Journal of Manufacturing Processes最新文献_第2页

Microstructure, mechanical properties and corrosion resistance of FeCoCrNiMo0.2/ER120s-G gradient structures fabricated by arcing-wire powder hybrid additive manufacturing 电弧丝粉复合增材制造feccrnimo0.2 /ER120s-G梯度结构的显微组织、力学性能及耐蚀性

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-01-28 DOI: 10.1016/j.jmapro.2026.01.086

Chuanqi Liu , Yugang Miao , Ji Liu , Yuyang Zhao , Yuhang Yang , Yifan Wu , Zhiqiang Gao

Functionally graded materials (FGMs) offer a pathway to reconcile conflicting requirements of strength, ductility, and corrosion resistance in structural applications. Here we report the fabrication of FeCoCrNiMo_0.2 high-entropy alloy (HEA)/ER120S-G steel gradient structures using an arcing-wire powder hybrid additive manufacturing (AWPH-AM) approach. By continuously varying the wire–powder feed ratio, we achieve in situ control of phase evolution, grain orientation, and passive-film chemistry across the compositional gradient. Microstructural analysis reveals a progressive transition from acicular ferrite to FCC-dominated solid solutions, accompanied by Mo-induced grain-boundary precipitation at high HEA fractions. Mechanical testing shows a trade-off between strength and ductility: steel-rich layers exhibit ultimate tensile strengths approximately1200 MPa with limited elongation, whereas intermediate layers achieve elongation above 30% owing to stable FCC solid solutions. At higher HEA content, precipitation of Mo-rich phases enhances hardness but induces brittle fracture. Electrochemical testing demonstrates a systematic improvement in corrosion resistance with increasing HEA fraction, culminating in the formation of a self-healing Cr₂O₃–MoO_x composite passive film that provides superior protection in chloride environments. This work establishes AWPH-AM as a versatile platform for the design of FGMs, and demonstrates composition–microstructure-property coupling as a strategy to balance strength, ductility, and corrosion resistance in demanding marine and energy applications.

功能梯度材料（fgm）为结构应用中对强度、延展性和耐腐蚀性的矛盾要求提供了一条途径。本文报道了采用电弧线粉末混合增材制造（AWPH-AM）方法制备feccrnimo0.2高熵合金(HEA)/ER120S-G钢梯度结构。通过连续改变线粉进料比，我们实现了跨成分梯度的相演化、晶粒取向和被动膜化学的原位控制。显微组织分析表明，在高HEA分数下，铁素体逐渐转变为fcc主导的固溶体，并伴有mo诱导的晶界析出。力学测试显示了强度和延性之间的权衡：富钢层的极限抗拉强度约为1200 MPa，延伸率有限，而中间层由于稳定的FCC固溶体，延伸率超过30%。在HEA含量较高时，富mo相的析出提高了硬度，但导致脆性断裂。电化学测试表明，随着HEA含量的增加，抗腐蚀性能有了系统性的提高，最终形成了自修复的Cr2O3-MoOx复合钝化膜，在氯化物环境中提供了更好的保护。这项工作建立了AWPH-AM作为fgm设计的通用平台，并证明了成分-微观结构-性能耦合是一种平衡强度、延展性和耐腐蚀性的策略，适用于要求苛刻的海洋和能源应用。

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引用次数: 0

Sensor and feature selection for cost- and time-efficient online monitoring of ultrasonic metal welding 超声金属焊接在线监测的传感器和特征选择

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-01-28 DOI: 10.1016/j.jmapro.2026.01.034

Kuan-Chieh Lu , Zhiqiao Dong , Chenhui Shao

Ultrasonic metal welding (UMW) is a solid-state joining process widely used in industrial applications. However, its sensitivity to tool wear, surface contamination, and material variability presents persistent challenges for ensuring weld quality. Existing online monitoring systems often emphasize predictive accuracy while neglecting practical constraints such as hardware cost, data acquisition rate, and computational latency. To overcome this gap, this paper develops a systematic framework for cost- and time-efficient sensor and feature selection in UMW monitoring. The proposed method integrates signal decomposition, feature importance analysis, cost-aware genetic algorithm optimization, and a separability-analysis-based adaptation mechanism to identify an optimal subset of sensors, features, and time segments that balance predictive accuracy with resource efficiency. Extensive case studies using a multi-sensor data acquisition system demonstrate that the framework achieves high monitoring accuracy in both weld quality prediction and mixed tool and sample surface condition classification while reducing the feature pool by 96.8%–99.4%. Even under reduced sampling frequency (6.25 kHz) and shortened time windows (0.3 s), the model maintains strong predictive performance. Furthermore, the separability-analysis-based adaptation accurately recognizes new fault types using only three samples, reducing retraining data requirements by 90%. Overall, the proposed framework provides a new, scalable solution for cost- and time-efficient UMW monitoring and establishes a foundation for adaptive, lightweight monitoring systems applicable to other manufacturing processes.

超声波金属焊接（UMW）是一种广泛应用于工业的固态焊接工艺。然而，它对工具磨损、表面污染和材料可变性的敏感性为确保焊接质量带来了持续的挑战。现有的在线监测系统往往强调预测的准确性，而忽略了实际的限制，如硬件成本、数据采集率和计算延迟。为了克服这一缺陷，本文开发了一种具有成本效益和时间效益的传感器和特征选择系统框架。该方法集成了信号分解、特征重要性分析、成本感知遗传算法优化和基于可分性分析的自适应机制，以识别传感器、特征和时间段的最优子集，从而平衡预测精度和资源效率。使用多传感器数据采集系统的大量案例研究表明，该框架在焊缝质量预测和混合工具和样品表面状况分类方面都具有很高的监测精度，同时将特征库减少了96.8%-99.4%。即使在降低采样频率（6.25 kHz）和缩短时间窗（0.3 s）的情况下，该模型仍保持较强的预测性能。此外，基于可分离性分析的自适应仅使用三个样本就能准确识别新的故障类型，将再训练数据需求减少了90%。总体而言，所提出的框架为成本和时间效率高的UMW监控提供了一种新的可扩展解决方案，并为适用于其他制造工艺的自适应轻量级监控系统奠定了基础。

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引用次数: 0

Melt-casting parameters optimization of energetic materials for minimizing shrinkage and solidification time via adaptive clustering local Kriging and NSGA II-MOHHO 基于自适应聚类局部Kriging和NSGA II-MOHHO的含能材料熔铸参数优化研究

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-01-28 DOI: 10.1016/j.jmapro.2026.01.068

Xiaocheng Tian , Yufeng Li , Youshuo Zhang , Yan He

Optimizing the process parameters of the melt-casting solidification process for energetic materials (MCSPEM) is crucial for improving the quality and efficiency of melt-casting forming systems. The influence of melt-casting process parameters on shrinkage volume (SV) and solidification time (ST) exhibited a highly nonlinear correlation, with significant interactive effects among variables. However, existing process parameters control primarily relies on manual experience, lacking quantitative characterization and co-optimization of MCSPEM parameters concerning SV and ST, leading to inconsistent quality and low efficiency. Therefore, this paper proposed a multi-objective optimization approach to identify the optimal MCSPEM parameters based on adaptive clustering local Kriging (ACLK) and NSGA II-MOHHO algorithm. Firstly, the nonlinear associations of MCSPEM parameters (i.e., pouring temperature, mold preheating temperature, riser insulation temperature and time, jacket insulation temperature and time) with SV and ST were accurately established using the ACLK model. Secondly, a bi-objective optimization model involving SV and ST was established under the process constraints. Thirdly, a hybrid NSGA II-MOHHO algorithm was developed to tackle the bi-objective optimization model, integrating NSGA II's strengths in solution diversity with MOHHO's advantages in adaptive exploration-exploitation switching. Finally, the EWM-TOPSIS method was applied to obtain the optimal MCSPEM parameters from the Pareto front. Case results show that compared with the empirical scheme, the proposed method reduced SV and ST by 54.02% and 16.68%, respectively. This method can recommend the optimal configuration of MCSPEM process parameters and provide quantitative SV and ST information to guide technicians in accurately optimizing and controlling forming defects and efficiency.

优化含能材料熔铸凝固工艺参数对提高熔铸成形系统的质量和效率至关重要。熔铸工艺参数对收缩体积（SV）和凝固时间（ST）的影响呈高度非线性相关，变量之间存在显著的交互作用。然而，现有的工艺参数控制主要依靠人工经验，缺乏对SV和ST的MCSPEM参数的定量表征和协同优化，导致质量不一致，效率低。为此，本文提出了一种基于自适应聚类局部Kriging （ACLK）和NSGA II-MOHHO算法的多目标优化方法来识别最优MCSPEM参数。首先，利用ACLK模型精确建立了MCSPEM参数（浇注温度、模具预热温度、冒口保温温度和时间、夹套保温温度和时间）与SV和ST之间的非线性关系。其次，在工艺约束下建立了SV和ST的双目标优化模型；第三，结合NSGA II在解多样性方面的优势和MOHHO在自适应探索开发切换方面的优势，提出了一种NSGA II-MOHHO混合算法来解决双目标优化模型。最后，应用EWM-TOPSIS方法从Pareto前沿得到最优MCSPEM参数。实例结果表明，与经验方案相比，该方法分别减少了54.02%的SV和16.68%的ST。该方法可以推荐MCSPEM工艺参数的最优配置，并提供定量的SV和ST信息，指导技术人员准确优化和控制成形缺陷和效率。

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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 : 2026-01-28 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

An unsupervised welding quality detection method based on high-quality condition-guided diffusion reconstruction 基于高质量状态引导扩散重构的无监督焊接质量检测方法

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-01-27 DOI: 10.1016/j.jmapro.2026.01.059

Xin Chen , Bin Zi , Kai Tang , Wenjun Tang , Yuan Li

Radiographic testing of welds plays a critical role in ensuring the quality of welded manufacturing, because X-ray imaging technology can clearly reveal the internal structure of the weld area. However, existing mainstream detection methods rely on manual inspection or supervised detection, both of which are susceptible to limitations imposed by subjective factors and model generalization capabilities, respectively. Therefore, this paper proposes a two-stage unsupervised detection framework based on reconstruction to achieve fast and accurate detection of welding quality. First, an algorithm for generating simulated defects based on real welding defect characteristics is designed. A dataset encompassing multiple defect types is constructed, and image quality is further optimized through data augmentation algorithms. Second, a high-quality diffusion model (H-DiffuM) based on residual learning is proposed, which achieves accurate reconstruction of weld defect images through a residual-guided noise scheduling mechanism. Finally, by combining the gated mechanism with frequency domain features of X-ray images, a multi-scale frequency domain attention fusion module (MFDAFM) is designed and embedded into the discriminative network (Seg-net), thereby enhancing detection accuracy. The final experimental results demonstrated that the proposed method achieved 97.80% in pixel-level AUROC and 93.34% in AP, which surpassed the current state-of-the-art unsupervised detection approaches. Meanwhile, the inspection method described in this paper offers the advantages of rapid detection speed and high precision, demonstrating its potential for application in the rapid assessment of welding quality.

焊缝的射线检测对于保证焊接制造质量起着至关重要的作用，因为x射线成像技术可以清晰地显示焊缝区域的内部结构。然而，现有的主流检测方法依赖于人工检测或监督检测，这两种方法都容易受到主观因素和模型泛化能力的限制。为此，本文提出了一种基于重构的两阶段无监督检测框架，以实现对焊接质量的快速准确检测。首先，设计了一种基于实际焊接缺陷特征的模拟缺陷生成算法。构建了包含多种缺陷类型的数据集，并通过数据增强算法进一步优化了图像质量。其次，提出了一种基于残差学习的高质量扩散模型（H-DiffuM），该模型通过残差引导的噪声调度机制实现焊缝缺陷图像的精确重建。最后，将门控机制与x射线图像的频域特征相结合，设计了多尺度频域注意力融合模块（MFDAFM），并将其嵌入到判别网络（Seg-net）中，从而提高了检测精度。最终的实验结果表明，该方法在像素级AUROC上达到97.80%，在AP上达到93.34%，超过了目前最先进的无监督检测方法。同时，本文所描述的检测方法具有检测速度快、精度高的优点，在焊接质量的快速评估中具有应用潜力。

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引用次数: 0

Femtosecond laser bonding of transparent polycarbonate: a study on the weld seam quality and strength 透明聚碳酸酯飞秒激光粘接：焊缝质量和强度的研究

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-01-27 DOI: 10.1016/j.jmapro.2026.01.070

Soheil Alee Mazreshadi , Pol Vanwersch , Tim Evens , Sylvie Castagne

Reliable bonding of transparent polymers is essential for microfluidic and biomedical devices. Ultrashort laser welding provides localized energy deposition with minimal heat-affected zones, but nonlinear absorption introduces challenges in achieving consistent weld quality. This study investigates femtosecond laser bonding of transparent polycarbonate using a 1030

nm

source, focusing on the effects of fluence per pulse, scanning speed, and repetition rate. Two degradation mechanisms are identified: excessive cumulative fluence and high per-pulse fluence, both defining operational limits. At optimized fluence values, pre-focal absorption before the focus causes a focal offset between the intended and actual weld location. Even after refocusing on the interface, the gap between plates and the distorted beam profile produce a non-uniform fluence distribution, compromising weld quality. Simulations confirm that such offsets distort beam shape and fluence delivery. To overcome this, a low-fluence with high-overscan strategy is proposed, mitigating offset effects. Uniform welds are achieved at a repetition rate of 1

MHz

with scanning speeds ranging from 10 to 30

mm / s

and fluences of 0.08–0.17

J / {cm}^{²}

, with more than five overscans. FTIR measurements are performed to monitor potential chemical changes in the bonded regions. The optimized process has a maximum shear strength of 30.2 ± 5

N / {mm}^{²}

, corresponding to 48% of pristine polycarbonate. Weibull analysis identifies a highest modulus of 5.2, confirming process reliability. Devices withstand leakage tests up to 1

bar

. This approach establishes a reproducible femtosecond welding strategy for transparent polymers, resulting in stronger, and more reliable fabrication of microfluidic and biomedical systems.

透明聚合物的可靠结合对于微流体和生物医学设备至关重要。超短激光焊接提供了局部能量沉积和最小的热影响区，但非线性吸收给实现一致的焊接质量带来了挑战。本研究使用1030nm光源研究透明聚碳酸酯的飞秒激光键合，重点研究了每脉冲影响、扫描速度和重复率的影响。确定了两种降解机制：过量的累积通量和高的每脉冲通量，两者都确定了操作极限。在优化的通量值下，焦点前的焦前吸收会导致预期和实际焊接位置之间的焦偏移。即使重新聚焦在界面上，板材之间的间隙和扭曲的光束轮廓也会产生不均匀的流量分布，从而影响焊接质量。模拟证实了这种偏移会扭曲光束的形状和能量的传递。为了克服这一问题，提出了一种低通量高过频策略，以减轻抵消效应。在1 MHz的重复频率下实现均匀焊接，扫描速度范围为10至30 mm/s，影响范围为0.08-0.17 J/cm²，超过5次扫描。FTIR测量用于监测键合区的潜在化学变化。优化工艺的最大抗剪强度为30.2±5 N/mm²，相当于原始聚碳酸酯的48%。威布尔分析确定了最高模量5.2，确认了过程的可靠性。设备可承受高达1bar的泄漏测试。这种方法为透明聚合物建立了一种可重复的飞秒焊接策略，从而产生更强、更可靠的微流体和生物医学系统制造。

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引用次数: 0

Integrating mechanism and data-driven approaches in pre-aged hardening warm forming: Performance prediction and process parameters deduction 预时效硬化热成形的综合机理与数据驱动方法：性能预测与工艺参数推导

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-01-27 DOI: 10.1016/j.jmapro.2026.01.075

Huijuan Ma , Xiaoying Wei , Peiliao Wang , Zhiang Gong , Zhili Hu , Lin Hua

Pre-aged hardening warm forming (PHF) technology enables precise control of process parameters, allowing pre-hardened sheet to achieve superior formability compared to the O-temper condition. During subsequent forming stages, this process utilizes synergistic control of deformation and phase transformation, enabling the final component to attain mechanical properties comparable to the T6 temper. By employing pre-hardened technology in conjunction with warm forming process, the post-forming solution heat treatment and aging steps can be eliminated, thereby significantly reducing the component manufacturing cycle. However, as an emerging technique, past experience has limited guidance on excavating the mechanism and deducting the parameters of PHF process. Here, the Long Short-Term Memory network (LSTM) model of 7075 aluminum alloy (AA7075) is firstly established, which innovatively facilitates bidirectional prediction between process parameters and mechanical properties. Crucially, a constitutive model of PHF process based on dynamic precipitation and dislocation strengthening is proposed, considering the direct phase precipitation from the solid solution and the inherited precipitation from GPII zones to η' phases based on microstructure characterization utilizing the HRTEM, DSC, SAXS and the XRD. Moreover, the accuracy of the LSTM model is further improved through a novel pre-training approach that assimilates knowledge from the AA7075 constitutive model, followed by fine-tuning with experimental dataset. Embracing a “mechanism + data” fusion-driven approach, the mechanical properties prediction and the process parameters deduction of high-strength aluminum alloy components formed under the PHF process are achieved. Additionally, rapid and accurate deduction of process parameters for 7050 aluminum alloy (AA7050) with similar phase evolution is realized by transfer learning from the AA7075 LSTM model using little experimental data. This study not only accelerates the development of higher-performance aluminum alloy components, but also establishes a foundational framework for swiftly determining the process window under the cooperative control of deformation and phase transformation.

预时效硬化热成形（PHF）技术可以精确控制工艺参数，使预硬化板材与o回火条件相比具有更好的成形性。在随后的成形阶段，该工艺利用变形和相变的协同控制，使最终部件获得与T6回火相当的机械性能。通过采用预硬化技术与热成形工艺相结合，可以消除成型后的固溶热处理和时效步骤，从而大大缩短了部件的制造周期。然而，作为一种新兴技术，以往的经验对于挖掘PHF工艺的机理和推导工艺参数的指导作用有限。本文首次建立了7075铝合金（AA7075）的长短期记忆网络（LSTM）模型，创新地实现了工艺参数与力学性能之间的双向预测。基于HRTEM、DSC、SAXS和XRD的微观结构表征，考虑了固溶的直接相析出和GPII区向η′相的继承析出，提出了基于动态析出和位错强化的PHF过程本构模型。此外，通过一种新的预训练方法，吸收AA7075本构模型的知识，然后与实验数据集进行微调，进一步提高了LSTM模型的准确性。采用“机制+数据”的融合驱动方法，实现了PHF成形高强铝合金构件的力学性能预测和工艺参数推导。此外，利用少量实验数据，通过对AA7075 LSTM模型的迁移学习，实现了相演化相似的7050铝合金（AA7050）工艺参数的快速准确推导。该研究不仅加速了高性能铝合金部件的开发，而且为快速确定变形与相变协同控制下的工艺窗口建立了基础框架。

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引用次数: 0

An error-controlled G3-continuous oriented toolpath optimization algorithm and modified speed planning for five-axis machining 误差控制的g3连续定向五轴加工刀路优化算法及修正速度规划

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-01-27 DOI: 10.1016/j.jmapro.2026.01.066

Tao Wu , Yong Zhang , Yongfei Wang , Bin Hu , Chen Li

Converting micro-segment toolpaths into high-order curves can significantly enhance the stability of five-axis CNC machining processes. However, conventional toolpath optimization approaches tend to simultaneously cause both undercutting and overcutting on the workpiece surfaces. Overcutting leads to irreversible morphological damage to the workpiece, thereby resulting in scrapped parts. Furthermore, the asynchronous variation between speed limit trends and speed planning curves undermines the effectiveness of conventional speed planning strategies in the machining of complex structural workpieces. To achieve effective control over machining stability and accuracy in five-axis CNC machining of complex workpieces, this work proposed an error-controllable G3-continuous oriented toolpath optimization algorithm. Based on the G3-continuous quartic symmetric Bezier curve, the toolpath was directionally offset according to the viewing-angle theorem. To ensure toolpath reachability, the Sobolev seminorm method and CVE method were subsequently employed to further optimize toolpath stability. Additionally, an enhanced speed planning strategy with an extra verification mechanism was designed. By incorporating adaptive quintic Gauss-Legendre quadrature and S-shaped speed model, a numerical model was established to characterize the relationships among curvature radius, arc length, and motion time. The activation conditions for the verification mechanism were derived using quartic non-uniform difference formulas. The secant method was applied to dynamically adjust local snap parameters of current toolpath segments for speed profile modulation. Five-axis machining experiments on dentures were conducted to validate the effectiveness of optimization algorithms. Experimental results demonstrated that, compared with traditional strategies, the modified toolpath optimization and speed look-ahead algorithms reduced machine tool vibration by 6.62% and 19.46%, respectively, while increasing dimensional compliance rates by 283.79% and 439.774%, respectively. This work successfully mitigates the challenges of overcutting, machine chatter, and accuracy drift in the five-axis CNC machining of complex structural components, thereby offering theoretical support for the development of high-precision and stable machining technologies for such components.

将微段刀具轨迹转换成高阶曲线，可以显著提高五轴数控加工过程的稳定性。然而，传统的刀具轨迹优化方法往往会同时引起工件表面的过切和下切。过切会对工件造成不可逆的形态损伤，从而导致零件报废。此外，速度限制趋势与速度规划曲线之间的异步变化破坏了传统速度规划策略在复杂结构工件加工中的有效性。为了有效控制复杂工件五轴数控加工的加工稳定性和加工精度，本文提出了一种误差可控的g3连续导向刀路优化算法。基于g3 -连续四次对称Bezier曲线，根据视角定理对刀具轨迹进行方向偏移。为了保证刀具路径的可达性，随后采用Sobolev半正规方法和CVE方法进一步优化刀具路径的稳定性。此外，设计了一种具有额外验证机制的增强速度规划策略。结合自适应五次Gauss-Legendre正交和s形速度模型，建立了曲率半径、弧长和运动时间之间关系的数值模型。利用四次非均匀差分公式推导了验证机构的激活条件。采用割线法动态调整当前刀路段的局部卡扣参数，实现速度剖面调制。通过义齿五轴加工实验验证了优化算法的有效性。实验结果表明，与传统策略相比，改进的刀具路径优化和速度预测算法分别使机床振动降低了6.62%和19.46%，尺寸顺应率分别提高了283.79%和439.774%。本工作成功地解决了复杂结构件五轴数控加工中存在的过切削、机床颤振和精度漂移等问题，为该类零件高精度稳定加工技术的发展提供了理论支持。

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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 : 2026-01-27 DOI: 10.1016/j.jmapro.2025.12.069

Seyed Mehrab Hosseini , Ehsan Vaghefi , Elham Mirkoohi

引用次数: 0

Filament-assisted combined pulse laser ablation of metal targets: Mechanistic insights, efficiency enhancement, and spatial tolerance 细丝辅助联合脉冲激光烧蚀金属目标：机理见解，效率提高，和空间公差

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-01-26 DOI: 10.1016/j.jmapro.2026.01.050

Zhou Li , Junyang Xu , Yuyang Chen , Kai Li , Lu Zhang , Xianshi Jia , Cong Wang , Ji'an Duan

Filament-assisted combined pulse laser (CPL) ablation, coupling a millisecond laser with a nanosecond-laser-induced air filament, is demonstrated to achieve high-efficiency metal ablation under near-on-target power densities of 10³-10⁴ W/cm². An integrated diagnostic system, combining transient temperature measurement and time-resolved imaging, enables direct identification of the coupled processes of melting and filament-driven expulsion, with the assistance of post-process three-dimensional morphology analysis. The filament assistance yields nearly an order-of-magnitude enhancement in ablation volume and material removal rate compared with millisecond laser irradiation alone. Crater depth is increased by more than a factor of two, while smaller and more stable laser-supported combustion waves are maintained. Moreover, CPL exhibits pronounced spatial tolerance, sustaining significant efficiency gains even under deliberate lateral misalignment and thereby confirming its robustness for non-ideal and long-range conditions. These findings highlight both mechanistic insight and performance advancement, consolidating filament-assisted CPL as a robust and scalable strategy for efficient, stable, and spatially tolerant ablation in high-energy laser damage.

在接近目标的功率密度为103-104 W/cm2的情况下，将毫秒激光与纳秒激光诱导的空气灯丝耦合在一起的灯丝辅助组合脉冲激光（CPL）烧蚀可以实现高效率的金属烧蚀。一个集成的诊断系统，结合瞬态温度测量和时间分辨成像，可以直接识别熔融和细丝驱动的排出耦合过程，并辅以加工后的三维形态分析。与单独的毫秒激光照射相比，灯丝辅助在烧蚀体积和材料去除率方面几乎提高了一个数量级。弹坑深度增加了两倍以上，同时保持了更小、更稳定的激光支持燃烧波。此外，CPL表现出明显的空间容忍度，即使在故意的横向不对准下也能保持显著的效率提高，从而证实了其在非理想和远程条件下的稳健性。这些发现强调了机理的洞察和性能的进步，巩固了细丝辅助CPL作为高效、稳定和空间容忍度高的高能激光损伤烧蚀的稳健和可扩展策略。

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