Manufacturing Letters最新文献

{"title":"Experimental Study of Residual Stresses in LPBF Using Different Island Scanning Strategies","authors":"Ehsan Malekipour,&nbsp;Farshad Samadpour,&nbsp;Hazim El Mounayri","doi":"10.1016/j.mfglet.2026.07.098","DOIUrl":"10.1016/j.mfglet.2026.07.098","url":null,"abstract":"<div><div>Residual stress remains a major limitation in laser powder bed fusion (LPBF), yet most prior work has focused on hatch patterns and rotation angles rather than the sequence in which individual islands or stripes are printed. This study addresses that gap by examining how four island-sequence strategies, two fixed sequences and two optimized sequences, influence residual stress in SS316L parts. Samples were fabricated using LPBF system, and top-surface stresses were measured at twelve locations using X-ray diffraction. While the top surface residual stress field contained both tensile and compressive stresses, the conventional successive and chessboard sequences exhibited higher local tensile extremes, consistent with localized heat accumulation and steep temperature gradient. In contrast, the optimized sequences distributed heat more evenly, reduced peak tensile stress, and produced smoother stress maps after baseplate removal. The results demonstrate that controlling the order of island activation is a powerful and underutilized method for mitigating residual stress, offering meaningful improvements.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 839-845"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149095908","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":"AI Modeling of Roll-to-Roll Coating System for Manufacturing Secondary Battery Electrodes","authors":"Wan Tae Lee,&nbsp;Yunseon Byun,&nbsp;Uzair Ali,&nbsp;Seung-Hyun Lee,&nbsp;Soonwoo Shin,&nbsp;Hyun Chul Kim,&nbsp;Inyoung Kim,&nbsp;Taik-Min Lee","doi":"10.1016/j.mfglet.2026.07.013","DOIUrl":"10.1016/j.mfglet.2026.07.013","url":null,"abstract":"<div><div>Innovation in artificial intelligence (AI) has rapidly expanded its applicability across various industrial processes, demonstrating particularly strong effectiveness in continuous manufacturing systems such as roll-to-roll (R2R) production. Secondary battery manufacturing relies heavily on R2R-based electrode fabrication, where slight variations in tension, line speed, or coating conditions can significantly influence the final product quality. In this study, an automated data pipeline was developed to support AI modeling of the electrode manufacturing process for secondary batteries. The proposed pipeline consists of data synchronization, region partitioning, file fusion, and automated input variable selection. Based on this pipeline, key process variables required for mass loading prediction were systematically identified and optimized. This study proposes a data-handling workflow for battery-manufacturing process data and evaluates an AI-based predictor on five continuous-production cases. On the hold-out test sets, the models achieved a maximum R² of 0.966, and four of the five cases attained R² values of at least 0.95. The RMSE values ranged from 20.125 to 52.097.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 42-50"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149095963","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":"Springback prediction and control in sheet metal stamping using advanced blank holder load path strategies with ferrofluidic actuators","authors":"Mattia Dal Maso,&nbsp;Enrico Simonetto,&nbsp;Andrea Ghiotti,&nbsp;Stefania Bruschi,&nbsp;Stefano Filippi","doi":"10.1016/j.mfglet.2026.07.060","DOIUrl":"10.1016/j.mfglet.2026.07.060","url":null,"abstract":"<div><div>This study investigates an adaptive sheet forming system assisted by magnetorheological (MR) fluid actuators, also known as ferrofluidic actuator, combining experimental testing and numerical simulation to analyze and assess blank holder force (BHF) modulation strategies. Building upon previous developments in ferrofluidic actuation, the present work introduces and compares constant and stepped BHF paths applied to the forming of a hat-shaped component. The stepped strategy was designed to selectively increase the BHF during the initial and final bending stages, where springback originates, while maintaining lower forces during the intermediate phase to reduce energy consumption. Finite element (FE) simulations were employed to replicate the experimental conditions and to analyze the strain and stress evolution during forming and springback. The numerical model, validated against experimental data, provided insights into the local deformation mechanisms and the effectiveness of each BHF strategy. Results show that stepped BHF modulation can achieve a springback reduction comparable to that of high constant-force conditions, but with significantly improved energy efficiency and reduced strain in the vertical walls. The integrated experimental–numerical approach thus demonstrates the potential of MR-actuated systems to enable in-process adaptable control strategies, offering new perspectives for the design of energy-efficient and dimensionally accurate forming operations.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 495-505"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096304","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":"An Interpretable Machine Learning based Predictive Control Method for Manufacturing Processes with Turning as A Case Study","authors":"Mason Ma,&nbsp;Victor Wu,&nbsp;Jingang Yi,&nbsp;Hong Wang,&nbsp;Bradley Jared,&nbsp;Tony Schmitz,&nbsp;Tony Shi","doi":"10.1016/j.mfglet.2026.07.063","DOIUrl":"10.1016/j.mfglet.2026.07.063","url":null,"abstract":"<div><div>Modern digital manufacturing machines can record massive process signals through embedded internal controllers and instrumented external sensors. This provides a good base for the development of machine learning-based control methods for modeling and control of manufacturing processes. This study develops an interpretable machine learning based predictive control method, and that can learn effective ordinary differential equations as process models to achieve reliable and robust control performance. Verification and validation results on a synthetic example of Lorenz 3 system with control show the proposed method can achieve superior performance on model discovery from highly noisy data, and both set point and trajectory controls. In addition, a case study on the turning process using experimentally validated time domain simulator is conducted. Compared with existing methods, the proposed method shows a two order-of-magnitude anti-noise capability improvement for model discovery, and that enables stable process parameter selection with maximum material removal rate. The proposed method has the potential to be used for process model discovery, and model-based monitoring and control for machining and other manufacturing processes.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 525-536"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096307","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":"Effect of Tool Polarity on TiN Layer Formation in Dry Electrical Discharge Machining with Nitrogen Dielectric","authors":"Anurag Virendra Srivastava,&nbsp;Soham Mujumdar","doi":"10.1016/j.mfglet.2026.07.044","DOIUrl":"10.1016/j.mfglet.2026.07.044","url":null,"abstract":"<div><div>A hard white or recast layer forms during electrical discharge machining (EDM) due to rapid melting and re-solidification. Owing to its high quenching rates and altered composition, this layer often exhibits substantially higher hardness than the base material, offering opportunities for surface engineering applications. In this study, dry EDM was employed to develop a white layer consisting of titanium nitride (TiN) mixed in an iron matrix on an annealed H13 tool steel substrate. An additively manufactured Ti6Al4V was used as the tool electrode, with both polarities explored, and nitrogen gas was used as the dielectric medium. A layer thickness of approximately <span><math><mrow><mn>11.4</mn><mo>±</mo><mn>3.8</mn><mi>μ</mi><mi>m</mi></mrow></math></span> was achieved under positive tool polarity, compared to <span><math><mrow><mn>32.6</mn><mo>±</mo><mn>11.7</mn><mi>μ</mi><mi>m</mi></mrow></math></span> under negative polarity. However, the surface roughness (<span><math><mrow><msub><mrow><mi>S</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></math></span>) obtained with positive polarity was 45% lower than that under negative polarity. The titanium content in the white layer increased by 103% under negative polarity, demonstrating a strong polarity-dependent effect on material transfer. A maximum microhardness of <span><math><mrow><mn>1063</mn><mo>±</mo><mn>165</mn><msub><mrow><mi>HV</mi></mrow><mrow><mn>0.05</mn></mrow></msub></mrow></math></span>, over four times that of the base material, was achieved in the white layer. X-ray diffraction (XRD) analysis confirmed the presence of distinct TiN phases in the machined samples that contribute to increased microhardness. Overall, this approach offers an environmentally friendly EDM route for generating ceramic-based surface layers, enabling the deposition of diverse functional white layers through the controlled selection of dielectric gases, tool materials, and polarity.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 344-350"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096330","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":"Electrochemical Impedance Studies on the Corrosion Phenomena in Additively Manufactured Al-Si-Mg Alloys in a Chloride Environment","authors":"Samson D. Oguntuyi,&nbsp;Peter A. Olubambi","doi":"10.1016/j.mfglet.2026.07.096","DOIUrl":"10.1016/j.mfglet.2026.07.096","url":null,"abstract":"<div><div>This study explores how corrosion unfolds in aluminium hybrid structures created by integrating Selective Laser Melting (SLM) with conventional casting techniques. To better understand the impact of different fabrication routes, three sample configurations were considered: fully SLM-built components, cast aluminium substrates, and hybrid structures where an SLM layer was directly deposited onto a cast base. Each of these samples was inserted into a 3.5 wt.% NaCl solution and their respective electrochemical performances were evaluated via Electrochemical Impedance Spectroscopy (EIS). With an equivalent electrical circuit model, the impedance spectra were interpreted and also to capture the interfacial corrosion dynamics. The SLM-built samples depicted greater charge transfer resistance (Rct), indicating a more protective and stable passive oxide layer. On the other hand, the cast samples showed decreased impedance responses and lower Rct values, which connote a more defective or porous oxide layer and higher susceptibility to chloride-induced corrosion. While the hybrid samples showed an intermediate corrosion reaction, which is ascribed to the combined effect of the SLM-built surface and the cast substrate. A subtle reflection of interfacial galvanic interactions was detected, consequently reducing slightly their corrosion behaviour compared to the monolithic SLM sample. Additionally, to get the degradations mechanisms, post-corrosion surface characterization was carried out via Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD). In the cast and hybrid samples, localized corrosion characteristics were revealed by the SEM, and the XRD analysis affirms the variation in the oxide phases and microstructural alterations.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 821-828"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149095906","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":"Experimental Evaluation of Rectangular Slot Nozzle on Print Performance in Fused Filament Fabrication Additive Manufacturing Technology Using PLA","authors":"Rishabh Yadav,&nbsp;Mathew A. Kuttolamadom,&nbsp;Jyhwen Wang","doi":"10.1016/j.mfglet.2026.07.097","DOIUrl":"10.1016/j.mfglet.2026.07.097","url":null,"abstract":"<div><div>The application of Additive Manufacturing (AM) in producing functional parts is growing rapidly. Fused Filament Fabrication (FFF) is a cost-effective and rapidly growing AM technology with an increased focus on minimizing manufacturing time. This work evaluated the feasibility of rectangular slot nozzle geometry of dimensions 6 × 0.6 mm in FFF printer in reducing print time and voids. First, experiments were carried out by varying three process parameters - nozzle temperature, layer height, and print speed to identify stable printing conditions. Next, under stable printing conditions, dimensional accuracy was measured and compared with those printed using a standard circular nozzle. Then mechanical behavior was evaluated using three-point bending test. Finally, a dual-nozzle printing approach was tested, combining the circular and rectangular nozzles to print a single part. The results showed that the rectangular nozzle produced consistent prints within identified process window. The dimensional errors in width and thickness were around 5% for both nozzle types, while printing was 80% faster for the rectangular nozzle when printing ASTM D790 test specimens. These findings demonstrate that rectangular slot nozzle is a viable solution and can be integrated into dual-nozzle printers for faster printing.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 829-838"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149095907","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":"Characterization and Machine Learning-Based Prediction of Aerosol Jet Printing Behavior on Inclined Substrates","authors":"Reihane Arabpoor,&nbsp;Azadeh Haghighi","doi":"10.1016/j.mfglet.2026.07.086","DOIUrl":"10.1016/j.mfglet.2026.07.086","url":null,"abstract":"<div><div>Aerosol Jet Printing (AJP) has emerged as a versatile additive manufacturing process for producing high-resolution electronic features across a wide range of materials and substrates. While the technique is well established for planar surfaces, its full potential for conformal and hybrid additive manufacturing remains underdeveloped. As the field advances toward printing on complex three-dimensional geometries, a deeper understanding of the influence of surface orientation and deposition angle on print quality becomes essential. In this study, we present a comprehensive experimental investigation of AJP on inclined surfaces to quantify the effects of geometric parameters on printed line morphology and electrical performance. A series of various line orientations was deposited at surface inclination angles ranging from 0° to 60° and orientation angles from 0° (horizontal) to 90° (vertical), systematically evaluating the effect of line orientation on inclined surfaces for the first time. The resulting line characteristics, including but not limited to width, thickness, and resistivity, were systematically measured and analyzed. The results reveal that both the surface inclination and print orientation significantly affect the deposited line geometry, with line width generally increasing at higher inclination angles due to altered gas flow rate and droplet spreading behavior. However, resistivity exhibits a nonlinear dependence on deposition geometry, indicating a complex interplay between droplet deposition dynamics, ink redistribution, and post-deposition sintering. To capture these relationships quantitatively, a Gaussian Process Regression model was trained on the experimental dataset. The model achieved a prediction error as low as 1.58 <span><math><mrow><mo>%</mo></mrow></math></span>, demonstrating strong potential for predictive process planning and optimization. This data-driven approach provides a foundation for developing adaptive path planning strategies for conformal AJP on non-planar surfaces. Overall, this work advances the understanding of geometric effects in aerosol jet printing and provides key insights for improving the reliability and precision of conformal and hybrid additive manufacturing of electronics. The outcomes contribute toward enabling robust, high-performance, and geometry-aware AJP processes for next-generation three-dimensional electronic systems.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 721-729"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096083","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":"Intelligent monitoring of drilling tool wear with a sensory tool holder","authors":"Her-Terng Yau,&nbsp;Ping-Huan Kuo,&nbsp;Hao-Yang Lin,&nbsp;Chia-Ho Lee","doi":"10.1016/j.mfglet.2026.07.070","DOIUrl":"10.1016/j.mfglet.2026.07.070","url":null,"abstract":"<div><div>During machining, friction between the tool and a workpiece or shavings causes unavoidable physical or chemical damage to the tool. The amount of damage depends on several factors, including the machining parameters, material hardness, tool material, and cooling. To achieve the online estimation of the tool wear condition, a sensory tool holder (STH) was developed in this study to measure the thrust and torque signals of a drill during machining. Motor current signals from the spindle and feed shaft and the machining center were also measured. The collected current and STH signals were processed using two methods. First, empirical mode decomposition was performed to obtain intrinsic mode functions from the first five iterations as feature inputs for training wear estimation models. Second, third-order discrete wavelet transform (DWT) was performed to obtain approximation coefficients as the feature inputs. Moreover, an image-based tool wear detection system with a charge coupled device camera was installed in the machine to quantify the tool wear after drilling; the wear data were labeled and used as input labels for model training. Three algorithms were used to train various models using the feature inputs obtained from the two aforesaid processing methods, and a 1D convolutional neural network (CNN) model on the STH dataset with DWT was verified to demonstrate the highest accuracy, yielding a mean absolute error of 0.013, root mean square error of 0.018, and coefficient of determination R² of 0.933. The use of this system helps to significantly reduce the preliminary costs of building wear models. Through the combination of near real-time wear image measurement and STH, the time and labor consumption of wear experiments can be reduced. The finally deployed CNN model can achieve real-time drill bit wear prediction during STH machining.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 584-594"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096152","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":"Digital machining database for machine learning","authors":"Tony Schmitz,&nbsp;Ross Zameroski,&nbsp;Michael Gomez","doi":"10.1016/j.mfglet.2026.07.065","DOIUrl":"10.1016/j.mfglet.2026.07.065","url":null,"abstract":"<div><div>The purpose of the open-source Digital Machining Database is to support machine learning algorithm development for machining process modeling. The database is made available to the research community and provides organized data for algorithm training, validation, and benchmarking. The data are generated using deterministic, physics-based numerical (time domain) and analytical (frequency domain) simulations for many milling conditions. For each dataset corresponding to a selected set of milling conditions, hundreds of simulation results are provided for individual spindle speed-axial depth of cut combinations. The time domain signals are: 1) tool displacement, velocity, and acceleration; 2) workpiece displacement, velocity, and acceleration; and 3) cutting force. The spindle speed-dependent stability boundary is also included for each dataset.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 548-554"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096153","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}