Journal of Manufacturing Processes最新文献

{"title":"Surface morphology generation mechanism of cortical bone longitudinal-torsional ultrasonic vibration assisted micro-milling","authors":"Peng Gao ,&nbsp;Min Wang ,&nbsp;Zhiqiang Liang ,&nbsp;Xiangsheng Gao ,&nbsp;Tao Zan","doi":"10.1016/j.jmapro.2025.04.060","DOIUrl":"10.1016/j.jmapro.2025.04.060","url":null,"abstract":"<div><div>Bone ultrasonic vibration micro-milling has an advantage of low cutting damage, and it potentially applied in minimally invasive medical surgery. However, cortical bone is a quasi-brittle biological material, and its cutting surface morphology generation mechanism are complex. Hence, this paper reveals the surface morphology generation mechanism of bone micro longitudinal-torsional ultrasonic assisted milling (LTUAM). The surface morphology generation of different cutting parameters in LTUAM was simulated. The bone ultrasonic vibration micro-milling experiments were carried out considering the bone transverse, across and parallel cutting directions. The surface morphology generation mechanism of cortical bone tissue in LTUAM was discussed. The experimental results indicated the crack propagation is strongly influenced by the micro-milling direction. The crack tends to propagate in parallel milling direction due to cement line cracking. The surface of bone in LTUAM has less damage and more smoother surface morphology. The bone LTUAM method effectively suppresses brittle fracture cutting, and reduces surface damage defects. This research provides efficient minimally invasive cutting methods and technical support for minimally invasive interventional surgical medical equipment.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"145 ","pages":"Pages 300-312"},"PeriodicalIF":6.1,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-task physics-constrained dictionary learning for efficient estimation of porosity distribution in laser powder bed fusion of copper","authors":"Longye Pan ,&nbsp;Guangfa Li ,&nbsp;Xin Zhang ,&nbsp;Jinze Cheng ,&nbsp;Dehao Liu ,&nbsp;Yanglong Lu","doi":"10.1016/j.jmapro.2025.04.053","DOIUrl":"10.1016/j.jmapro.2025.04.053","url":null,"abstract":"<div><div>The high porosity, as a primary defect in the laser powder bed fusion (LPBF) process for highly reflective metal components, significantly restricts the broader application of LPBF. However, existing pore detection methods primarily focus on classifying individual pores, offering limited insight into optimizing printing parameters. Additionally, these methods often overlook the storage and processing challenges associated with the large volumes of image data collected. Therefore, this paper introduces a multi-task physics-constrained dictionary learning approach that simultaneously compresses and estimates the porosity distribution in metallographic images of copper components produced by LPBF. Specifically, a physics-constrained label-consistent dictionary learning (PC-LCDL) algorithm is proposed for compressing images into discriminative sparse vectors. The pixel characteristics of low-resolution images are incorporated as a physical constraint, enabling the reconstruction of high-resolution images from the low-resolution ones. Hence, image acquisition efficiency can be improved. Moreover, a residual-based graph sample and aggregate (GraphSAGE) algorithm is integrated with the PC-LCDL to estimate the porosity distribution in the copper images. To thoroughly extract the distinctive features of pores, the reconstructed image patches concatenated with the sparse vectors are fed into the classifier. Experimental results demonstrate that even at a high compression ratio of 4.9, clear images can still be reconstructed from blurry ones which are down-sampled at a rate of 12.25 %. Consequently, a classification accuracy exceeding 89 % is still achieved, outperforming many other classification methods. Furthermore, the impact of printing parameters on porosity distribution is also investigated, leading to recommendations for adjusting printing parameters to minimize porosity levels.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"145 ","pages":"Pages 286-299"},"PeriodicalIF":6.1,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real time 3D reconstruction for enhanced cybersecurity of additive manufacturing processes","authors":"Ankush Kumar Mishra ,&nbsp;Shi Yong Goh ,&nbsp;Baskar Ganapathysubramanian ,&nbsp;Adarsh Krishnamurthy","doi":"10.1016/j.jmapro.2025.04.004","DOIUrl":"10.1016/j.jmapro.2025.04.004","url":null,"abstract":"<div><div>Industry 4.0 has enhanced automation and connectivity in manufacturing but also increased the risk of cyber-intrusions, particularly in additive manufacturing (AM) used in critical sectors such as defense, aerospace, and healthcare. This study presents a real-time process monitoring framework that detects cyber intrusions and halts the printing in a 3D printer. We retrofit existing printers with low-cost depth sensors that continuously capture 3D spatial data. Our framework reconstructs the printed object in real-time and compares it against a virtual ground truth model to detect geometric discrepancies indicative of cyber-intrusions. The entire detection process operates within the time taken to print a single layer for our case study of a standard 3D Benchy. We validate our approach by simulating cyber-intrusions that alter the G-code sent to the printer, successfully detecting and stopping compromised prints. This framework enhances AM cybersecurity by providing real-time threat detection and intervention, ensuring secure and resilient automated manufacturing in Industry 4.0.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"145 ","pages":"Pages 274-285"},"PeriodicalIF":6.1,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Micro-convex structure-assisted laser-induced backside dry etching for high-efficiency fabrication of micro-groove array on fused silica toward manipulation of wetting characteristics","authors":"Hu Huang ,&nbsp;Hong An ,&nbsp;Yongfeng Qian ,&nbsp;Zhiyu Zhang ,&nbsp;Minqiang Jiang ,&nbsp;Jiwang Yan","doi":"10.1016/j.jmapro.2025.04.063","DOIUrl":"10.1016/j.jmapro.2025.04.063","url":null,"abstract":"<div><div>Fused silica has garnered significant attention in various industrial applications. The creation of surface micro/nanostructures on fused silica can impart specialized functional properties, such as extreme wettability, ultra-high transmittance, and excellent antimicrobial characteristics. Nonetheless, existing technologies for achieving surface micro/nanopatterning of fused silica, including wire electrical discharge machining, chemical etching, and electrochemical deposition, generally suffer from long processing cycles and high energy consumption, which are inconsistent with the development concept of the low-carbon economy. In this study, a novel approach termed micro-convex structure-assisted laser-induced backside dry etching (MCSALIBDE) is proposed for high-efficiency fabrication of micro-groove array on fused silica surfaces. The synergistic effect of enhanced heat conduction and plasma explosion in a confined space facilitates material ablation. The impact of processing parameters including scanning pitch and peak laser power intensity on the morphological and topographical features of the MCSALIBDE-processed fused silica surfaces is studied. Notably, the micro-groove structures with a depth-to-width ratio of 1.19 is fabricated. Furthermore, the wetting behaviors of the MCSALIBDE-processed fused silica surfaces before and after annealing treatment are studied. Experimental findings reveal that the MCSALIBDE-processed fused silica surfaces exhibit enhanced hydrophilicity compared to the untreated one. After annealing treatment, a transformation from hydrophilicity to superhydrophobicity is observed. The superhydrophobic surfaces produced under different laser parameters exhibit distinct adhesion and droplet bouncing behaviors. This work offers an in-depth understanding of the high-efficiency fabrication of micro/nanostructures on fused silica surfaces and as well the modulation of their wetting characteristics, heralding innovations in surface engineering for diverse applications.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"145 ","pages":"Pages 236-251"},"PeriodicalIF":6.1,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven online prediction and control method for injection molding product quality","authors":"Youkang Cheng,&nbsp;Hongfei Zhan,&nbsp;Junhe Yu,&nbsp;Rui Wang","doi":"10.1016/j.jmapro.2025.04.054","DOIUrl":"10.1016/j.jmapro.2025.04.054","url":null,"abstract":"<div><div>Injection molding is a complex, non-linear production process where product quality depends on variable and interacting process parameters. In continuous mass production, fluctuations in process parameters make it impossible to ensure the stability of product quality. Existing quality control mainly relies on historical experience for manual adjustments, often leading to high scrap rates, reduced productivity, and resource consumption. Therefore, this paper proposes a data-driven quality control method for injection molded products, which uses a feature prediction model to forecast the process parameters for the next production cycle. An optimization algorithm based on the quality prediction model is used to fine-tune the process parameters, providing operators with a reasonable parameter scheme in advance. First, a time series feature prediction model is proposed based on the multiscale retention module in the Retentive Network (RetNet) model. This model integrates Empirical Mode Decomposition (EMD) and a Mixed Domain Attention Module (MDAM). The model extends features across different time dimensions using EMD to explore the time-series relationships. Additionally, a new MDAM is designed to identify key process features adaptively. Second, a quality prediction model based on the Extreme Gradient Boosting (XGBoost) algorithm is built on the feature prediction model. The output of the prediction model is used to calculate fitness. At the same time, the Dung Beetle Optimization algorithm is employed for efficient reverse search to adjust the process parameters precisely. Finally, the effectiveness of the proposed method in injection product quality control is validated through the computational analysis of two injection molding datasets, providing strong support and solutions for real-time quality management in this field.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"145 ","pages":"Pages 252-273"},"PeriodicalIF":6.1,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-efficient sequential learning framework for melt pool defect classification in Laser Powder Bed Fusion","authors":"Ahmed Shoyeb Raihan ,&nbsp;Austin Harper ,&nbsp;Israt Zarin Era ,&nbsp;Omar Al-Shebeeb ,&nbsp;Thorsten Wuest ,&nbsp;Srinjoy Das ,&nbsp;Imtiaz Ahmed","doi":"10.1016/j.jmapro.2025.03.118","DOIUrl":"10.1016/j.jmapro.2025.03.118","url":null,"abstract":"<div><div>Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects such as keyhole, balling, and lack of fusion can significantly compromise structural integrity. This study presents SL-RF+ (Sequentially Learned Random Forest with Enhanced Sampling), a novel Sequential Learning (SL) framework for melt pool defect classification designed to maximize data efficiency and model accuracy in data-scarce environments. SL-RF+ utilizes an RF classifier combined with Least Confidence Sampling (LCS) and Sobol sequence-based synthetic sampling to iteratively select the most informative samples, refining the model’s decision boundaries with minimal labeled data. Results demonstrate that SL-RF+ achieves an accuracy of 83.3%, outperforming the traditional RF model (78.8%) with significantly fewer labeled samples in melt pool defect classification. Moreover, SL-RF+ improves precision (83.1%), recall (76.9%), and F1-score (78.9%), surpassing the baseline model in all key performance metrics. Notably, SL-RF+ achieves competitive classification performance with fewer than 150 sequentially added samples, whereas the traditional RF model requires all 275 labeled samples to reach similar accuracy levels. By prioritizing high-uncertainty regions in the process parameter space, this framework efficiently captures complex defect patterns, ultimately achieving superior classification performance without the need for extensive labeled datasets. While this study utilizes pre-existing experimental data, SL-RF+ shows strong potential for real-world applications in pure sequential learning settings, where data is acquired and labeled incrementally, mitigating the high costs and time constraints of sample acquisition.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"145 ","pages":"Pages 201-210"},"PeriodicalIF":6.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Directed energy deposition on sheet metal forming for reinforcement structures","authors":"Fan Chen ,&nbsp;Rujing Zha ,&nbsp;Jihoon Jeong ,&nbsp;Shuheng Liao ,&nbsp;Jian Cao","doi":"10.1016/j.jmapro.2025.03.120","DOIUrl":"10.1016/j.jmapro.2025.03.120","url":null,"abstract":"<div><div>While incremental forming processes can inexpensively create complex geometries from sheet metal, they struggle with adding sharp out of plane features for stiffness enhancement. With the implementation of directed-energy deposition (DED), an additive manufacturing process that locally deposits metal onto metallic substrates, reinforcement structures can be formed on the sheet metal. Furthermore, a design engineer may take advantage of the high residual stresses of DED to directly alter shapes in the substrate metal sheet. This hybrid forming-deposition process, as well as the application of local reinforcement, requires a good understanding of the process mechanism to predict expected shapes and minimize undesired deformations. In this work, numerical approaches are applied to evaluate heat transfer, thermal stress, and buckling of thin sheets under the stresses of deposition. These results are compared to analogous experiments conducted on an open-architecture laser-powder DED machine. The results of the thermal-mechanical analysis resemble the deformation trends observed in the experiments. However, the small-displacement formulation in the simulation used for ease of convergence does not fully capture the magnitude of the observed deformations. Nevertheless, the simulations effectively illustrate the effect of different scan strategies on the final deformed shape of the sheet metal.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"144 ","pages":"Pages 339-349"},"PeriodicalIF":6.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based early detection of malicious G-code manipulations in 3D printing","authors":"Hala Ali,&nbsp;Alberto Cano,&nbsp;Irfan Ahmed","doi":"10.1016/j.jmapro.2025.04.012","DOIUrl":"10.1016/j.jmapro.2025.04.012","url":null,"abstract":"<div><div>The increased adoption of 3D printing across various critical manufacturing sectors has made it a fruitful target for adversaries, particularly through the manipulation of G-code instructions that control the operations of 3D printers. Simple modifications to these instructions could significantly impact the integrity of 3D-printed objects. While side-channel analysis during printing is a common detection method, identifying potential malicious G-code before printing can save time and resources. Existing work relies on primitive encryption and hashing techniques and cannot distinguish between benign and malicious G-code instructions. It assumes that G-code files are benign and uses them as a reference model, focusing only on the integrity checking of G-code during storage and transmission. This paper introduces a novel automated approach to efficiently differentiate between benign and subtly manipulated G-code caused by filament, thermodynamic, and Z-profile attacks without requiring a reference model. As the first study leveraging recent advancements in Machine Learning (ML), we address several challenges in dataset generation, feature engineering, G-code segmenting and labeling, and ML classifier selection. We generate diverse G-code datasets to identify the optimal dataset characteristics and conduct a comprehensive formal analysis to extract the most suitable features. Efficient labeling strategies are employed at both layer and command levels, using the Multiple Instance Learning (MIL) paradigm for the former. We adopt the Bidirectional Long Short-Term Memory (Bi-LSTM) model enhanced by an attention mechanism and focal loss function for layer classification. Meanwhile, the Random Forest (RF) algorithm and Multilayer Perceptron (MLP) neural network model are used for command classification. All classifiers are designed to handle the imbalanced dataset. Experimental evaluation demonstrates the efficacy of our approach. The Bi-LSTM model achieves F1 scores up to 91.3% in detecting filament attacks, while the RF algorithm performs better in detecting nuanced thermodynamic and Z-profile changes at the command level, achieving F1 scores between 81.6% and 99.3%.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"145 ","pages":"Pages 211-235"},"PeriodicalIF":6.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fabrication and characterization of 3D micro-coils with hybrid manufacturing methods","authors":"Hakan Alaboz ,&nbsp;Andras Kovacs ,&nbsp;Daniel Johannes Förster ,&nbsp;Lucas Werling ,&nbsp;Jajnabalkya Guhathakurta ,&nbsp;Julien Petit ,&nbsp;Morgan Madec ,&nbsp;Luc Hébrard ,&nbsp;Ulrich Mescheder","doi":"10.1016/j.jmapro.2025.04.040","DOIUrl":"10.1016/j.jmapro.2025.04.040","url":null,"abstract":"<div><div>This study presents novel fabrication steps and hybrid approaches that combine contemporary methods with modified manufacturing strategies for fabricating 3D micro-coils on tubular surfaces. Although several methods have been developed in the last decade, most of them still rely on complicated multi-step lithography processes or sophisticated devices. In this article, rapid, simple, and straightforward approaches are presented for fabricating micro-coils directly on curved surfaces. The hybrid fabrication methods in this study provide an alternative solution to reduce the complexity in fabrication steps, provide better control over aspect ratios and the homogeneity of thin film coatings on tubular surfaces. Micro-coils from copper and conductive polymer with outer diameter of 2.5 mm and line width of 40 μm and a separation of 110 μm were designed, produced, and characterized. Magnetic fields inside the coils made of copper and conductive polymer were measured as 11 and 2.5 μT, respectively. The developed coating and structuring methods will open new avenues not only in application fields such as analytical characterization methods of fluidic samples with miniaturized nuclear magnetic resonance (μNMR) systems, but also for 3D sensor fabrication on curved surfaces.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"145 ","pages":"Pages 190-200"},"PeriodicalIF":6.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A non-intrusive interval analysis method for chatter stability of uncertain milling systems","authors":"Jiachang Tang ,&nbsp;Taolin Zhang ,&nbsp;Tao Liu ,&nbsp;Zhanzhan Zhang ,&nbsp;Lixiong Cao ,&nbsp;Qishui Yao","doi":"10.1016/j.jmapro.2025.04.047","DOIUrl":"10.1016/j.jmapro.2025.04.047","url":null,"abstract":"<div><div>A non-intrusive interval analysis method is proposed to evaluate the chatter stability of uncertain milling systems. First, the interval theory is introduced to establish the interval milling system dynamics model. Second, a novel contraction and expansion sampling strategy is proposed to obtain the upper and lower bounds of the stability lobes diagram (SLD) in the solution process inspired by the simplex method. Subsequently, a convergence mechanism is proposed based on the edge detection technique to improve the convergence rate of the bounds calculation of the SLDs. The mean square error and structural similarity of the two iteration results were calculated to control the iteration. In addition, the proposed method is non-intrusive. It can be employed by selecting the corresponding chatter stability analysis method according to the characteristics of different milling systems, without being limited to a specific analysis method. Finally, the accuracy and efficiency are verified by the examples of one and two degrees of freedom milling systems, and the feasibility of the proposed method is validated through experiments.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"145 ","pages":"Pages 142-157"},"PeriodicalIF":6.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}