10.1016/j.mfglet.2024.09.085","DOIUrl":"10.1016/j.mfglet.2024.09.085","url":null,"abstract":"<div><div>Additive manufacturing (AM), such as directed energy deposition (DED), enables fabrication of complex geometries for critical parts at near-net shape, but creates a need for post-processing to achieve desired geometry and performance. In particular, parts made using DED are sometimes printed with a high initial surface roughness, requiring post-processing to meet application-dependent requirements. Magnetic field-assisted finishing (MAF), in which a magnetic polishing tool is manipulated by magnetic force and generates relative motion against a target surface, has been applied to smooth AM parts. An advantage of MAF is that the magnetically manipulated polishing tools can finish both external part surfaces and part interiors. In this paper, an oscillating magnetic polishing tool is proposed to smooth the inner surfaces of rectangular NASA HR-1 alloy channels made using DED. Because effective tool motion allows reduction of surface roughness and waviness, parameters that control polishing-tool motion are of great interest. This paper describes three parameters that control polishing-tool motion: number of polishing tools, magnetic field, and abrasive slurry. The effects of tool motion on the polishing characteristics are demonstrated, showing that the roughness of the interior channel surface can be reduced from several tens of micron to a sub-micron level.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 670-678"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A qualitative validation of an in-situ monitoring system for EHD inkjet printing via laser diffraction 通过激光衍射对 EHD 喷墨打印原位监测系统进行定性验证

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.029

Xuepeng Jiang, Pengyu Zhang, Hantang Qin

引用次数: 0

Optimization and prediction of additively manufactured PLA-PHA biodegradable polymer blend using TOPSIS and GA-ANN 利用 TOPSIS 和 GA-ANN 对添加式制造的聚乳酸--羟乙基苯酚可生物降解聚合物混合物进行优化和预测

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.099

Shafahat Ali , Vijayant Mehra , Abdelkrem Eltaggaz , Ibrahim Deiab , Salman Pervaiz

{"title":"Optimization and prediction of additively manufactured PLA-PHA biodegradable polymer blend using TOPSIS and GA-ANN","authors":"Shafahat Ali , Vijayant Mehra , Abdelkrem Eltaggaz , Ibrahim Deiab , Salman Pervaiz","doi":"10.1016/j.mfglet.2024.09.099","DOIUrl":"10.1016/j.mfglet.2024.09.099","url":null,"abstract":"<div><div>Recent years have seen the proliferation of fused deposition modeling (FDM) as a means of manufacturing biodegradable products, for different applications such as rigid packaging, agricultural and biomedical. Blends of Polyhydroxyalkanoates (PHA) and polylactic acid (PLA) have been investigated to ascertain their prospective applications through FDM. This paper includes three parameters that affect the build process: layer height, nozzle temperature, and flow rate. 3D printed PLA/PHA can be characterized mechanically, and process parameters can be optimized to maximize design functionality. The experimental setup utilized a Taguchi L9 design, and TOSPIS was employed to optimize the output results. Using TOPSIS analysis, 0.2 mm layer thickness, 195 °C nozzle temperature, and 100 % flow rate were found to be the most optimal initiation parameters. The Taguchi analysis was used to analyze the output responses, and it was found that layer height had the greatest influence on mechanical properties, followed by flow rate and nozzle temperature. The percentage elongation at break has been improved significantly by adding PHA i.e., 170 % compared to PLA (5–10 %). This paper presents a framework for in-depth mechanical characterization of PLA-PHA 3D-printed parts, along with methods for optimizing process parameters to achieve optimal performance, as well as tools for modeling output responses using GA-ANN with an accuracy of 95 %.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 795-802"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive models for 3D inkjet material printer using automated image analysis and machine learning algorithms 利用自动图像分析和机器学习算法建立三维喷墨材料打印机的预测模型

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.101

Mutha Nandipati, Michael Ogunsanya, Salil Desai

{"title":"Predictive models for 3D inkjet material printer using automated image analysis and machine learning algorithms","authors":"Mutha Nandipati, Michael Ogunsanya, Salil Desai","doi":"10.1016/j.mfglet.2024.09.101","DOIUrl":"10.1016/j.mfglet.2024.09.101","url":null,"abstract":"<div><div>Additive manufacturing (AM) is a smart manufacturing process to fabricate components with high precision, minimal post-processing, and increased component complexity in a variety of materials. This research focuses on developing automated image analysis and predictive models for a widely used 3D material inkjet printing (IJP) process. The interplay of four input process parameters, which include frequency, voltage, temperature, and meniscus vacuum, on the output metrics of the inkjet printer was evaluated using statistical measures (ANOVA). Droplet types were classified as no drop, satellite drop, and normal drop using four machine learning classifiers, including random forest, support vector classifier, k-nearest neighbor, and decision trees. Hyperparameter tuning was performed for each model for over 486 data points. Regression predictive models were developed for both ink droplet velocity and volume with three linear models (linear, lasso, and ridge regression) and four non-linear models (random forest, decision tree, support vector regression, and k-nearest neighbor). Mean squared error and the coefficient of determination, r-squared value, were used to evaluate the performance of the predictive models. For the drop type classification models, k-fold of 5 yielded the highest accuracy for the RF, KNN, and DT models of around 98%. Similarly, for the regression based predictive models RF, DT and KNN accuracy results ranged from 97 to 99%. All the machine learning models were validated with experimental data with high prediction accuracies accuracy. This research serves as a foundation for developing design guidelines for 3D material inkjet printing with applications in biosensors, flexible electronics, and regenerative tissue engineering.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 810-821"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A study on the gas film formation in electrochemical discharging processes by molecular dynamics simulation 通过分子动力学模拟研究电化学放电过程中的气膜形成

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.042

Yu-Jen Chen, Murali Sundaram

引用次数: 0

NAMRC 52 fast-tracked research papers to Journal of Manufacturing Systems and Journal of Manufacturing Processes NAMRC 向《制造系统杂志》和《制造工艺杂志》快速提交 52 篇研究论文

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.003

Robert X. Gao (NAMRI/SME Scientific Committee Chair), Xun Xu (NAMRI/SME Scientific Committee Chair-Elect), Ihab Ragai (NAMRI/SME Scientific Committee Advisor)

引用次数: 0

Hybrid manufacturing cost models: Additive friction stir deposition, measurement, and CNC machining 混合制造成本模型：快速摩擦搅拌沉积、测量和数控加工

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.038

Nathan Wilson , Robert Patterson , Elijah Charles , Malachi Landis , Joshua Kincaid , Ryan Garcia , Gregory Corson , Tony Schmitz

{"title":"Hybrid manufacturing cost models: Additive friction stir deposition, measurement, and CNC machining","authors":"Nathan Wilson , Robert Patterson , Elijah Charles , Malachi Landis , Joshua Kincaid , Ryan Garcia , Gregory Corson , Tony Schmitz","doi":"10.1016/j.mfglet.2024.09.038","DOIUrl":"10.1016/j.mfglet.2024.09.038","url":null,"abstract":"<div><div>Based on its potential to reduce lead times, hybrid manufacturing, which often includes both additive manufacturing and machining processes, is receiving more attention from manufacturers as they seek to increase their supply chain resilience and efficiency. A new solid-state additive manufacturing, referred to as additive friction stir deposition (AFSD), has shown the potential to become an important process for hybrid manufacturing. To justify the selection of a hybrid manufacturing approach, the cost needs to be estimated for comparison to conventional approaches. Historically, hybrid manufacturing costs have been difficult to estimate due to the complexity and diversity of the manufacturing processes. This paper proposes cost models that include additive friction stir deposition, structured light scanning, milling, and turning, which can be combined in hybrid manufacturing process planning. These cost models are demonstrated in a case study and cost estimates are compared for hybrid and conventional (machining-only) manufacturing approaches. For the selected case, the hybrid manufacturing process cost was $1007.58, while the conventional milling process cost was $833.60. The results of the case study show that both labor and material costs must be considered to make an informed decision between hybrid and conventional manufacturing approaches.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 320-331"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of materials and process parameters on machinability of stainless steels 材料和工艺参数对不锈钢机加工性能的影响

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.088

Iqbal Shareef , Durga Kumar Raja Potluri , Gerry Horton

{"title":"Effect of materials and process parameters on machinability of stainless steels","authors":"Iqbal Shareef , Durga Kumar Raja Potluri , Gerry Horton","doi":"10.1016/j.mfglet.2024.09.088","DOIUrl":"10.1016/j.mfglet.2024.09.088","url":null,"abstract":"<div><div>Stainless steels, recognized for their corrosion resistance attributed to a minimum of 11 % Chromium, encompass a variety of alloys with distinctive microstructures and properties. Machinability significantly varies among these alloys. Austenitic steels such as SS303 and 304 present challenges, demonstrating poor surface finish and high power consumption. This study, employing a central composite design, investigates the machinability of AISI 303, 304, 316, AISI 416, and AISI A36. Turning tests with PVD TiAlN-coated inserts revealed optimal parameters for cutting speeds (90.5256–244.411 m/min), feed (0.0635–0.4826 mm/rev), and depth (0.00016–0.00187 m.). Surface finish analysis identified AISI 316 as the best, closely followed by AISI 303. From a power consumption standpoint, AISI 303 performed the best, and concerning fragmented chip morphology, AISI 303 also excelled. The superior performance of AISI 303 is attributed to 2 % Manganese and 0.15 % Sulfur, proving to be the most effective combination compared to the other four steels, resulting in a higher percentage of MnS<sub>2</sub>, optimal for improving machinability. The depth of cut emerges as the most influential factor affecting dimensional accuracy. These findings hold practical significance in the selection of stainless steels and corresponding process parameters across various industries, including the manufacturing of heavy earthmoving equipment. By shedding light on the optimal composition and machining conditions, this study contributes valuable insights for enhancing performance and efficiency in stainless steel applications.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 696-707"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A virtual reality-based immersive teleoperation system for remote human-robot collaborative manufacturing 基于虚拟现实的沉浸式远程操纵系统，用于远程人机协作制造

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.008

Ke Wan, Chengxi Li, Fo-Sing Lo, Pai Zheng

引用次数: 0

Influence of micro- and macroscopic tool features and errors within one batch in end milling 端面铣削中微观和宏观刀具特征及同批误差的影响

IF 1.9

Manufacturing Letters Pub Date : 2024-10-01 DOI: 10.1016/j.mfglet.2024.09.081

Johanna Steiner-Stark, Maximilian Berndt, Benjamin Kirsch, Jan C. Aurich

引用次数: 0