Manufacturing Letters最新文献

{"title":"Characterization of oscillatory magnetic field-assisted finishing of directed energy deposition NASA HR-1 integral channels","authors":"Kateland Hutt ,&nbsp;Justin Rietberg ,&nbsp;Paul Gradl ,&nbsp;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}

{"title":"A qualitative validation of an in-situ monitoring system for EHD inkjet printing via laser diffraction","authors":"Xuepeng Jiang,&nbsp;Pengyu Zhang,&nbsp;Hantang Qin","doi":"10.1016/j.mfglet.2024.09.029","DOIUrl":"10.1016/j.mfglet.2024.09.029","url":null,"abstract":"<div><div>Electrohydrodynamic inkjet printing enables high-resolution patterning for nano features. In-flight dynamics of EHD inkjet printing play an essential role in the quality control of printing results. We applied a laser diffraction/scattering in-situ analyzing setup for the EHD inkjet printing system to replace the zoom lens and high-speed camera imaging system. In contrast to conventional imaging systems, the laser diffraction/scattering system is based on analyzing the diffraction pattern and scattering intensity, respectively, which provided higher resolution for micro-scale jetting measurement and enabled sub-micron level jetting correlation between the voltage applied to the electrode and printing results. Furthermore, Taylor cone information from the nozzle head could also be analyzed in real-time to make adjustments to the printing process. In this work, we successfully validated the feasibility of laser diffraction analysis in-situ monitoring for EHD inkjet printing at micron and sub-micron levels.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 248-252"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434277","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}

{"title":"Optimization and prediction of additively manufactured PLA-PHA biodegradable polymer blend using TOPSIS and GA-ANN","authors":"Shafahat Ali ,&nbsp;Vijayant Mehra ,&nbsp;Abdelkrem Eltaggaz ,&nbsp;Ibrahim Deiab ,&nbsp;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}

{"title":"Predictive models for 3D inkjet material printer using automated image analysis and machine learning algorithms","authors":"Mutha Nandipati,&nbsp;Michael Ogunsanya,&nbsp;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}

{"title":"Investigating the use of 3D printed tools for electrochemical machining: Lessons learned and future improvements","authors":"Rhett Jones,&nbsp;Robert Prins,&nbsp;Jack Zhao","doi":"10.1016/j.mfglet.2024.09.062","DOIUrl":"10.1016/j.mfglet.2024.09.062","url":null,"abstract":"<div><div>This paper describes the use of 3D printing in the production of tool electrodes for use in electrochemical machining (ECM). The majority of ECM jobs require the use of a unique form tool, production of which represents a significant expense. Additive manufacturing processes such as 3D printing offer the potential to lower cost of production and allow design of more complex tool electrode geometries. The tool electrodes used in this research effort were printed in polylactic acid (PLA) and subsequently fit with a copper electrode to serve as the electrical connection terminal for the tool. The tool surface intended for use as the electrode for ECM was coated with an electrically conductive paint before being copper electroplated to form a conductive surface. These 3D printed tool electrodes were successfully demonstrated to machine hardened tool steel in a prototype ECM machine, although challenges remain. This paper describes the development of ECM tools from 3D printed tool blanks, the prototype ECM system that was constructed to demonstrate use of the tool blanks, and the results of applying the 3D printed blanks to machine hardened tool steel. Next steps including potential improvements to tool electrodes are also discussed.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 513-517"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434155","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}

{"title":"CAPP-GPT: A computer-aided process planning-generative pretrained transformer framework for smart manufacturing","authors":"Ahmed Azab ,&nbsp;Hany Osman ,&nbsp;Fazle Baki","doi":"10.1016/j.mfglet.2024.09.009","DOIUrl":"10.1016/j.mfglet.2024.09.009","url":null,"abstract":"<div><div>Smart manufacturing (SM) constitutes the backbone of Industry 4.0 (I4.0), allowing for heightened autonomy of the various interacting cyber-physical systems, making the various entities on the production floor. Connectivity, a vital enabler, plays a crucial role through state-of-the-art Digital Twinning (DT) technologies driven by underlying innovations like the industrial Internet of Things, Cloud Computing, and advancements in sensory devices. DT, which plays a vital role in the various planning functions under the production and operations management umbrella, is being used in the developed combined CAPP-GPT (Computer-Aided Process Planning-Generative Pretrained Transformer) and production scheduling approach to address disruptions on the shopfloor and in self-healing of the manufacturing processes at a micro-CAPP level by optimally adapting the process parameters and the developed toolpath on the fly based on online process signature measurements. In a leap commensurate with that which has taken place in Natural Language Processing-Large Language Models (Chat-GPT), similar efforts are currently being undertaken to parse CAD data structures and blueprints, fusing operations research and predictive analytics algorithms to carry out setup planning as well as sequencing and grouping manufacturing sub-operations. A hybridized Optimization and Machine Learning (ML) approach is employed where Logical Analysis of Data is used to solve the problem heuristically, exploiting various generative and variant methods at heart. Another extension of this macro-CAPP problem is being tackled by integrating the problem with delayed product differentiation, lot-sizing, and transfer line balance for futuristic batch-production shops employing Hybrid Manufacturing (HM) and Smart Assembly. At the micro-CAPP level, HM process parameters are optimized using a comprehensive approach employing the Taguchi loss function to assess surface roughness, internal failure costs, and other criteria, including greenhouse gas emissions and expended energy. Online measurements of the process signatures are also employed to adapt the initial set of process parameters using different automatic control schemes. ML is used to identify the process parameters carrying simulations on Simulink before the system is deployed.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 51-62"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434339","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}

{"title":"Production efficiency analysis based on the RFID-collected manufacturing big data","authors":"Zhihui Chen ,&nbsp;Zeyu Xiao ,&nbsp;Yize Sun ,&nbsp;Yuhao Dong ,&nbsp;Ray Y. Zhong","doi":"10.1016/j.mfglet.2024.09.012","DOIUrl":"10.1016/j.mfglet.2024.09.012","url":null,"abstract":"<div><div>Radio Frequency Identification (RFID) technology is widely used for production data collection in manufacturing shop-floors. The RFID-collected manufacturing big data reflects detailed statuses of production processes and manufactured products, which, in turn, can be used to support a comprehensive production analysis. This paper introduces an analytical approach to conduct production efficiency analysis based on the RFID-collected manufacturing big data. The proposed method involves four key steps: data cleansing, data processing, key performance indicator (KPI) estimation, and data analytics. Specifically, speed and quality aspects of shopfloor manufacturing are investigated collectively as production efficiency to support the comprehensive production analysis. The findings highlight the influence of various factors, such as operation date, working hour, and machine failure, on production efficiency.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 81-90"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434342","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}

{"title":"Smart design and additive manufacturing of bending tools to improve production flexibility","authors":"Michael Schiller ,&nbsp;Peter Frohn-Sörensen ,&nbsp;Florian Schreiber ,&nbsp;Daniel Morez ,&nbsp;Martin Manns ,&nbsp;Bernd Engel","doi":"10.1016/j.mfglet.2024.09.013","DOIUrl":"10.1016/j.mfglet.2024.09.013","url":null,"abstract":"<div><div>For the automotive industry, especially on the part of Tier 1 and Tier 2 suppliers, the future will be about maintaining sovereignty in the form of technology openness and accelerating digitization. The product portfolio, which is generally passed on by OEMs to suppliers for production, often includes body parts that cannot always be manufactured economically with the prevailing production technology. The reason for this is a high diversity of model-variants, which requires smaller batches. To this end, highly flexible large-series production cells for body sheet components that can be scaled in all dimensions are being developed and tested. For the first time, they will make it possible to redesign the process planning in series production on a component-specific basis. The aim is to reduce production costs for new, geometrically different component variants. The basic components of the flexible manufacturing system are, firstly, new flexible forming technologies which have the potential to produce typical vehicle part geometries. Secondly, a process generator develops the corresponding production plan. A digital mapping of the manufacturing processes enables the selection of cost-, efficiency-, flexibility- and resilience-optimized production chains depending on the number of parts. Established manufacturing processes to produce car body components are supplemented in the cell by flexible processes such as 3D swivel bending. As a use case for flexible manufacturing, a concept for Rapid Tooling of 3D swivel bending tools is developed. In the flexible manufacturing system to be developed, a method of a standardized process sequence to produce forming tools within 24 h has been lacking to date. For this purpose, the concept of an automated design is being developed in which a reconfigurable tool body can be sliced into sheet metal stripes The active tool surface is additively manufactured after the tool has been packaged using LMD and adapted to individual requirements. The goal in the application of Rapid Tooling is to reduce lead times and development costs through a largely automated tool design and lead time-optimized manufacturing concept.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 91-102"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434343","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}

{"title":"Prediction of tool wear and surface finish using ANFIS modelling during turning of Carbon Fiber Reinforced Plastic (CFRP) composites","authors":"Anil K. Srivastava,&nbsp;Md. Mofakkirul Islam","doi":"10.1016/j.mfglet.2024.09.084","DOIUrl":"10.1016/j.mfglet.2024.09.084","url":null,"abstract":"<div><div>Carbon fiber-reinforced plastics (CFRP) are widely used in various industries due to their high strength to weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties. The machining of CFRP composites has always been a challenge for the manufacturers. In this study, CNC turning operation with coated carbide tool is used to machine a specific CFRP and the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated. An adaptive-network-based fuzzy inference system (ANFIS) model with two multi-input–single-output (MISO) system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameters. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. This model is capable of predicting the cutting tool wear and surface finish during turning of CFRP composite. The predicted tool wear and surface finish data are compared to the experimental results. The predicted data agreed well with the actual experimental data with 98.96 % accuracy for tool wear and 99.61 % accuracy for surface finish.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 658-669"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434356","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}

{"title":"A study on the gas film formation in electrochemical discharging processes by molecular dynamics simulation","authors":"Yu-Jen Chen,&nbsp;Murali Sundaram","doi":"10.1016/j.mfglet.2024.09.042","DOIUrl":"10.1016/j.mfglet.2024.09.042","url":null,"abstract":"<div><div>Molecular Dynamics (MD) simulations have emerged as a potent analytical tool for dissecting the intricate processes involved in nano gas film bubble generation. This study employs MD simulations to identify critical voltage that marks the transition from bubble saturation to gas film formation, while employing a mimic electrolysis model to expedite simulations through accelerated molecular insert rates. The simulations provide insights into underlying mechanisms, revealing the reforming and condensing dynamics of gas structures preceding gas film genesis. Experimental validation corroborates the accuracy of critical voltage predictions derived from MD simulations, with the close alignment between simulated critical points and experimental outcomes underscoring the robust predictive capability of MD simulations in elucidating electrochemical discharging (ECD) processes.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 351-356"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434245","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}