Journal of Manufacturing Processes最新文献_第8页

Reducing tool wear in high-speed milling of Inconel 718 by optimizing flank-face coolant direction: A CFD-supported approach toward sustainable machining 通过优化侧面冷却剂方向来减少高速铣削Inconel 718的刀具磨损：cfd支持的可持续加工方法

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-21 DOI: 10.1016/j.jmapro.2026.01.065

Jingtian Mao , Kensuke Tsuchiya , Chikara Morigo , Shinji Yukinari , Hiroki Tahara , Yoshihide Kurashiki

This study investigates the impact of flank face coolant nozzle orientation on tool wear suppression in the high-speed milling of Inconel 718. Preliminary dry milling of Ti-6Al-4V with a Tool-Nose-Aimed (TNA) tool revealed severe flank wear at the bolt side edge. This failure mode persisted in Inconel 718, where the TNA tool suffered from severe adhesion and brittle fracture at this location under dry, flood and all high-pressure coolant (HPC) conditions except ultra-high pressure coolant (UHPC) at 20 MPa. Thermal and Computational Fluid Dynamics (CFD) simulations diagnosed the cause: the bolt side edge is a thermal throttling zone, and the TNA's coolant jet core deviates from this critical spot. Guided by this analysis, a novel Bolt-Side-Edge-Flank-Aimed (BSEFA) tool was designed. Its nozzle orientation was optimized to ensure the jet core directly impinges on the flank face of the bolt side edge, enhancing convective heat transfer through higher liquidity, velocity, and Turbulence Kinetic Energy (TKE). Experimentally, the BSEFA tool suppressed catastrophic failure, massive adhesion and reduced maximum flank wear (VB_max) within the wear land by 40–56% compared to the TNA tool. CFD results confirmed the mechanism, showing the optimized nozzle delivered superior coolant coverage (liquidity >0.95), higher velocity (>110 m/s), and drastically intensified turbulence (TKE increase >150%) at the target. This work establishes that strategic coolant orientation surpasses indiscriminate pressure increase. The BSEFA strategy enables high performance with minimal flow rate (<1.0 L/min), representing a > 95% reduction versus flood cooling, offering a highly efficient and sustainable machining strategy.

研究了高速铣削Inconel 718时，后端面冷却液喷嘴取向对刀具磨损抑制的影响。用工具头瞄准（TNA）工具对Ti-6Al-4V进行初步干铣削，发现螺栓侧面边缘存在严重的侧面磨损。这种失效模式在Inconel 718中持续存在，在干燥、洪水和所有高压冷却剂（HPC）条件下（20mpa的超高压冷却剂（UHPC）除外），TNA工具在该位置都存在严重的粘连和脆性断裂。热学和计算流体动力学（CFD）模拟诊断了原因：螺栓侧边缘是一个热节流区，TNA的冷却剂射流核心偏离了这个临界点。在此基础上，设计了一种新型的螺栓-侧面-边缘-侧翼瞄准（BSEFA）工具。它的喷嘴方向进行了优化，以确保射流核心直接撞击螺栓侧面边缘的侧面，通过更高的流动性、速度和湍流动能（TKE）来增强对流换热。实验表明，与TNA工具相比，BSEFA工具抑制了灾难性失效、大量粘附，并将磨损区域内的最大侧面磨损（VBmax）降低了40-56%。CFD结果证实了这一机制，优化后的喷嘴提供了更好的冷却剂覆盖范围（流动性>；0.95），更高的速度（>110 m/s），并大幅加剧了目标处的湍流（TKE增加了>；150%）。这项工作确立了战略性冷却剂定向优于无差别的压力增加。BSEFA策略以最小的流量（1.0 L/min）实现高性能，与洪水冷却相比减少了95%，提供了高效和可持续的加工策略。

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引用次数: 0

A physics-informed neural network with adaptive loss weighting for tool wear and remaining useful life prediction in deep hole boring 一种具有自适应损失加权的物理信息神经网络，用于深孔钻孔中刀具磨损和剩余使用寿命预测

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-21 DOI: 10.1016/j.jmapro.2026.01.047

Pengfei Zhang , Hanxiao Zhao , Ang Li , Chao Sun , Hongzhe Zhang , Naohiko Sugita , Liming Shu

Tool wear and remaining useful life (RUL) prediction are critical for ensuring machining quality and reducing production costs, playing an important role in deep-hole machining. Recently, physics-informed neural network (PINN) has attracted great attention to achieve this goal. However, the weights between physics-based models and data-driven models are often set empirically, which severely affects training accuracy and stability. To address this issue, this paper proposes a PINN with adaptive loss weighting, by quantifying the variance of prediction errors for tool wear and RUL prediction. First, multi-channel signals in deep hole boring are used to extract time-domain and frequency-domain features. Then, correlation coefficients between tool wear and features are calculated for feature selection, and combined with cutting stroke information to form the dataset. Next, based on the cutting stroke and flank wear values, a tool wear rate model is constructed using the least squares method. This equation serves as the physical consistency constraint of the PINN. The total loss function is constructed by combining the data loss from the data-driven model, the monotonicity loss, and the physical consistency loss. Finally, based on the AutoRegressive Integrated Moving Average (ARIMA) model and historical tool wear values, multi-step-ahead forecasting of tool wear and RUL prediction are achieved. Results show that the proposed PINN with adaptive loss weighting achieves the best tool wear prediction performance, compared with PINNs without weight adjustment (fixed weights), without monotonicity constraints, or without physical consistency constraints. Moreover, ARIMA multi-step-ahead forecasts closely match the measured tool wear and outperform the GRU baseline. The findings of this paper lay the foundation for automation and even unmanned operation in deep-hole machining.

刀具磨损和剩余使用寿命（RUL）预测是保证加工质量和降低生产成本的关键，在深孔加工中起着重要作用。近年来，物理信息神经网络（PINN）在实现这一目标方面引起了人们的广泛关注。然而，基于物理的模型和数据驱动的模型之间的权重往往是经验设定的，这严重影响了训练的准确性和稳定性。为了解决这一问题，本文通过量化刀具磨损和RUL预测误差的方差，提出了一种自适应损失加权的PINN。首先，利用深孔掘进过程中的多通道信号提取深孔掘进过程的时域和频域特征；然后，计算刀具磨损与特征之间的相关系数进行特征选择，并结合切削行程信息形成数据集。其次，基于切削行程和刀面磨损值，采用最小二乘法建立刀具磨损率模型；该方程作为PINN的物理一致性约束。综合考虑数据驱动模型的数据损失、单调性损失和物理一致性损失，构造了总损失函数。最后，基于自回归综合移动平均（ARIMA）模型和刀具历史磨损值，实现了刀具磨损的多步预测和RUL预测。结果表明，与无权值调整（固定权值）、无单调性约束或无物理一致性约束的PINN相比，本文提出的具有自适应损失加权的PINN具有最佳的刀具磨损预测性能。此外，ARIMA多步提前预测与测量的工具磨损密切匹配，优于GRU基线。本文的研究结果为深孔加工的自动化甚至无人操作奠定了基础。

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引用次数: 0

Electric arc electrochemical machining: Synergistic effect, material removal mechanism and process research 电弧电化学加工：协同效应、材料去除机理及工艺研究

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-22 DOI: 10.1016/j.jmapro.2026.01.057

Shengsheng Zhang , Yinan Zhao , Jianping Zhou , Xiaoxiao Chen , Yufeng Wang , Wenwu Zhang

This paper presents a hybrid process- electric arc electrochemical machining (EAECM). This process utilizes synergistic effect of discharge and dissolution to achieve efficient and high quality manufacturing. The surface polishing characteristics, formation and dissolution behavior of recast layer, overcutting, electrode wear mechanism, and product properties of EAECM were experimentally investigated. The results show that EAECM can dissolve recast layer and polish surface at non-discharge moments while maintaining the efficiency of EAM. There is significant anisotropy in the recast layer, and the solidification rate difference also leads to the proliferation of internal grain dislocations, causing residual tensile stress to fail the material. EAECM eliminates the residual tensile stress during machining and restores the material's own property. In addition, EAECM has a heat diffusion inhibition behavior, which has a protective effect on the substrate. The alternating discharge and dissolution points of EAECM, combined with the limited dissolution time of the high-speed moving electrode, the electrolyte concentration of 10–15 Wt% is still no over-corrosion. The synergistic effect of lightweight electrolytic products with tiny volume and safe distance improves the discharge state and reduces the electrode wear, consequently maintaining the shape accuracy. The process performance of conventional electric arc machining (EAM) and EAECM was compared to demonstrate the process value of EAECM. Parameter optimization was also performed to enhance the process potential. Finally, the engineering potential of EAECM was demonstrated by some artifacts.

提出了一种电弧电化学复合加工方法。该工艺利用排放和溶解的协同效应，实现高效率和高质量的生产。实验研究了EAECM的表面抛光特性、重铸层的形成和溶解行为、过切削、电极磨损机理和产品性能。结果表明，EAECM可以在不放电时刻溶解重铸层和抛光表面，同时保持EAECM的效率。在重铸层中存在明显的各向异性，凝固速率的差异也导致了内部晶粒位错的扩散，导致材料的残余拉应力失效。EAECM消除了加工过程中的残余拉伸应力，恢复了材料本身的性能。此外，EAECM还具有热扩散抑制行为，对衬底有保护作用。EAECM的交变放电和溶解点，结合高速移动电极的有限溶解时间，电解液浓度在10-15 Wt%时仍无过腐蚀。体积小、安全距离小的轻量化电解产品的协同作用，改善了放电状态，减少了电极磨损，从而保持了形状精度。对比了传统电弧加工（EAM）和EAECM的工艺性能，论证了EAECM的工艺价值。并对工艺参数进行了优化，以提高工艺潜力。最后，通过一些人工制品展示了EAECM的工程潜力。

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引用次数: 0

Retraction notice to “The role of defect structure and residual stress on fatigue failure mechanisms of Ti-6Al-4V manufactured via laser powder bed fusion: Effect of process parameters and geometrical factors” [Journal of Manufacturing Processes 102 (2023) 549–563] “缺陷结构和残余应力在Ti-6Al-4V激光粉末床熔合疲劳失效机制中的作用：工艺参数和几何因素的影响”[j] .制造工艺学报，102(2023):549-563。

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-27 DOI: 10.1016/j.jmapro.2025.12.069

Seyed Mehrab Hosseini , Ehsan Vaghefi , Elham Mirkoohi

引用次数: 0

A Physics-Informed Neural Network framework with strong robustness to low-accuracy physical models for predicting adhesive wear of self-made BNNC milling tool 自制BNNC铣刀黏着磨损预测的物理信息神经网络框架对低精度物理模型具有强鲁棒性

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-23 DOI: 10.1016/j.jmapro.2026.01.049

Shengyue Tan , Dongqian Wang , Yongliang Liu , Yonglin Cai , Jia Wei , Lei Wang , Uwe Teicher , Albrecht Hänel , Steffen Ihlenfeldt , Zhiqiang Liang

In high-speed hard milling, flank wear prediction of ultra-hard tools is necessary, where accuracy and stability are the two key indicators. Physics-Informed Neural Network (PINN) improves the prediction stability by embedding consistent physical laws into the training process. However, when low-accuracy physical models are commonly employed to constrain the solution space, optimization paths may be misled. This limits the performance of PINNs and their weighted frameworks. To address the issue of low-accuracy physical model misleading the optimization direction, a novel framework termed Physics-Informed Weighted Neural Network based on Prediction Error of Physics-driven models (PIWNN-PEP) is proposed. PIWNN-PEP can enhance the robustness of PINN against low-accuracy models. Furthermore, to capture long-term and complex dependencies over long time scales, a collaborative network xLSTM-Informer (xICNet) with a stacked mLSTM-sLSTM-Informer architecture is established, of which xICNet directly builds a mapping between multidimensional cutting forces and wear values within the PIWNN-PEP framework. The experimental result demonstrates that the proposed method prominently enhances robustness to low-accuracy physical models, compared with existing weighted PINN frameworks. The average tool wear prediction error remains below 1 μm.

在高速硬铣削中，超硬刀具的刃口磨损预测是必要的，其中精度和稳定性是两个关键指标。物理信息神经网络（PINN）通过在训练过程中嵌入一致的物理定律来提高预测的稳定性。然而，当通常使用低精度的物理模型来约束解空间时，可能会误导优化路径。这限制了pin及其加权框架的性能。为了解决低精度物理模型误导优化方向的问题，提出了一种基于物理驱动模型预测误差的物理知情加权神经网络框架（PIWNN-PEP）。PIWNN-PEP可以增强PINN对低精度模型的鲁棒性。此外，为了捕获长时间尺度的长期和复杂依赖关系，建立了一个具有堆叠mLSTM-sLSTM-Informer架构的协作网络xLSTM-Informer (xICNet)，其中xICNet在PIWNN-PEP框架内直接建立了多维切削力与磨损值之间的映射。实验结果表明，与现有的加权PINN框架相比，该方法显著提高了对低精度物理模型的鲁棒性。平均刀具磨损预测误差保持在1 μm以下。

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引用次数: 0

Electro-magnetic coupled field-assisted laser-directed energy of Ni-based WC composite coatings: Defect suppression, microstructural evolution, and tribological behavior 电磁耦合场辅助激光定向能制备镍基WC复合涂层：缺陷抑制、微观组织演变和摩擦学行为

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-26 DOI: 10.1016/j.jmapro.2026.01.069

Wanyang Li , Weiwei Liu , Huanqiang Liu , Junjie Tan , Haojun Yang , Baimao Lei , Qiang Chen , Jianrong Song , Zongyu Ma , Tao Li , Yulin Wang , Fengtao Wang , Hongchao Zhang

In this study, laser-directed energy deposition (LDED) assisted by an electromagnetic coupled field (EMF) was employed to systematically investigate the effects of current intensity and direction on melt pool dynamics, defect evolution, microstructural evolution, and tribological behavior of composite coatings. The coupled field reshaped melt pool flow patterns and effectively suppress pores and cracks, reducing the defect density to 0.075%. Electromagnetic stirring effect enhanced solute redistribution and grain morphology transformation, unveiling a coupled mechanism of “grain-boundary transformation-defect accumulation-strain release”. EBSD and TEM analyses revealed a microstructural transition from a “WC/W₂C-dominated” state to a “γ-Ni matrix-Cr carbide co-dominated” configuration, which regulated interfacial carbon activity, promoted stable Cr₇C₃ precipitation, and facilitated the formation of Ni₃Si + γ-Ni(Fe,Cr) eutectics. An appropriate current intensity further facilitated particle redistribution, suppressed fatigue spalling, and enhanced wear resistance by nearly 64.70%. These findings demonstrate that the external coupled field regulates microstructural evolution and wear behavior, providing new processing pathways for tailoring the performance of high-performance composite coatings.

在本研究中，采用电磁场辅助下的激光定向能沉积（LDED）技术，系统地研究了电流强度和方向对复合涂层熔池动力学、缺陷演变、微观组织演变和摩擦学行为的影响。耦合场重塑了熔池流动模式，有效抑制了气孔和裂纹，将缺陷密度降低到0.075%。电磁搅拌效应增强了溶质重分布和晶粒形貌转变，揭示了“晶界转变-缺陷积累-应变释放”的耦合机制。EBSD和TEM分析表明，微观结构从“WC/ w2c主导”状态转变为“γ-Ni基体-Cr碳化物共同主导”结构，这一结构调节了界面碳活性，促进了Cr7C3的稳定析出，并促进了Ni3Si + γ-Ni（Fe,Cr）共晶的形成。适当的电流强度进一步促进了颗粒的再分布，抑制了疲劳剥落，耐磨性提高了近64.70%。这些发现表明，外部耦合场调节了微观组织演变和磨损行为，为定制高性能复合涂层的性能提供了新的加工途径。

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引用次数: 0

Machine learning prediction of recoater damage topography deviations 重涂器损伤形貌偏差的机器学习预测

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-28 DOI: 10.1016/j.jmapro.2026.01.060

Caroline E. Massey , Christopher J. Saldaña

In-situ monitoring in laser powder bed fusion (PBF-LB) presents a paradigm for progress towards born qualified parts. This technology has proven useful in many applications such as monitoring for geometric error, layer-wise part defects, and spreading defects. The significance of spreading defects is particularly understudied, especially in the experimental domain. Recoater damage can be particularly detrimental to mechanical performance, as it lends to topography deviations in the build, which could cause porosity, geometric inaccuracies, and potential build failure. Yet, prior literature has not addressed machine learning's ability to predict the severity of recoater damage. This work used multiple feature-based and image-based machine learning algorithms combined with in-situ layer-wise monitoring to predict the amount of topography deviation within recoater damaged sections. The height and width of the topography deviations were measured after the spread profile was exposed to multiple different sizes of recoater wear at different recoater spread speeds and layer thicknesses. The acquired images had different image filtering methods applied to see if a particular image filtering method can increase prediction performance. Ultimately, the image-based machine learning methods showed the best performance when combined with noising filters. In all, this work seeks to find the ideal configuration for the prediction of topography height and width deviations when the powder bed is exposed to recoater damage.

激光粉末床熔合（PBF-LB）的现场监测为生产合格零件提供了一种范式。该技术已被证明在许多应用中是有用的，例如监测几何误差、分层零件缺陷和扩展缺陷。对于扩展缺陷的重要性，特别是在实验领域的研究尤其不足。重接器损坏对机械性能尤其有害，因为它会导致构造中的地形偏差，从而导致孔隙度、几何不精确和潜在的构造失败。然而，之前的文献并没有提到机器学习预测重涂器损伤严重程度的能力。这项工作使用了多种基于特征和基于图像的机器学习算法，并结合现场分层监测来预测重涂器损坏截面内的地形偏差量。在不同涂布速度和涂布层厚度下，涂布轮廓暴露于多种不同尺寸的涂布磨损后，测量了形貌偏差的高度和宽度。获取的图像采用不同的图像滤波方法，以观察特定的图像滤波方法是否可以提高预测性能。最终，基于图像的机器学习方法在与噪声滤波器相结合时表现出最佳性能。总之，这项工作旨在找到理想的配置，以预测地形高度和宽度偏差，当粉末床暴露在重涂器损坏。

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引用次数: 0

Improving curved surface machining quality of blasting erosion arc milling by applying working fluid blockers 应用工作液阻断剂改善喷蚀弧铣曲面加工质量

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-20 DOI: 10.1016/j.jmapro.2026.01.056

Lin Gu , Kelin Li , Guojian He , Lijie Jiang , Xiaoka Wang

Electrical Arc Machining (EAM) is a promising method for processing difficult-to-cut materials, offering a satisfactory material removal rate and high efficiency. It has been applied in the aerospace industry to remove most of the residue from the blank to save machining time and cost. However, for aerospace parts with complex curved surfaces, such as blades, turbine disks, and impellers, it's prone to lead to intensive working fluid leakage during machining. This fluid leakage adversely recedes the arc breaking effect and results in an unacceptable coarse surface. To address this issue, this study defines the criteria for achieving good machined surface by EAM and proposes the working fluid guiding strategy including internal, external, and combined guiding approaches. The flow field is simulated and a blocker is designed to study the working fluid guiding strategy for the suppression of flushing deficiency. The results indicate that the combined strategy yields the most significant improvement effect, increasing the ratio of effective flushing by 1.2 times and desirable discharge rate by over 35%. Additionally, it noticeably reduces the surface roughness and the thickness of the recast layer. The validity of this novel approach is further demonstrated through the machining of a three-dimensional flow impeller sample using Blasting Erosion Arc Machining (BEAM) with the working fluid guiding strategy.

电弧加工（EAM）是一种很有前途的加工难切削材料的方法，具有令人满意的材料去除率和高效率。它已应用于航空航天工业，以去除毛坯中的大部分残留物，节省加工时间和成本。然而，对于具有复杂曲面的航空航天零件，如叶片、涡轮盘和叶轮，在加工过程中容易导致大量的工作液泄漏。这种流体泄漏对断弧效果不利，并导致不可接受的粗糙表面。为了解决这一问题，本研究定义了EAM获得良好加工表面的标准，并提出了包括内部导向、外部导向和组合导向在内的工作流体导向策略。模拟了流场，设计了阻挡剂，研究了抑制冲洗不足的工质导液策略。结果表明，该组合策略改善效果最显著，有效冲洗率提高1.2倍，理想排放率提高35%以上。此外，它显著降低了表面粗糙度和重铸层的厚度。通过采用工作流体导向策略对三维流动叶轮进行喷蚀弧加工（BEAM），进一步验证了该方法的有效性。

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引用次数: 0

QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with vision language models QA-VLM：利用视觉语言模型为线喂激光增材制造零件提供人类可解释的质量评估

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-30 DOI: 10.1016/j.jmapro.2026.01.071

Qiaojie Zheng , Jiucai Zhang , Joy Gockel , Michael B. Wakin , Craig Brice , Xiaoli Zhang

Image-based quality assessment (QA) in additive manufacturing (AM) often relies heavily on the expertise and constant attention of skilled human operators. While machine learning and deep learning methods have been introduced to assist in this task, they typically provide black-box outputs without interpretable justifications, limiting their trust and adoption in real-world settings. In this work, we introduce a novel QA-VLM framework that leverages the attention mechanisms and reasoning capabilities of vision-language models (VLMs), enriched with application-specific knowledge distilled from peer-reviewed journal articles, to generate human-interpretable quality assessments. When evaluated on 24 single-bead samples produced by laser wire direct energy deposition (DED-LW), our framework demonstrates higher validity and consistency in explanation quality than off-the-shelf VLMs. These findings indicate that the literature-supported quality assessment model has the potential to improve reliability for QA tasks, motivating future validation on larger, multi-layer, and multi-pass builds, and broader process/material conditions.

增材制造（AM）中基于图像的质量评估（QA）通常在很大程度上依赖于熟练操作人员的专业知识和持续关注。虽然已经引入了机器学习和深度学习方法来协助完成这项任务，但它们通常提供没有可解释理由的黑箱输出，限制了它们在现实环境中的信任和采用。在这项工作中，我们引入了一种新的QA-VLM框架，该框架利用视觉语言模型（vlm）的注意机制和推理能力，丰富了从同行评审的期刊文章中提取的应用特定知识，以生成人类可解释的质量评估。通过对24个由激光线直接能量沉积（ed - lw）产生的单头样品进行评估，我们的框架在解释质量上比现成的vlm具有更高的有效性和一致性。这些发现表明，文献支持的质量评估模型有潜力提高QA任务的可靠性，激励未来在更大、多层、多通道构建和更广泛的工艺/材料条件下进行验证。

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引用次数: 0

An improved residual height model for compliant robotic belt grinding with application to dwell time optimization 一种改进的柔性机器人带磨削剩余高度模型及其在停留时间优化中的应用

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-28 Epub Date: 2026-01-22 DOI: 10.1016/j.jmapro.2026.01.045

Xiaoyu Zhao , Lai Zou , Kangkang Song , Wenxi Wang , Yilin Mu

Compliant robotic belt grinding is widely used for finishing complex components like turbine blades and blisks. However, conventional geometric models often fail to predict residual height accurately under force-compliant conditions. This inaccuracy arises because the actual material removal profile is dynamically distorted by the coupling of contact wheel elastic deformation and time-varying abrasive wear. To address these effects, an improved residual height model is presented for structured abrasive belts. By integrating Hertzian contact mechanics and Archard's wear law, the model explicitly quantifies the over-grinding effect caused by pressure superposition in overlapping zones and the degradation of abrasive grains. Based on this predictive model, a dwell-time optimization strategy is formulated to coordinate process efficiency and contour precision under constraints of residual height, chord error, and allowance matching. Experimental validation on curved TC4 titanium alloy components shows that the proposed approach reduces residual height prediction error to within ±10 μm. Under the tested conditions, surface roughness decreased by approximately 30%, and profile accuracy by 25% compared to constant-feed strategies, demonstrating the engineering feasibility of the proposed framework.

柔性机器人带磨削广泛应用于涡轮叶片、轮盘等复杂部件的磨削加工。然而，传统的几何模型往往不能准确地预测力柔条件下的剩余高度。由于接触轮弹性变形和时变磨料磨损的耦合作用，实际的材料去除轮廓发生了动态畸变，从而产生了这种不准确性。为了解决这些影响，提出了一种改进的结构砂带剩余高度模型。该模型通过整合赫兹接触力学和阿卡德磨损定律，明确量化了重叠区压力叠加和磨粒退化造成的过磨效应。基于该预测模型，在残差高度、弦差和余量匹配约束下，制定了加工效率和轮廓精度的停留时间优化策略。对弯曲TC4钛合金部件的实验验证表明，该方法可将剩余高度预测误差降低到±10 μm以内。在测试条件下，与恒定进给策略相比，表面粗糙度降低了约30%，轮廓精度降低了25%，证明了所提出框架的工程可行性。

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引用次数: 0