Journal of Manufacturing Processes最新文献_第3页

An integrated learning, monitoring, and control system for ultrasonic metal welding 超声金属焊接的综合学习、监测和控制系统

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-05 DOI: 10.1016/j.jmapro.2026.01.077

Kuan-Chieh Lu , Li-Wei Shih , Chenhui Shao

Ultrasonic metal welding (UMW) is a solid-state joining technology with widespread industrial applications. However, the weld quality in UMW is highly sensitive to process disturbances such as tool degradation and surface contamination. To address this challenge, this paper presents an integrated learning, monitoring, and control (LMC) system to improve process robustness and weld quality in UMW. The proposed system integrates in-situ sensing, online process monitoring, and within-cycle process adjustment to automatically compensate for process disturbances. Extensive experiments involving 700 welds with varied acting time, pressure adjustments, and contamination levels, are carried out to thoroughly evaluate the effectiveness of the LMC system. It is shown that the proposed method significantly and consistently outperforms the existing controller. Specifically, the weld success rate is increased from 0% to 92% under 20% surface contamination, and from 6% to 72% under 10% surface contamination. Furthermore, a response surface model is developed to quantify the causal relationships between control inputs (i.e., acting time and pressure increase amount) and the resulting weld success rate, which enables efficient optimization of control parameters. Overall, the proposed LMC approach improves the UMW process robustness and weld quality, demonstrating strong potential for industrial-scale implementation. To the best of our knowledge, this study represents one of the first integrated LMC systems developed for UMW.

超声波金属焊接（UMW）是一种具有广泛工业应用的固态焊接技术。然而，在UMW焊接质量是高度敏感的过程干扰，如工具退化和表面污染。为了解决这一挑战，本文提出了一种集成学习、监测和控制（LMC）系统，以提高UMW的工艺稳健性和焊接质量。该系统集成了原位传感、在线过程监测和周期内过程调节，以自动补偿过程干扰。为了彻底评估LMC系统的有效性，对700个不同作用时间、压力调节和污染水平的焊缝进行了广泛的实验。结果表明，该方法明显优于现有的控制器。具体来说，当表面污染为20%时，焊接成功率从0%提高到92%，当表面污染为10%时，焊接成功率从6%提高到72%。此外，建立了响应面模型来量化控制输入（即作用时间和压力增加量）与焊接成功率之间的因果关系，从而实现控制参数的有效优化。总体而言，所提出的LMC方法提高了UMW工艺的稳健性和焊接质量，显示出工业规模实施的强大潜力。据我们所知，这项研究是为UMW开发的第一个集成LMC系统之一。

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引用次数: 0

High-temperature wear behaviour of laser-textured assisted cold-sprayed metal matrix composite coatings: tamping effect of particles in a confined space 激光织构辅助冷喷涂金属基复合涂层的高温磨损行为：密闭空间中颗粒的夯实效应

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-05 DOI: 10.1016/j.jmapro.2026.01.097

Zhiyuan Wang , Jianing Wang , Bowen Yao , Huan Chen , Fengyuan Bao , Yang Liu , Xueze Jin , Oleg Bashkov , Lin Cao

To address the surface protection challenges faced by turbine blades in gas turbines under extreme operating conditions—including high temperatures, high pressures, and high-velocity gas combustion gases—this study employs an efficient composite process combining laser surface texturing with cold spraying (LST-CS). This process is designed to enhance the interfacial bonding of cold-sprayed nickel-based coatings, thereby improving their operational reliability and extending component lifespan. Following substrate pretreatment, coating porosity decreased by 2.28%, bond strength increased by 3.68-fold, and wear rates at ambient and elevated temperatures decreased by 52.67% and 68.71% respectively. Experimental and characterisation analyses revealed that the textured groove structures not only increased particle-substrate contact area but also functioned as a guiding framework directing particle deposition. This induced a ceramic tamping effect within confined spaces, establishing a mortise-and-tenon mechanical interlocking structure at the coating-substrate interface. This structure not only enhances the bonding strength between the coating and the substrate, promoting overall densification of the coating, but also effectively suppresses crack initiation at elevated temperatures through dovetail mechanical anchoring. By leveraging substrate softening, it transforms frictional loads into lateral compression that reinforces interfacial bonding, thereby significantly mitigating high-temperature shear slippage and interfacial delamination. This extends the service life of the coating and provides a theoretical foundation and technical guidance for improving the surface repair performance of high-end equipment under extreme operating conditions.

为了解决燃气轮机涡轮叶片在极端工况下（包括高温、高压和高速燃气燃烧气体）面临的表面保护挑战，本研究采用了激光表面纹理与冷喷涂（LST-CS）相结合的高效复合工艺。该工艺旨在增强冷喷涂镍基涂层的界面结合，从而提高其运行可靠性并延长组件寿命。基材预处理后，涂层孔隙率降低了2.28%，结合强度提高了3.68倍，室温和高温下的磨损率分别降低了52.67%和68.71%。实验和表征分析表明，织构槽结构不仅增加了颗粒与衬底的接触面积，而且还起到了指导颗粒沉积的导向框架作用。这在有限的空间内诱发了陶瓷夯实效应，在涂层-衬底界面处建立了榫卯机械联锁结构。这种结构不仅提高了涂层与基体的结合强度，促进涂层的整体致密化，而且通过燕尾机械锚定有效抑制高温下裂纹的产生。通过利用基材软化，它将摩擦载荷转化为侧向压缩，从而加强界面结合，从而显著减轻高温剪切滑移和界面分层。这延长了涂层的使用寿命，为提高高端设备在极端工况下的表面修复性能提供了理论基础和技术指导。

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引用次数: 0

Enhancing gear surface integrity: A study of longitudinal-torsional ultrasonic strengthening effect on surface roughness 提高齿轮表面完整性：纵向-扭转超声强化对表面粗糙度的影响研究

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-05 DOI: 10.1016/j.jmapro.2026.01.098

Yan Jiang, Qinyuan Yang, Qiang Guo, Bo Zhao, Tian Li, Ying Niu

Tooth surface properties are critical for gear longevity, efficiency, and accuracy. Traditional strengthening methods often fall short in high-precision, long-life applications. Fortunately, with its precise energy control, ultrasonic machining offers a novel approach to overcoming these drawbacks and achieving superior surfaces quality. However, there are few studies on the effect elucidation of longitudinal-torsional ultrasonic machining for gears. Thus, this paper introduces a new gear surface strengthening method by integrating longitudinal-torsional ultrasonic machining with gear meshing theory, focusing on surface formation mechanisms and roughness evolution. The underlying micro-forming mechanism, based on Boussinesq-Flamant theory, models the process in three stages: 1) Triangular indentation, where initial grinding peaks yield and subside under combined static and ultrasonic loading; 2) Crushing collapse, involving material work hardening, fracture, and flow into valleys under cyclic load, dissolving the original topography; 3) New surface formation, where stabilized plastic flow fills valleys to create a smooth, dense surface with beneficial residual compressive stress. This model systematically describes the evolution from the initial to the hardened surface. For validation, a dedicated experimental platform was used alongside white-light interferometry. Results show high consistency between experimental and theoretical surface morphology trends along the tooth profile, with an average quantitative error of 15%. These findings validate the three-stage model, providing a theoretical foundation for understanding and optimizing ultrasonic gear strengthening processes.

齿面特性对齿轮寿命、效率和精度至关重要。传统的强化方法在高精度、长寿命的应用中往往存在不足。幸运的是，凭借其精确的能量控制，超声波加工提供了一种克服这些缺点并获得卓越表面质量的新方法。然而，对齿轮纵扭超声加工的效果分析研究较少。因此，本文介绍了一种将纵扭超声加工与齿轮啮合理论相结合的齿轮表面强化新方法，重点研究了表面形成机理和粗糙度演变。基于Boussinesq-Flamant理论，将微成形机理分为三个阶段：1)三角形压痕，在静态和超声复合载荷下，初始磨峰屈服并沉降；2)破碎崩塌，材料在循环荷载作用下加工硬化、断裂、流向山谷，使原始地形溶解；3)新的表面形成，其中稳定的塑性流填充山谷，创造一个光滑，密集的表面与有益的残余压应力。该模型系统地描述了从初始表面到硬化表面的演变过程。为了验证，在白光干涉测量的同时使用了一个专用的实验平台。结果表明，沿齿廓方向的表面形貌趋势与实验结果高度一致，平均定量误差为15%。这些结果验证了三阶段模型，为理解和优化超声齿轮强化工艺提供了理论基础。

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引用次数: 0

A multiscale simulation framework for composite manufacturing process: Data transfer and experimental verification 复合材料制造过程的多尺度模拟框架：数据传递与实验验证

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-05 DOI: 10.1016/j.jmapro.2026.01.063

Mingliang Men , Bao Meng , Jinquan Han , Min Wan

The precise forming of complex thin-walled metallic components can be achieved through composite manufacturing process, where the macroscopic mechanical response and microstructural evolution exhibit significant coupling effects. A general multiscale sequential simulation framework was developed by coupling crystal plasticity finite element (CPFE) and cellular automaton (CA) models. A bidirectional grid mapping and data transfer method was established to address grid incompatibility and physical quantity mapping between different models. During the transfer from the CPFE model to the CA model, the proposed grid refinement mapping approach achieves lossless data transmission compared with the nearest-neighbor mapping method. In the reverse transfer from CA to CPFE, the average data transmission error is also nearly negligible when the coarsened element size approaches the CA cell size. The proposed multiscale simulation framework is applicable to both 2D and 3D conditions. For simulations of a two-stage uniaxial tension with intermediate annealing, the average prediction error of the 2D and 3D models is about 5%. Although the 3D model exhibits slightly improved prediction accuracy, the computational cost is approximately six times that of the 2D model. It indicates that the 2D model provides a reasonable balance between computational efficiency and predictive accuracy. Furthermore, the multiscale framework was applied to simulate the post-heat treatment process of additively manufactured alloy. The prediction errors for the recrystallized volume fraction and average grain size are both below 10%, and the stress-strain curves during subsequent uniaxial tension is predicted with an accuracy of approximately 95%. The results from the two application cases demonstrate that the proposed coupled model can accurately capture the mechanical response during deformation as well as the static recrystallization behavior during annealing, confirming the generality and reliability of the multiscale simulation framework.

复合材料制造工艺可以实现复杂薄壁金属零件的精密成形，其宏观力学响应和微观组织演化表现出显著的耦合效应。通过晶体塑性有限元（CPFE）和元胞自动机（CA）模型的耦合，建立了一个通用的多尺度序列模拟框架。针对不同模型之间的网格不兼容和物理量映射问题，建立了一种双向网格映射和数据传输方法。在CPFE模型到CA模型的转换过程中，与最近邻映射方法相比，本文提出的网格细化映射方法实现了数据的无损传输。在从CA到CPFE的反向传输中，当粗化单元尺寸接近CA单元尺寸时，平均数据传输误差也几乎可以忽略不计。所提出的多尺度模拟框架适用于二维和三维条件。对于中间退火的两阶段单轴拉伸模拟，二维和三维模型的平均预测误差约为5%。虽然3D模型的预测精度略有提高，但计算成本大约是2D模型的6倍。这表明二维模型在计算效率和预测精度之间取得了合理的平衡。此外，采用多尺度框架对增材制造合金的后热处理过程进行了模拟。再结晶体积分数和平均晶粒尺寸的预测误差均在10%以下，后续单轴拉伸过程的应力-应变曲线预测精度约为95%。两个应用实例的结果表明，所提出的耦合模型能够准确地捕捉到变形过程中的力学响应和退火过程中的静态再结晶行为，验证了多尺度模拟框架的通用性和可靠性。

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引用次数: 0

A novel fluid-driven vibration finishing method for internal surfaces of additively manufactured channels 一种新型增材制造通道内表面流体驱动振动精加工方法

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-04 DOI: 10.1016/j.jmapro.2026.01.100

Qikai Li , Mohammad Rakibul Hasan , Ankang Yuan , Ruoying Wang , Chao Ni , Pu Qin , Xu Zhu , Mingyang Lu , Qing Luo , Danlei Zhao , Guangyi Ma

To address the critical challenge of poor surface quality in complex internal channels fabricated by additive manufacturing (AM) technologies, this paper proposes a novel fluid-driven vibration finishing (FDVF) method for the internal surface of channels. When the fluid encounters an abrupt reduction in flow area, the static pressure decreases dramatically due to the increase in velocity. This pressure variation is utilized to drive periodic vibrations of a blocking ball within the channel. By leveraging the ball's impact on the internal surface and controlling the position of the ball, uniform finishing of the AM-fabricated channel can be achieved. The mechanisms of ball vibration and surface finishing processes were investigated through the integration of computational fluid dynamics simulations and high-speed imaging. Results showed >4 kHz high-frequency vibrations of the ball were generated coupled with periodic hydrodynamic cavitation, effectively finishing the wall with depths of material deformation surpassing 25 μm. A 3.6 mm inner-diameter SLM stainless steel tube with an initial average roughness Sa of 6.798 μm was finished under 3.5 MPa. After 60 min, the internal roughness Sa of a 90 mm tube was reduced by 98%, with axial roughness Sa variation within ±0.041 μm. Additionally, the controllability and feasibility of this method for complex channels was validated by finishing a U-shaped tube with an identical inner diameter, achieving a uniform roughness Sa below 0.1 μm on the straight part.

针对增材制造（AM）技术制造复杂内通道表面质量差的关键问题，提出了一种新型的通道内表面流体驱动振动精加工（FDVF）方法。当流体流过面积突然减小时，由于速度的增加，静压急剧下降。这种压力变化被用来驱动通道内封堵球的周期性振动。通过利用球对内表面的冲击和控制球的位置，可以实现am制造通道的均匀精加工。通过计算流体动力学模拟和高速成像相结合的方法，研究了球振动和表面处理过程的机理。结果表明：该球产生了>；4 kHz的高频振动，并伴有周期性的流体动力空化，有效地完成了材料变形深度超过25 μm的管壁；在3.5 MPa的压力下，制备了一根内径为3.6 mm，初始平均粗糙度Sa为6.798 μm的SLM不锈钢管。60 min后，90 mm管的内部粗糙度Sa降低了98%，轴向粗糙度Sa变化幅度在±0.041 μm以内。此外，通过加工相同内径的u型管，验证了该方法在复杂通道上的可控性和可行性，直线部分粗糙度Sa均匀小于0.1 μm。

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引用次数: 0

Targeted disentangling of melt pool features for layer-wise printing quality assessment in L-PBF 基于熔池特征的分层印刷质量评估

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-04 DOI: 10.1016/j.jmapro.2026.01.074

Hao Jiang , Zhibin Zhao , Xingwu Zhang , Chenxi Wang , Huihui Miao , Xuefeng Chen

Ensuring the stability and consistency of laser-powder bed fusion (L-PBF) additive manufacturing remains a persistent challenge in the industry, which has led to growing research interest in process monitoring in recent years. Among various monitoring techniques, coaxial melt pool imaging stands out as one of the most promising approaches. However, achieving stable and scalable print quality assessment based on coaxial melt pool images remains challenging. Deep learning methods often suffer from limited interpretability and weak generalization, while traditional image processing approaches tend to lack flexibility and exhibit low discriminative accuracy. To address these issues, this paper proposes an interpretable directional feature disentanglement framework designed to enable the extraction of strongly-correlated physical features with structures from melt pool images for printing quality assessment. Specifically, a feature anchoring module is incorporated into a variational autoencoder (VAE) generation framework to stabilize the position of the disentangled target features in the latent space. A multi-stage, multi-task training strategy is then introduced to progressively accomplish melt pool image reconstruction, feature anchoring, and feature disentanglement. Finally, the effectiveness of the proposed framework is verified by cross-device and cross-material experiments involving unsupported overhang structures, which proves that it is a technology worthy of engineering promotion.

确保激光粉末床融合（L-PBF）增材制造的稳定性和一致性仍然是行业面临的一个持续挑战，这导致近年来对过程监控的研究兴趣日益浓厚。在各种监测技术中，同轴熔池成像是最有前途的方法之一。然而，基于同轴熔池图像实现稳定和可扩展的打印质量评估仍然具有挑战性。深度学习方法往往具有有限的可解释性和弱泛化，而传统的图像处理方法往往缺乏灵活性和低判别精度。为了解决这些问题，本文提出了一个可解释的定向特征解缠框架，旨在从熔池图像中提取与结构强相关的物理特征，用于打印质量评估。具体而言，在变分自编码器（VAE）生成框架中加入特征锚定模块，以稳定解纠缠目标特征在潜在空间中的位置。然后引入一种多阶段、多任务的训练策略，逐步完成熔池图像重建、特征锚定和特征解纠缠。最后，通过无支撑悬挑结构的跨装置、跨材料试验验证了该框架的有效性，证明了该框架是一种值得工程推广的技术。

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引用次数: 0

A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks 基于物理信息神经网络的激光焊接过程自监督学习熔透预测模型

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-03 DOI: 10.1016/j.jmapro.2026.01.035

Sen Li , Xiaoying Liu , Xiaojian Xu , Chendong Shao , Yaqi Wang , Ling Lan , Xinhua Tang , Haichao Cui

The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. Accurate prediction of the penetration state is therefore essential for ensuring weld quality. To this end, this paper introduces SimPhysNet, a novel algorithm that achieves high classification accuracy in laser welding penetration prediction using only a limited number of labeled images. This approach effectively overcomes the limitations of supervised learning classification algorithms, which are hindered in industrial applications by their dependence on extensive, high-quality labeled data. The core of SimPhysNet is a unique self-supervised learning paradigm that embeds physical priors into a contrastive learning framework. By incorporating a physics-informed neural network (PINN), the model is guided to extract physically meaningful features of the molten pool and keyhole from a large set of unlabelled data, while three image augmentation tasks further enhance its generalization capabilities. Subsequently, a few-shot learning strategy, based on prototypical networks, enables robust classification by constructing class representations from a minimal set of labeled images. Experimental results demonstrate that SimPhysNet achieves a classification accuracy of 96.06% using only 200 labeled images (approximately 5% of the total labeled dataset), which is comparable to the performance of conventional supervised learning algorithms that utilize the entire labeled dataset. This work presents a new, efficient, and highly accurate method, providing the way for the intelligent automation of laser welding.

激光焊接的全熔透是实现焊接接头无缺陷的基本因素之一，具有十分重要的意义。因此，准确预测熔透状态对于确保焊接质量至关重要。为此，本文介绍了一种新的算法SimPhysNet，该算法仅使用有限数量的标记图像就可以实现激光焊接熔透预测的高分类精度。这种方法有效地克服了监督学习分类算法的局限性，这些算法依赖于大量高质量的标记数据，阻碍了它们在工业应用中的应用。SimPhysNet的核心是一个独特的自监督学习范例，它将物理先验嵌入到对比学习框架中。通过结合物理信息神经网络（PINN），该模型被引导从大量未标记数据中提取熔池和锁孔的物理有意义的特征，同时三个图像增强任务进一步增强其泛化能力。随后，基于原型网络的几次学习策略通过从最小的标记图像集构建类表示来实现鲁棒分类。实验结果表明，SimPhysNet仅使用200张标记图像（约占总标记数据集的5%）就实现了96.06%的分类准确率，这与使用整个标记数据集的传统监督学习算法的性能相当。提出了一种新的、高效的、高精度的方法，为激光焊接的智能化自动化提供了途径。

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引用次数: 0

An optimization method of compound cutting parameters for hole machining in consideration of low-carbon and surface roughness 一种考虑低碳和表面粗糙度的孔加工复合切削参数优化方法

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-03 DOI: 10.1016/j.jmapro.2026.01.083

Junbo Tuo , Yongkai Zhao , Bo Liang , Yongliang Li

In the actual production process of machine tools, hole machining is the most common machining method, which produces different amounts of carbon emissions depending on the processing methods. Among them, optimizing the cutting parameters of hole machining is a common and effective method to diminish carbon emissions. However, concentrating on the research of optimizing carbon emission parameters related to hole machining, which has displayed that most of them are focused on a single option in diverse processes, for instance drilling, milling, turning, boring, etc., with limited research on compound processes. In order to supplement this deficiency, this article proposes a new optimization process of cutting parameters for hole compound machining based on the “drilling-boring” craft route. By using different drill bits and the same boring tool, the boring allowance can be changed by changing the diameter of the drill bit. This method links two processes with boring allowance as a medium, not only considering carbon emissions, surface quality, and machining efficiency, but also seeking to achieve a balance among the three in the actual production department. Originally, we have summarized the functional relationship between the compound process parameters involved in from drilling to boring process and the objective functions, then subsequently established corresponding multi-objective optimization models. Whereupon, an improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) was propounded to solve the problem. Taking actual machining as an example, we conducted experiments on hole machining on aluminum alloys. Finally, the optimization results were screened for the optimal solution by combining subjective and objective weighting methods with TOPSIS method. The optimized results represent carbon emissions, surface roughness, and processing time have been reduced by 2.4%, 15.1%, and 2.8%, respectively, which demonstrated the effectiveness and correctness of the method researched in this article.

在机床的实际生产过程中，孔加工是最常见的加工方法，根据加工方法的不同，产生不同的碳排放量。其中，优化孔加工的切削参数是减少碳排放的一种常用而有效的方法。然而，集中于孔加工相关碳排放参数优化的研究表明，它们大多集中在钻、铣、车、镗等多种工艺中的单一选择，对复合工艺的研究较少。为了弥补这一不足，本文提出了一种基于“钻-镗”工艺路线的孔复合加工切削参数优化新工艺。使用不同的钻头和相同的镗具，可以通过改变钻头的直径来改变镗削余量。该方法以镗削余量为媒介，将两道工序联系起来，既考虑碳排放、表面质量、加工效率，又在实际生产部门中寻求三者之间的平衡。首先总结了从钻到镗过程所涉及的复合工艺参数与目标函数之间的函数关系，建立了相应的多目标优化模型。为此，提出了一种改进的强度Pareto进化算法2 （ISPEA2）来解决该问题。以实际加工为例，对铝合金进行了孔加工实验。最后，结合主客观加权法和TOPSIS法对优化结果进行筛选，得到最优解。优化后的碳排放量、表面粗糙度和加工时间分别降低了2.4%、15.1%和2.8%，证明了本文研究方法的有效性和正确性。

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引用次数: 0

Intelligent laser micromachining parameter optimization via causality-enhanced data-driven modeling 基于因果关系增强数据驱动建模的智能激光微加工参数优化

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-03 DOI: 10.1016/j.jmapro.2026.02.003

Wei-Ming Jiang , Yan-Ning Sun , Li-Lan Liu , Jie-Cai Feng , Zeng-Gui Gao

Laser micromachining is pivotal for producing surface micropore structures that comply with specifications in thermal barrier coating manufacturing. However, the intricate nonlinear interactions among laser parameters result in considerable variability in the final processing quality of the micropores. Data-driven methods have mapped laser parameters to processing quality, but traditional machine learning (ML) models provide limited understanding of the causal mechanisms involved, hindering deeper insights into quality optimization process. To address this limitation, this study proposes a causal-enhanced ML (CEML) framework that incorporates causality to identify the optimal combination of laser parameters for enhanced processing quality. The study is structured into three sequential experimental stages. First, a series of micromachining experiments were conducted to generate a dataset comprising laser parameters and corresponding micropore quality indicators. Second, based on the experimental dataset, causal analysis was conducted and the extracted causal information was integrated into model training. The resulting CEML surrogate model was benchmarked against baseline ML models, demonstrating improved predictive performance across multiple evaluation metrics and stable performance under 5-fold cross-validation. Third, a multi-objective optimization based on particle swarm optimization was employed to derive optimal parameters for improved processing quality. The effectiveness of the optimal parameters was subsequently validated through physical machining experiments.

在热障涂层制造中，激光微加工是制造符合规范的表面微孔结构的关键。然而，激光参数之间复杂的非线性相互作用导致微孔的最终加工质量有相当大的变化。数据驱动的方法已经将激光参数映射到加工质量，但传统的机器学习（ML）模型对所涉及的因果机制的理解有限，阻碍了对质量优化过程的深入了解。为了解决这一限制，本研究提出了一个因果关系增强的ML （CEML）框架，该框架包含因果关系，以确定提高加工质量的激光参数的最佳组合。这项研究分为三个连续的实验阶段。首先，进行一系列微加工实验，生成包含激光参数和相应微孔质量指标的数据集。其次，基于实验数据集进行因果分析，并将提取的因果信息整合到模型训练中。由此产生的CEML代理模型与基线ML模型进行了基准测试，在多个评估指标中显示出改进的预测性能，并且在5次交叉验证下表现稳定。第三，采用基于粒子群算法的多目标优化方法，求出提高加工质量的最优参数。通过物理加工实验验证了优化参数的有效性。

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引用次数: 0

A high-efficiency laser processing array micro-holes method based on acousto-optic deflector and galvanometer hybrid 基于声光偏转器和振镜混合的高效激光处理阵列微孔方法

IF 6.8 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Journal of Manufacturing Processes

Pub Date : 2026-02-03 DOI: 10.1016/j.jmapro.2026.01.061

Zihao Feng , Yufeng Liang , Tian Zhang , Youmin Rong , Hongping Yang , Long Chen , Xiufeng Liu , Guojun Zhang , Yu Huang

Array micro-holes have been widely used as optical apertures for ambient light sensors in display panels. Existing galvanometer processing method suffers from inefficient fabrication of the vast number of micro-holes (< 30 μm) due to the scanning speed limitations imposed by the mechanical inertia of mirror oscillation. To address this issue, a laser processing method based on acousto-optic deflector (AOD) and galvanometer hybrid is proposed, centered on a discrete-trajectory partitioning framework tailored for discontinuous, high-density point arrays. The galvanometer trajectory is derived from processing area divided by fully exploiting the AOD scanning field, reducing the path length and number of galvanometer jumps. The target trajectory is determined by pulse distribution from micro-holes, and the AOD displacements are derived through coordinate transformation to the result of positional vector subtraction. The experimental results indicated that compared with galvanometer-only processing, the maximum galvanometer following errors of proposed method are reduced by up to 89.8% with better consistency and higher processing quality. Meanwhile, the total processing time of given drawings decreased from 2.157–3.813 s to 0.487–0.875 s, reduced by up to 77.4%.

阵列微孔被广泛用作显示面板环境光传感器的光学孔径。由于反射镜振荡的机械惯性限制了扫描速度，现有的振镜加工方法在制造大量微孔（< 30 μm）时效率低下。为了解决这一问题，提出了一种基于声光偏转器（AOD）和振镜混合的激光加工方法，该方法以针对不连续高密度点阵列量身定制的离散轨迹划分框架为中心。通过充分利用AOD扫描场，减少了路径长度和振镜跳跃次数，得到了加工区域划分的振镜轨迹。利用微孔脉冲分布确定目标轨迹，通过坐标变换到位置矢量减法的结果得到AOD位移。实验结果表明，与仅用振镜处理相比，该方法最大振镜跟踪误差降低了89.8%，一致性更好，加工质量更高。同时，给定图纸的总处理时间从2.157-3.813 s减少到0.487-0.875 s，减少幅度达77.4%。

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