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Contribution to the analytical determination of uncut chip thickness for cutting force modelling in milling with refinements for high-feed milling 对高进给铣削中切削力建模中未切削切屑厚度分析测定的贡献
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-26 DOI: 10.1016/j.cirpj.2025.09.001
Thomas Jacquet, Jean-Baptiste Guyon, Fabien Viprey, Guillaume Fromentin, David Prat
In modern manufacturing, accurately predicting cutting forces is essential for the design and control of machining operations. Common mechanistic models of cutting forces rely on a precise description of the local uncut chip area. However, in milling, the specific trajectories of cutting edges create challenges in modelling this quantity. Existing analytical models are typically limited to 2D contexts or assume circular tooth trajectories, which are mostly valid for cylindrical end mills. These assumptions limit their applicability to high-feed milling, especially due to low lead angles and complex insert cutter geometries producing non-circular paths. This article presents a new three-dimensional analytical model for evaluating the local uncut chip thickness in high-feed milling. It relies on closed-form expressions derived from geometric analysis and Taylor expansions to approximate the uncut chip area and cutter-workpiece engagement, even in regions where conventional models fail. The model applies to linear-path milling and accounts for tool run-out and differential pitch. Compared to a Newton–Raphson numerical method, it achieves a relative error below 5% while being 3 to 9 times faster, enabling efficient integration in force models. Beyond its computational efficiency, the explicit formulation enables analysis of geometric influence, such as sensitivity to feed per tooth or tooth count-capabilities not easily accessible with purely numerical approaches. This work contributes a rigorous and interpretable alternative for improving cutting force prediction in high-feed milling.
在现代制造业中,准确预测切削力对加工操作的设计和控制至关重要。常见的切削力机理模型依赖于对局部未切削切屑区域的精确描述。然而,在铣削过程中,切削刃的特定轨迹在建模这个数量时带来了挑战。现有的分析模型通常局限于二维环境或假设圆齿轨迹,这主要适用于圆柱立铣刀。这些假设限制了它们在高进给铣削中的适用性,特别是由于低导角和复杂的切削齿几何形状会产生非圆轨迹。提出了一种新的高进给铣削局部未切削切屑厚度的三维分析模型。它依赖于由几何分析和泰勒展开导出的封闭形式表达式来近似未切割的切屑面积和刀具-工件啮合,即使在传统模型失效的区域也是如此。该模型适用于直线铣削,并考虑了刀具跳动和差动节距。与Newton-Raphson数值方法相比,该方法的相对误差低于5%,但速度提高了3到9倍,从而实现了力模型的有效集成。除了计算效率之外,显式公式还可以分析几何影响,例如对每齿进给量的灵敏度或纯数值方法难以获得的齿数能力。这项工作为提高高进给铣削切削力预测提供了一种严格的、可解释的替代方法。
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引用次数: 0
Dynamic decision-making on the number and selection of measurement markers for stochastic control of overlay errors in photolithography 光刻叠加误差随机控制中测量标尺数量与选择的动态决策
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-26 DOI: 10.1016/j.cirpj.2025.09.008
Yangmeng Li , Huidong Zhang , Noah Graff , Roberto Dailey , Dragan Djurdjanovic
Accurate multilayer overlay alignment in photolithography is critical for semiconductor manufacturing. It is crucial to use a limited number of measurement markers to ensure the throughput while maintaining the overlay estimation and control accuracy. This work presents a novel optimization framework for dynamically down-selecting overlay measurement markers. The framework employs a stochastic multilayer control algorithm for tractable real-time control and select an optimal subset of markers that maximize overlay error estimation accuracy. The optimal marker number is determined by maximizing an objective that balances production quality and throughput. Industrial evaluation in a 300 mm fab demonstrates substantial cost-benefit improvements over traditional Run-to-Run control, highlighting enhanced process efficiency and yield.
在光刻技术中,精确的多层叠加排列对半导体制造至关重要。使用有限数量的测量标记来确保吞吐量,同时保持覆盖估计和控制精度是至关重要的。本文提出了一种新的动态下选择叠加测量标记的优化框架。该框架采用随机多层控制算法进行可处理的实时控制,并选择最优标记子集,使覆盖误差估计精度最大化。最优标记数量是通过最大化平衡生产质量和吞吐量的目标来确定的。300 mm晶圆厂的工业评估表明,与传统的运行到运行控制相比,成本效益有了实质性的改善,突出了工艺效率和产量的提高。
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引用次数: 0
Similarity-based anomaly detection method for turning of multi-material workpieces with varying axially constant blank diameter 基于相似性的多材料变轴向恒定毛坯直径车削异常检测方法
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-24 DOI: 10.1016/j.cirpj.2025.09.014
Berend Denkena, Benjamin Bergmann, Henning Buhl, Miriam Handrup
Geometry and hardness fluctuations of formed blanks are challenging for process monitoring of the subsequent machining process because they lead to deviating process forces during roughing. Ordinary anomaly detection methods require the forces to be similar. In this work, a similarity-based anomaly detection method is proposed that utilizes Dynamic Time Warping to achieve robustness against blank fluctuations during roughing. It extracts the average signal shape from training signals, scales it individually for each novel process, and uses confidence limits for anomaly detection. The method is tested with multi-material shafts whose blank diameter is axially constant but varies between workpieces.
由于成形毛坯的几何形状和硬度波动会导致粗加工过程中的加工力偏差,因此对后续加工过程的过程监控具有挑战性。普通的异常检测方法要求力是相似的。在这项工作中,提出了一种基于相似性的异常检测方法,利用动态时间扭曲来实现对粗加工过程中空白波动的鲁棒性。它从训练信号中提取平均信号形状,为每个新过程单独缩放它,并使用置信限进行异常检测。该方法在多材料轴上进行了试验,这些轴的毛坯直径是轴向恒定的,但在工件之间是不同的。
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引用次数: 0
Free abrasive assisted magnetorheological polishing: Device design and processing performance analysis 游离磨料辅助磁流变抛光:装置设计及加工性能分析
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-24 DOI: 10.1016/j.cirpj.2025.09.015
Yakun Yang, Mingming Lu, Jieqiong Lin, Yongsheng Du
The processing stability and properties of magnetorheological polishing device (MPD) play a crucial role in the processing of optical materials. In this study, a novel MPD was developed to improve the processing stability and properties. The device uses free abrasives to assist in magnetorheological polishing, and completes the self-sharpening of the abrasives in flexible pad using a dynamic magnetic field. This paper presents the principles and structures design involved. The mechanical characteristics of main components and magnetic field characteristics of a Halbach array were analyzed. Based on the developed device, the stability is studied. The advantages of free abrasive assisted magnetorheological polishing method were investigated. The results indicate that the structural design of the main components is reasonable. A dynamic magnetic field device can achieve greater changes in magnetic field intensity and gradient with fewer magnets. It exhibits excellent magnetic field properties. The results obtained by marathon experiment under the same parameters are all distributed within 95 % confidence interval. The processing stability of the MPD was verified. The method can effectively improve the processing performance and has certain advantages. Compared with the traditional magnetorheological polishing method, the processing efficiency can be improved by more than 29.68 %.
磁流变抛光装置(MPD)的加工稳定性和性能在光学材料加工中起着至关重要的作用。在本研究中,开发了一种新型MPD,以提高加工稳定性和性能。该装置利用游离磨料辅助磁流变抛光,利用动态磁场完成柔性垫内磨料的自锐化。本文介绍了所涉及的原理和结构设计。分析了哈尔巴赫阵列主要元件的力学特性和磁场特性。基于所研制的装置,对其稳定性进行了研究。研究了游离磨料辅助磁流变抛光方法的优点。结果表明,主要部件的结构设计是合理的。动态磁场装置可以用更少的磁体实现更大的磁场强度和梯度变化。它具有优异的磁场性能。相同参数下的马拉松试验结果均分布在95% %置信区间内。验证了MPD的加工稳定性。该方法能有效提高加工性能,具有一定的优势。与传统的磁流变抛光方法相比,加工效率可提高29.68 %以上。
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引用次数: 0
Fatigue failure mechanism of gradient nanostructured materials produced by turning 车削梯度纳米结构材料疲劳失效机理研究
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-23 DOI: 10.1016/j.cirpj.2025.09.013
Lihua He, Jinhui Zhou, Bokai Lou, Jing Ni, Xiaoping Hu
Most safety-critical components and load-bearing structures continue to be manufactured using hard turning, a process that induces gradient nanostructures (GNS) in the surface layer. To investigate the effect of GNS layer on fatigue properties, crystal plasticity finite element model (CPFEM) and ± 0.8 % strain fatigue test were used in this study. The objectives were to investigate the correlation between turning parameters and surface GNS layer of 316 L stainless steel, and to reveal the fatigue failure mechanism of GNS layer from multiple scales. The results show that the turning parameters significantly influence the thickness of the GNS layer, with turning depth having the greatest impact, followed by cutting speed. CPFEM simulations predict stress distribution within the GNS layer across regions with varying grain sizes. stresses in fine-grained regions are primarily concentrated at grain boundaries, whereas stresses in coarse-grained regions are distributed within the grains. The model predictions of fatigue crack locations closely align with stress concentration distributions. Fatigue testing reveals that cracks in the GNS layer primarily propagate intergranular boundaries, while cracks in the coarse-grained (CG) layer exhibit both intergranular and transgranular extensions. This behavior mirrors the damage patterns predicted by simulation, demonstrating the model's high accuracy.
大多数安全关键部件和承重结构继续使用硬车削制造,这是一种在表层诱导梯度纳米结构(GNS)的工艺。为了研究GNS层对疲劳性能的影响,采用晶体塑性有限元模型(CPFEM)和±0.8 %应变疲劳试验。研究316 L不锈钢车削参数与表面GNS层的关系,从多个尺度揭示GNS层的疲劳破坏机理。结果表明:车削参数对GNS层厚度影响显著,其中车削深度影响最大,其次是切削速度;CPFEM模拟预测了GNS层内不同晶粒尺寸区域的应力分布。细晶区的应力主要集中在晶界处,而粗晶区的应力主要分布在晶粒内。模型预测的疲劳裂纹位置与应力集中分布密切相关。疲劳试验表明,GNS层的裂纹主要沿晶扩展,而粗晶(CG)层的裂纹同时沿晶扩展和穿晶扩展。这种行为反映了模拟预测的损伤模式,证明了模型的高准确性。
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引用次数: 0
Interpretable generative machine learning model based in-situ process monitoring in robotic wire arc based directed energy deposition of aluminum alloys 基于可解释生成机器学习模型的机器人电弧铝合金定向能沉积原位过程监测
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-23 DOI: 10.1016/j.cirpj.2025.08.010
Deepak Kumar, Sunil Jha
WA-DED using CMT is emerging as a high-throughput metal AM strategy, yet it remains susceptible to a variety of thermomechanical instabilities and metallurgical discontinuities. In this study, we present an advanced AE based in-situ monitoring utilizing the generative ML framework to robustly detect and characterize anomalous conditions that compromise part integrity. Specifically, we examine five critical fault scenarios which are overcurrent, high travel speed, insufficient shielding gas flow, combination of overcurrent and low shielding gas flow rate and combination of high travel speed and low shielding gas flow rate elucidate their distinct signatures in the acoustic domain. A rigorous selection of time and frequency domain descriptors is leveraged to train the variational autoencoder, enabling accurate reconstruction of normal process states and efficient outlier detection. Microstructural analyses, encompassing FESEM, Micro-CT, and XRD, validate the detrimental influence of these faults on porosity evolution, grain morphology, and mechanical properties such as UTS. The proposed VAE model demonstrated robust performance across multiple defect types, achieving peak detection accuracies of 87% for overcurrent-induced faults, 85% for high travel speed anomalies, 81% for defects caused by insufficient shielding gas flow, 87% for combined effect of overcurrent and low gas flow rate, and 84% for combined effect of high travel speed and low gas flow rate. Overcurrent anomalies induce coarse columnar grains and high porosity content, while high travel speed amplifies geometric irregularities. Low gas flow conditions foster oxidation induced porosity. The proposed approach achieves high fidelity in detection of these defects, underscoring the synergy between data driven reconstruction errors and material characterization. By integrating unsupervised generative deep learning with domain specific interpretability through feature sensitivity analysis, this acoustic monitoring paradigm provides a scalable and cost effective pathway to detect defects and ensure structural reliability in WA-DED manufactured components. The comprehensive experimental validations and multi-physics correlational insights position this framework as a robust framework for in-situ process monitoring in WA-DED.
使用CMT的WA-DED正在成为一种高通量金属AM策略,但它仍然容易受到各种热机械不稳定性和冶金不连续的影响。在这项研究中,我们提出了一种先进的基于声发射的原位监测,利用生成式ML框架来鲁棒地检测和表征损害部件完整性的异常情况。具体来说,我们研究了过流、高行程速度、保护气体流量不足、过流和低保护气体流量组合以及高行程速度和低保护气体流量组合五种临界故障场景,阐明了它们在声学领域的不同特征。一个严格的时间和频域描述符的选择被用来训练变分自编码器,使正常过程状态的准确重建和有效的离群检测。微观结构分析,包括FESEM, Micro-CT和XRD,验证了这些断层对孔隙演化,晶粒形貌和力学性能(如UTS)的不利影响。所提出的VAE模型在多种缺陷类型中表现出稳健的性能,对过流引起的故障的峰值检测准确率为87%,对高行程速度异常的峰值检测准确率为85%,对保护气体流量不足引起的缺陷的峰值检测准确率为81%,对过流和低气体流量联合作用的峰值检测准确率为87%,对高行程速度和低气体流量联合作用的峰值检测准确率为84%。过流异常导致粗柱状晶粒和高孔隙率,而高流速放大了几何不规则性。低气体流量条件促进氧化引起的孔隙度。所提出的方法在检测这些缺陷方面实现了高保真度,强调了数据驱动的重构误差和材料表征之间的协同作用。通过特征敏感性分析,将无监督生成深度学习与领域特定可解释性相结合,这种声学监测范式提供了一种可扩展且经济有效的途径来检测WA-DED制造组件的缺陷并确保结构可靠性。综合实验验证和多物理场相关见解使该框架成为WA-DED现场过程监测的强大框架。
{"title":"Interpretable generative machine learning model based in-situ process monitoring in robotic wire arc based directed energy deposition of aluminum alloys","authors":"Deepak Kumar,&nbsp;Sunil Jha","doi":"10.1016/j.cirpj.2025.08.010","DOIUrl":"10.1016/j.cirpj.2025.08.010","url":null,"abstract":"<div><div>WA-DED using CMT is emerging as a high-throughput metal AM strategy, yet it remains susceptible to a variety of thermomechanical instabilities and metallurgical discontinuities. In this study, we present an advanced AE based in-situ monitoring utilizing the generative ML framework to robustly detect and characterize anomalous conditions that compromise part integrity. Specifically, we examine five critical fault scenarios which are overcurrent, high travel speed, insufficient shielding gas flow, combination of overcurrent and low shielding gas flow rate and combination of high travel speed and low shielding gas flow rate elucidate their distinct signatures in the acoustic domain. A rigorous selection of time and frequency domain descriptors is leveraged to train the variational autoencoder, enabling accurate reconstruction of normal process states and efficient outlier detection. Microstructural analyses, encompassing FESEM, Micro-CT, and XRD, validate the detrimental influence of these faults on porosity evolution, grain morphology, and mechanical properties such as UTS. The proposed VAE model demonstrated robust performance across multiple defect types, achieving peak detection accuracies of 87% for overcurrent-induced faults, 85% for high travel speed anomalies, 81% for defects caused by insufficient shielding gas flow, 87% for combined effect of overcurrent and low gas flow rate, and 84% for combined effect of high travel speed and low gas flow rate. Overcurrent anomalies induce coarse columnar grains and high porosity content, while high travel speed amplifies geometric irregularities. Low gas flow conditions foster oxidation induced porosity. The proposed approach achieves high fidelity in detection of these defects, underscoring the synergy between data driven reconstruction errors and material characterization. By integrating unsupervised generative deep learning with domain specific interpretability through feature sensitivity analysis, this acoustic monitoring paradigm provides a scalable and cost effective pathway to detect defects and ensure structural reliability in WA-DED manufactured components. The comprehensive experimental validations and multi-physics correlational insights position this framework as a robust framework for in-situ process monitoring in WA-DED.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 185-204"},"PeriodicalIF":5.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Step-dependent machining uncertainty modeling for the process route and application in the machining of the ring-gear 工艺路线的步进不确定性建模及其在环齿加工中的应用
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-23 DOI: 10.1016/j.cirpj.2025.09.011
Yanan Zhao, Shaoming Yao
This paper proposes a step-dependent machining uncertainty modeling method for the process routes. With the between-step interaction, the process route integrity is accurately interpreted and all error sources in the production environment are involved in, including the workpiece positioning surface, machine positioning surface, cutter kinetics, workpiece kinetics, clamping force, cutting force, environmental factors, residual stress distortion, heat treatment distortion, and coating/plating variations. The machining uncertainty model shows that the machining uncertainty consists of both regenerated and inherited uncertainties. The proposed modeling method can evaluate a process route in terms of its error source impact on workpiece accuracy. A herringbone gear ring is used to demonstrate its effectiveness as a case study, where an uncertainty model is developed for the full process route, and the process route is assessed before the costly process trial. The process number is reduced from 18 to 15 without affecting the final workpiece accuracy. The experiment shows a good agreement with the uncertainty model results.
提出了一种基于步进的加工路线不确定性建模方法。通过步骤间的交互作用,可以准确地解释工艺路线的完整性,并涉及生产环境中的所有误差源,包括工件定位面、机床定位面、刀具动力学、工件动力学、夹紧力、切削力、环境因素、残余应力变形、热处理变形和涂层/电镀变化。加工不确定性模型表明,加工不确定性包括再生不确定性和继承不确定性。所提出的建模方法可以根据误差源对工件精度的影响来评估工艺路线。以人字齿环为例,建立了整个工艺路线的不确定性模型,并在昂贵的工艺试验前对工艺路线进行了评估。在不影响最终工件精度的情况下,工序数从18个减少到15个。实验结果与不确定性模型的计算结果吻合较好。
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引用次数: 0
Modelling of abrasive particle distribution for pre-mixed abrasive water jet peening surface 预混合磨料水射流喷丸表面磨料颗粒分布建模
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-22 DOI: 10.1016/j.cirpj.2025.09.010
Zhao Wang , Xiaowen Rong , Haoran Zhao , Yue Yang , Fusheng Liang , Cheng Fan
Abrasive water jet technology, as a non-traditional machining process, impinges on the workpiece surface with abrasive particles driven by the water jet beam to achieve material removal or surface modification. The abrasive particle distribution is the key factor affecting on the process quality, especially for Abrasive Water Jet Peening (AWJP) process. However, there is still limited research on the abrasive particle distribution in the AWJP process, especially regarding the distribution under variable traverse speeds and variable curvature movements of the abrasive water jet beam, which forms the basis for controlling abrasive water jet coverage, particularly on curved surfaces. In this study, an abrasive particle distribution prediction model is proposed for AWJP under different pump pressures, variable traverse speeds (accelerations), and various curvature radius by combining finite element and analytical modeling approaches. Validation experiments were conducted, and both simulation and experimental results under different parameters follow Gaussian distributions. The maximum prediction error was only 18.6 % across 24 comparisons from 15 experimental sets, confirming the feasibility and accuracy of the proposed model. Meanwhile, the influence of these three parameters on abrasive particle distribution laws is investigated respectively through comparisons between simulation and experimental results. The findings reveal that pump pressure primarily affects abrasive particle velocity and position distribution; traverse speed mainly influences abrasive particle position distribution and the percentage of particles at the central region; curvature radius predominantly affects the midline position of the abrasive particle distribution curve. This study not only provide a deep understanding of abrasive particle distribution laws under varying pump pressures, traverse speeds, and curvature radii, but the proposed model also offers valuable guidance for achieving uniform abrasive particle coverage on free-form surfaces during AWJP.
磨料水射流技术是一种非传统的加工工艺,在水射流光束的驱动下,使磨料颗粒撞击工件表面,达到去除材料或表面改性的目的。磨料颗粒分布是影响工艺质量的关键因素,特别是磨料水喷丸工艺。然而,对AWJP过程中磨料颗粒分布的研究仍然有限,特别是对磨料水射流束在变横移速度和变曲率运动下的分布,这是控制磨料水射流覆盖范围的基础,特别是在曲面上。本文采用有限元和解析建模相结合的方法,建立了不同泵压力、变横移速度(加速度)和不同曲率半径条件下AWJP的磨粒分布预测模型。进行了验证实验,不同参数下的仿真和实验结果均服从高斯分布。在15个实验集的24个对比中,最大预测误差仅为18.6 %,证实了所提出模型的可行性和准确性。同时,通过仿真结果与实验结果的对比,研究了这三个参数对磨粒分布规律的影响。结果表明:泵压力主要影响磨粒速度和位置分布;横移速度主要影响磨料颗粒的位置分布和颗粒在中心区域的百分比;曲率半径主要影响磨粒分布曲线的中线位置。这项研究不仅提供了对不同泵压力、穿越速度和曲率半径下磨粒分布规律的深入理解,而且所提出的模型也为在AWJP过程中实现自由曲面上均匀覆盖磨粒提供了有价值的指导。
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引用次数: 0
Research on the deformation mechanism for current-assisted splitting spinning forming of small-module gear-shaped parts with extreme diameter-to-module ratios 极径模比小模数齿轮件电流辅助劈裂旋压成形变形机理研究
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-18 DOI: 10.1016/j.cirpj.2025.08.012
Qinxiang Xia , Haoyang Zhou , Gangfeng Xiao , Sizhu Cheng , Junhao Zhang
Small-module gear-shaped parts (SMGSPs, module m < 1) with extreme diameter-to-module ratios (D/m>100) are critical components in miniature precision systems for spatial transmission and lightweight structural applications. However, it exhibits restricted fatigue strength and excessive material wastage when manufactured by conventional machining processes. A novel current-assisted splitting spinning forming (CASSF) method combining the precision of spinning technology with the electroplastic effects of pulsed current synergistically was proposed to realize the high-performance near-net shape forming of SMGSPs. A finite element model coupled with the electroplasticity effect is constructed. Finite element model (FEM) simulations and experimental studies systematically investigated the distribution of the electric field, temperature field, the equivalent stress and strain, and the dynamic material flow of small module gears during CASSF. The results revealed that the current density of the SMGSP is concentrated near the contact area of the roller, so the softening region, due to the electroplasticity effect, highly overlaps with the deformation region of the SMGSP. The gear profile deformation exhibits a non-uniform stress-strain distribution, with peak stress concentrations localized at the exit-side tooth root arc. The application of pulsed current effectively reduced equivalent stress and enhanced material deformability, achieving saturation thresholds at 17.5 A/mm² current density (Jp) and 40 % duty ratio (d). Five distinct material flow orientations develop during CASSF, forming four flow division surfaces between them. The uneven tooth height defect originates from asymmetric material flow between the entry and exit sides, whereas tooth underfilling stems from insufficient axial material flow. A forward-reversed forming strategy with intensified pulsed current eliminated tooth height discrepancies and improved tooth saturation (γ) to 97.8 %, demonstrating the potential of CASSF potential for forming extreme ratio SMGSPs.
具有极径模比(D/m>100)的小模块齿轮形零件(smgsp,模块m <; 1)是用于空间传动和轻量化结构应用的微型精密系统的关键部件。然而,当用传统的加工工艺制造时,它表现出有限的疲劳强度和过度的材料浪费。为了实现smgsp的高性能近净成形,提出了一种将旋压技术的精度与脉冲电流的电塑性效应协同结合的电流辅助分裂旋压成形方法。建立了考虑电塑性效应的有限元模型。通过有限元模型仿真和实验研究,系统地研究了小模数齿轮在CASSF过程中的电场、温度场、等效应力和应变分布以及动态物质流动。结果表明:SMGSP的电流密度集中在滚子接触区附近,因此由于电塑性效应,SMGSP的软化区与变形区高度重叠;齿形变形呈现非均匀应力应变分布,应力峰值集中在齿根弧出口处。脉冲电流的应用有效地降低了等效应力,增强了材料的变形能力,达到了17.5 A/mm²电流密度(Jp)和40 %占空比(d)的饱和阈值。在CASSF过程中,形成了五种不同的物质流动方向,在它们之间形成了四个流动划分面。齿高不均匀缺陷是由于进、出口侧物料流动不对称造成的,而齿深充填是由于轴向物料流动不足造成的。脉冲电流增强的正反向成形策略消除了齿高差异,并将齿饱和度(γ)提高到97.8% %,证明了CASSF潜力形成极端比smgsp的潜力。
{"title":"Research on the deformation mechanism for current-assisted splitting spinning forming of small-module gear-shaped parts with extreme diameter-to-module ratios","authors":"Qinxiang Xia ,&nbsp;Haoyang Zhou ,&nbsp;Gangfeng Xiao ,&nbsp;Sizhu Cheng ,&nbsp;Junhao Zhang","doi":"10.1016/j.cirpj.2025.08.012","DOIUrl":"10.1016/j.cirpj.2025.08.012","url":null,"abstract":"<div><div>Small-module gear-shaped parts (SMGSPs, module <em>m</em> &lt; 1) with extreme diameter-to-module ratios (<em>D</em>/<em>m</em>&gt;100) are critical components in miniature precision systems for spatial transmission and lightweight structural applications. However, it exhibits restricted fatigue strength and excessive material wastage when manufactured by conventional machining processes. A novel current-assisted splitting spinning forming (CASSF) method combining the precision of spinning technology with the electroplastic effects of pulsed current synergistically was proposed to realize the high-performance near-net shape forming of SMGSPs. A finite element model coupled with the electroplasticity effect is constructed. Finite element model (FEM) simulations and experimental studies systematically investigated the distribution of the electric field, temperature field, the equivalent stress and strain, and the dynamic material flow of small module gears during CASSF. The results revealed that the current density of the SMGSP is concentrated near the contact area of the roller, so the softening region, due to the electroplasticity effect, highly overlaps with the deformation region of the SMGSP. The gear profile deformation exhibits a non-uniform stress-strain distribution, with peak stress concentrations localized at the exit-side tooth root arc. The application of pulsed current effectively reduced equivalent stress and enhanced material deformability, achieving saturation thresholds at 17.5 A/mm² current density (<em>J</em><sub>p</sub>) and 40 % duty ratio (<em>d</em>). Five distinct material flow orientations develop during CASSF, forming four flow division surfaces between them. The uneven tooth height defect originates from asymmetric material flow between the entry and exit sides, whereas tooth underfilling stems from insufficient axial material flow. A forward-reversed forming strategy with intensified pulsed current eliminated tooth height discrepancies and improved tooth saturation (<em>γ</em>) to 97.8 %, demonstrating the potential of CASSF potential for forming extreme ratio SMGSPs.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 116-134"},"PeriodicalIF":5.4,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Gaussian process-based surrogate framework for efficient prediction of geometrical inaccuracy in Wire Electrical Discharge Machining of thin-wall miniature components 基于高斯过程的薄壁微细零件线切割加工几何误差预测替代框架
IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Pub Date : 2025-09-17 DOI: 10.1016/j.cirpj.2025.09.006
Aswin P., Rakesh G. Mote
High aspect ratio, thin-walled miniature structures are critical in applications such as microfluidics and micromechanical cooling. Wire Electrical Discharge Machining (Wire EDM) presents a commercially viable alternative to specialized micromachining setups for fabricating such features. However, as part size decreases, conventional Wire EDM faces challenges in achieving accurate profiles due to intensified thermal effects and reduced part stiffness, leading to increased geometrical errors. To address this, a reduced-order surrogate framework based on Gaussian Process Regression (GPR) is developed to predict key geometrical deviations specifically, reduced wall thickness and wall deformation as functions of process parameters. The framework integrates four GPR models trained on hybrid datasets combining experimental data and physics-based numerical results. A discrepancy model further refines numerical predictions by accounting for deviations from experimental data. The final GPR models achieve mean absolute errors of 3.39 μm and 6.08 μm for wall thickness and deformation, with R2 values of 0.96 and 0.99. K-fold cross-validation and validation experiments confirm model reliability, with prediction errors around 14.3 μm and 12.1 μm. The discrepancy model reduces the deviation of numerical predictions from actual values by 55%. Process parameter optimization is performed to fabricate thin walls with targeted deformation levels, achieving reasonable accuracy within 22.3 μm. Furthermore, sensitivity analysis is conducted to quantify both individual and interactive influences of major process parameters on geometrical errors.
高宽高比、薄壁微型结构在微流体和微机械冷却等应用中至关重要。线材电火花加工(线材EDM)提供了一种商业上可行的替代专门的微加工装置来制造这些特征。然而,随着零件尺寸的减小,由于热效应加剧和零件刚度降低,传统的线切割在实现精确轮廓方面面临挑战,从而导致几何误差增加。为了解决这一问题,开发了基于高斯过程回归(GPR)的降阶代理框架,以预测关键几何偏差,减少壁厚和壁变形作为工艺参数的函数。该框架集成了四种基于混合数据集训练的探地雷达模型,这些混合数据集结合了实验数据和基于物理的数值结果。差异模型通过考虑与实验数据的偏差进一步改进数值预测。最终GPR模型对壁厚和变形的平均绝对误差分别为3.39 μm和6.08 μm, R2分别为0.96和0.99。K-fold交叉验证和验证实验验证了模型的可靠性,预测误差分别为14.3 μm和12.1 μm。差异模型将数值预测与实际值的偏差减少了55%。通过优化工艺参数,实现了薄壁的目标变形水平,实现了22.3 μm以内的合理精度。此外,还进行了灵敏度分析,以量化主要工艺参数对几何误差的单独和相互影响。
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CIRP Journal of Manufacturing Science and Technology
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