Manufacturing Letters最新文献

{"title":"Effect of strain-integration gas-infusion casting process and post-treatment on the corrosion behavior of AZ91 alloy","authors":"V. Tiwari,&nbsp;Prithivirajan Sekar,&nbsp;S.K. Panigrahi","doi":"10.1016/j.mfglet.2025.06.041","DOIUrl":"10.1016/j.mfglet.2025.06.041","url":null,"abstract":"<div><div>AZ91 magnesium alloy casting is essential for lightweight<!--> <!-->automotive, aerospace, and electronics applications. This alloy accounts for 90 % of the applications of magnesium alloys. However, AZ91 castings typically exhibit a coarse dendritic microstructure and a brittle β-phase, which diminish their mechanical performance and limit their industrial applications. The Strain-Integrated Gas Infusion (SIGI) casting process has been explored to address these challenges. The SIGI casting process produces refined microstructure and eliminates microporosity. This study investigates the effect of the SIGI casting process on the corrosion resistance of AZ91 magnesium alloy. A comparative analysis was conducted to assess the impact of different casting processes, namely SIGI, Stir casting, and Conventional die casting, on the corrosion behavior of AZ91 magnesium alloy. The three casting methods were followed by solution treatment and subsequent aging, and the effects of these treatments combined with the casting processes were also examined.<!--> <!-->Microstructural characterization and mechanical testing were performed at each stage. Additionally, weight loss measurements and electrochemical behavior tests were conducted to evaluate the corrosion behavior under all conditions SIGI process enhanced the mechanical properties and corrosion resistance of AZ91 alloy, with peak-aged SIGI casting showing 280–300 MPa strength, 5–6 % ductility, and a corrosion rate of 0.011 mm/year, compared to conventional casting with 100–200 MPa strength, 2–3 % ductility, and 0.107 mm/year corrosion rate. The underlying mechanisms contributing to these improvements were also explored. The results confirm the effectiveness of the SIGI casting process in enhancing the corrosion resistance of AZ91 alloy, providing valuable insights for industrial applications.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 339-349"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-powered data processing framework for RFID-captured manufacturing datasets","authors":"Yau Pan Lim,&nbsp;Ray Y. Zhong","doi":"10.1016/j.mfglet.2025.06.010","DOIUrl":"10.1016/j.mfglet.2025.06.010","url":null,"abstract":"<div><div>With the rapid development of artificial intelligence (AI), the demand for data has been surging. More attention has been paid to data in their daily processes, such as the production processes. Deployed in manufacturing sites to control and monitor processes, the Internet of Things (IoT) technology specifically radio frequency identification (RFID) in industrial settings has shown its potential as a data collection approach. However, the data collected by the RFID suffers from several challenges such as duplication, missing data, etc. Therefore, this paper focuses on the development of a data processing framework for addressing the challenges. The framework will process real RFID-captured production data from an IoT-enabled manufacturing shop floor with three functionalities: data pre-processing, outlier detection, and big data analytics. For anomaly detection, this framework deals with the passing rate of different production processes with a detection model, which can be used to flag abnormal production cases to facilitate the quality control process. The flagged abnormal production cases will be generalized during big data analytics to investigate the reason behind underperformance.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 59-69"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning precedence relations for manufacturing operations sequencing using convolutional recurrent neural networks","authors":"Xiaoliang Yan,&nbsp;Zhichao Wang,&nbsp;David W. Rosen,&nbsp;Shreyes N. Melkote","doi":"10.1016/j.mfglet.2025.06.013","DOIUrl":"10.1016/j.mfglet.2025.06.013","url":null,"abstract":"<div><div>Cyber manufacturing as-a-service or platform-based manufacturing, which connects buyers and manufactured parts suppliers through an online marketplace, have recently emerged with the goal of democratizing access to manufacturing capabilities. This approach to sourcing manufactured parts places intense pressure on suppliers to efficiently and optimally plan for part manufacturing to reduce production costs and become more competitive. Sequencing of manufacturing operations is an important step in the process planning pipeline, which has historically relied on the knowledge of human experts. An automated approach to operations sequencing has long been sought but is urgently warranted today given the skilled labor shortage in the certain parts of the world. While researchers have proposed various algorithms for automating operations sequencing, an underlying assumption of these methods is that precedence relations (commonly referred to as precedence constraints) among manufacturing operations must be manually defined to preprocess inputs to operations sequencing algorithms. This assumption has significantly hampered the generalizability of existing operations sequencing algorithms. Considering this limitation, in this work we present a data-driven approach to learn precedence relations for machining operations instead of relying on human expertise. By embedding the precedence relations from successfully produced parts as latent recurrent vectors, it is demonstrated that the proposed 3D-convolutional recurrent neural network (3D-ConvRNN) model yields 97.6% precedence relation validation accuracy, outperforming a 3D-CNN binary classifier. Furthermore, the proposed model is used in case studies to assess simple sequences of machining operations for realistic parts and to automatically generate operations precedence graphs as inputs to operations sequencing algorithms. Our results suggest that a data-driven approach to learning precedence relations can be beneficial for automating operations sequencing by augmenting or replacing manually defined precedence constraints.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 91-101"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable CNN models for computationally efficient bearing fault diagnosis using learnable Gaussian/Sinc filters","authors":"Sabyasachi Biswas ,&nbsp;Abdullah Al Mamun ,&nbsp;MD Shafikul Islam ,&nbsp;Mahathir Mohammad Bappy","doi":"10.1016/j.mfglet.2025.06.015","DOIUrl":"10.1016/j.mfglet.2025.06.015","url":null,"abstract":"<div><div>Real-time monitoring of bearing health is essential for maintaining rotary machinery’s operational efficiency and reliability. Nowadays, deep learning-based convolutional neural networks (CNNs) have gained popularity for diagnosing various bearing fault conditions, surpassing traditional machine learning methods that rely on time–frequency analysis, feature extraction, and supervised learning. However, the foundational architecture of CNNs—including filter size, number of filters in convolutional layers, optimizers, batch size, and more—significantly impacts model performance. Among these parameters, filter size selection is particularly crucial, as it directly affects the computational efficiency and effectiveness of the trained model. To address these challenges, this paper presents an approach integrating parameter learning for structured functions used as filter banks within CNNs architecture. In the proposed architecture, the initial layer includes parameterized learnable filters (LFs) that operate directly on raw data, producing frequency-related features through the structured design of the filter functions. Specifically, two types of parameterized LFs—Sinc and Gaussian filters—are introduced, each providing distinct bandpass filtering in the frequency domain to improve the direct classification of raw time-series data. To validate its effectiveness, we used state-of-the-art bearing fault datasets leveraging vibration signals. Experimental results demonstrate that the proposed approach successfully detects various bearing fault conditions and achieves performance similar to benchmark methods, even with limited training data. Thus, the proposed method enhances classification performance while improving the interpretability and understanding of CNNs operations, leading to more effective bearing fault diagnosis.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 110-120"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stock design in hybrid manufacturing using a constrained clustering approach","authors":"Hany Osman ,&nbsp;Ahmed Azab ,&nbsp;Fazle Baki ,&nbsp;Mohamed Gadalla","doi":"10.1016/j.mfglet.2025.06.031","DOIUrl":"10.1016/j.mfglet.2025.06.031","url":null,"abstract":"<div><div>Hybrid Manufacturing (HM) is a key pillar of smart manufacturing, enabling the production of complex parts with high precision and superior surface quality while minimizing costs and enhancing sustainability. A key challenge in HM systems is selecting the appropriate stock geometry to initiate processing<!--> <!-->both additive and subtractive features while achieving these benefits. Poor stock design can lead to increased waste and energy consumption, whereas an optimized configuration improves operational efficiency and maximizes sustainability. This paper addresses finding stock designs in HM, a problem that has not been tackled before using hybridized machine learning optimization techniques. A constrained clustering machine learning approach to determine stock dimensions for prismatic end parts is proposed. Given the geometry of the features included in these end parts, a novel combinatorial optimization model is developed to assign these features to pre-defined clusters such that the Hausdorff distance between features within clusters is minimized. Multiple scenarios are explored by evaluating different numbers of clusters. The proposed optimization model is validated, and its computational efficiency is evaluated through a case study that includes two test parts extending an existing test part from the literature. The first test part includes 22 additive and subtractive features while the other one includes 27 features. Due to the intractability of this combinatorial optimization clustering problem, problem instances representing small and medium-sized scenarios can be solved to optimality within a short time, whereas for large instances, only feasible solutions are obtained within a limited computational time of two hours.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 253-260"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Displacement measurement using various Hall effect sensor-magnet configurations","authors":"Tobechukwu D. Nwabueze ,&nbsp;Ross Zameroski ,&nbsp;Michael Gomez ,&nbsp;Tony Schmitz","doi":"10.1016/j.mfglet.2025.06.032","DOIUrl":"10.1016/j.mfglet.2025.06.032","url":null,"abstract":"<div><div>Broad implementation of industry 4.0 in manufacturing capabilities requires mass integration of digital technologies into production environments. Mass integration of digital technologies requires sensors that have low cost with high sensitivity and range. This paper evaluates displacement measurement for four configurations of low-cost Hall effect sensor-magnet combinations. Two axial configurations, where the magnet motion is aligned with the sensor axis, and two lateral configurations, where the magnet motion is perpendicular to the sensor axis, are studied. The difference for the axial configuration is the presence of a back biasing magnet. The difference for the lateral configuration is the use of one or two magnets, where the poles are aligned in opposite directions for the two-magnet option. The sensitivity and linear range were determined for each configuration as well as the variation in these parameters with translational misalignment between the magnet(s) and sensor.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 261-268"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of heat treatment on fatigue life and accuracy of incrementally formed AA2024","authors":"K. Praveen ,&nbsp;N. Venkata Reddy","doi":"10.1016/j.mfglet.2025.06.037","DOIUrl":"10.1016/j.mfglet.2025.06.037","url":null,"abstract":"<div><div>Double-sided incremental forming (DSIF) gaining importance in shaping 3D sheet metal components without relying on geometric-specific tooling. The challenge persists in forming high-work hardening aerospace aluminium alloys (AA2xxx) with better properties (tensile and fatigue) along with accuracy. The amount of spring-back for the AA2xxx is more especially when deformation is carried out using a tempered sheet compared to an annealed one. In contrast, the strength of an annealed sheet will be less compared to a tempered one. In the present work, an experimental work is carried out to study the effect of heat treatment (solution treatment–quenching–aging) on the tensile and fatigue properties of components formed using annealed sheets, along with the dimensional deviations that occur during heat treatment compared to the DSIF formed part. Various geometries (pyramid, cone, free-form) are formed using annealed (AA2024-O) sheets followed by heat treatment (HT, to attain AA2024-T62). Results show that the fatigue life of specimens extracted from DSIF components before HT is significantly lower than that of the as-received material. This reduction is due to decreased uniform elongation. However, after heat treatment, the fatigue life of specimens extracted from DSIF components increased compared to the as-received material before HT. This improvement is attributed to the precipitation of fine second-phase particles. These particles impede dislocation motion, enhancing fatigue life under given strain amplitudes. For the geometries formed in present work, the maximum dimensional deviations from intended geometry before heat treatment ranged from −0.43 mm to −0.59 mm, (where the negative values indicate under forming). After heat treatment of components, measurements were carried out again, and it is observed that an increase in dimensional deviations (i.e., varied from −0.52 mm to −0.64 mm) with difference in magnitude less than 150 µm compared to initially formed DSIF part. This indicates that the warpage due to rapid quenching is minimal.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 306-313"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical analysis of the influence of sequential cuts during micro-milling of wrought and LPBF Ti6Al4V alloys","authors":"Necati Uçak ,&nbsp;Jose Outeiro ,&nbsp;Adem Çiçek ,&nbsp;Kubilay Aslantas","doi":"10.1016/j.mfglet.2025.06.073","DOIUrl":"10.1016/j.mfglet.2025.06.073","url":null,"abstract":"<div><div>This study investigates the distribution of stresses, plastic strains, and temperatures in the machined surface and subsurface during micro-milling of wrought and Laser Powder Bed Fusion (LPBF) Ti6Al4V alloys considering the effects of sequential cuts using modeling approach. A series of micro-milling tests and numerical simulations were performed at two spindle rotational speeds (12000, 24000 rpm), two feeds per tooth (2, 4 µm/tooth), and a constant depth of cut (100 µm) under dry conditions. A 3D finite element model was developed, and simulation of micro-milling process was performed using Coupled Eulerian Lagrangian (CEL) approach. The experimentally measured machining forces and surface residual stresses were used to validate the developed 3D micro-milling model. It was shown that the model can reasonably simulate the machining forces (2.51–14.53 % error) and surface residual stresses (0.7–29.3 % error) for both wrought and LPBF Ti6Al4V alloys under different cutting conditions. To investigate the effects of intermittent cutting (i.e. sequential cuts) during micro-milling, the numerical model was developed to simulate three sequential cuts by considering the process of entry and exit of each tool tooth as one cut. In addition, unloading and cooling of the work material were also simulated to compare the state of the material during and after the process. The numerical results showed that sequential cuts resulted in increased stresses and temperature after the first cut and affected the material state during and after the micro-milling process. Machining-induced surface and subsurface residual stresses increased with the number of cuts due to accumulated stresses and strains, leading to greater plastic deformation and mechanical loads. Furthermore, LPBF Ti6Al4V alloy led to higher stresses and temperatures during micro-milling than the wrought material. This was attributed to the specific microstructure and higher mechanical properties of the LPBF Ti6Al4V alloy.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 622-630"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scanning direction dependence on crystal orientations of a femtosecond laser-assisted 4H-SiC wafer slicing","authors":"Hayoung Youn ,&nbsp;Dong Hee Kang ,&nbsp;Jaeseung Lim ,&nbsp;Seongheum Han ,&nbsp;Jae-Hak Lee ,&nbsp;Jihoon Jeong ,&nbsp;Seungman Kim","doi":"10.1016/j.mfglet.2025.06.055","DOIUrl":"10.1016/j.mfglet.2025.06.055","url":null,"abstract":"<div><div>4H silicon carbide (4H-SiC) is one of the promising semiconductor materials due to its wide bandgap, high thermal conductivity, and high breakdown electric field strength. Its superior mechanical characteristics ensure reliable performance in extreme environments such as automotive, energy production, and aerospace. However, a traditional wafer production method that uses a diamond wire saw is ineffective due to the high hardness and brittleness of 4H-SiC. Recently, a pulsed laser-assisted process, including ns-, ps-, and fs-pulses has been applied to wafer slicing. Among different pulse widths, femtosecond laser-assisted wafer slicing technology can minimize the heat-affected zone without material loss and debris creation during the process. Besides considering pulse width for effective wafer slicing, it is crucial to consider the laser scanning direction to improve production capacity and maintain the quality of the sliced wafer surface because the biased crack propagation induces surface morphology variation and mitigates peeling stress in layer separation. Here, a femtosecond laser-assisted wafer slicing for 4H-SiC was performed to evaluate the modified surface morphology according to the laser scanning direction and processing sequence. Slicing with the laser scanning direction was investigated in relation to the crystal orientation. Although the average surface roughness is within 1 to 2 <span><math><mrow><mi>μ</mi></mrow></math></span>m, tensile stress for layer separation can be decreased down to 4.22<span><math><mrow><mo>±</mo></mrow></math></span>0.60 MPa depending on the laser scanning direction, while in some cases exceeding the upper limit of 38 MPa of the tensile test machine. The effect of the crack propagation region differs from the laser scanning direction and processing sequence despite the identical line interval condition for laser slicing. The interval between lines generates 6.65 to 3.55 <span><math><mrow><mi>μ</mi></mrow></math></span>m for the separable layers, which requires tensile stress that can be regulated from 18.1 to 3.8 MPa while improving surface roughness. Laser slicing, as a result, considering scanning direction and processing sequence induces different crack propagation patterns. Considering the crystal orientation during the wafer slicing is important to improve production efficiency and maintain wafer quality.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 466-472"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical and experimental investigation of glass micromachining using ultrasonic-assisted electrochemical discharge machining","authors":"Anurag Shanu ,&nbsp;Sharad Valvi ,&nbsp;Pradeep Dixit","doi":"10.1016/j.mfglet.2025.06.069","DOIUrl":"10.1016/j.mfglet.2025.06.069","url":null,"abstract":"<div><div>This paper addresses challenges in debris removal and electrolyte replenishment during the electrochemical discharge micromachining (ECDM) of glass. The study presents a comprehensive numerical and experimental study of glass micromachining using ultrasonic-assisted electrochemical discharge machining (UA-ECDM). A finite element method (FEM)-based numerical model was developed to simulate the effects of ultrasonic vibrations on electrolyte flow and debris movement. The simulation results reveal that increasing ultrasonic amplitudes from 5 µm to 10 µm improves the electrolyte flow velocity two times at the microhole bottom. Additionally, ultrasonic vibration enhances debris distribution, shifting it towards the periphery of the<!--> <!-->microhole, thus improving the electrochemical discharge conditions. Experimentally, glass micromachining was performed at different ultrasonic amplitudes (0, 5, 8, and 10 µm). The results demonstrate that ultrasonic vibrations increase machining depth, reducing hole taper as a result of improving electrolyte circulation, correlating with the simulation result. A 3 × 3 array of holes was successfully fabricated on a glass substrate with a depth of 835 µm, confirming the feasibility of UA-ECDM for microhole drilling. This study confirms that UA-ECDM improves electrolyte circulation, enhancing electrochemical reactions at the tool-workpiece interface and increasing machining depth.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 588-593"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}