GOALI: Engineering-Driven Modeling of Multi-Resolution Data for Surface Variation Control

GOALI:用于表面变化控制的多分辨率数据的工程驱动建模

基本信息

项目摘要

This GOALI project develops methodologies and algorithms to reduce surface shape variation, enabling cost-effective high-precision machining by integrating process physics with multi-resolution surface data for quality diagnosis and monitoring. The integration of the process physics model and multi-resolution data will improve surface data modeling, variation prediction accuracy, and interpretability. Based on the improved data model, a cost-effective surface measurement strategy and monitoring scheme will be developed by which abnormal process variations can be detected and identified with reduced false alarm rates. The model will also improve surface variation diagnosis. By combining the modeling and monitoring approaches, a two-level surface variation control methodology is established to improve surface quality for multistage machining processes. The PI and co-PIs will collaborate with major US automakers and/or powertrain system suppliers for data acquisition, experiments, algorithm verification and validation, and technology transfer.The outcome of this research provides new insights into surface variations in multistage machining processes and will transform in-plant quality control practice from dimensional variation reduction to surface shape variation control. If successful, this project will enhance US manufacturing competitiveness by improving powertrain machining precision and quality. The surface modeling and control framework established in this research focuses on face milling and can potentially benefit other surface machining applications. In addition, the methodology of surface variation control established in this research will contribute to development of a novel optical surface measurement system that can intelligently select metrology resolution and determine defective surface regions. The surface variation monitoring scheme can also be extended to quality control for micro manufacturing processes by reducing the measurement time of high-definition metrology such as atomic force microscopy and 3D profilometry.
该GOALI项目开发了减少表面形状变化的方法和算法,通过将过程物理学与多分辨率表面数据集成在一起进行质量诊断和监控,从而实现具有成本效益的高精度加工。过程物理模型和多分辨率数据的集成将改进表面数据建模、变化预测精度和可解释性。基于改进的数据模型,将开发一种具有成本效益的表面测量策略和监测方案,通过该方案可以检测和识别异常过程变化,降低误报率。该模型还将改善表面变化诊断。通过结合建模和监控方法,两个层次的表面变化控制方法建立,以提高表面质量的多级加工过程。PI和co-PI将与美国主要汽车制造商和/或动力系统供应商合作,进行数据采集、实验、算法验证和技术转让。这项研究的成果为多阶段加工过程中的表面变化提供了新的见解,并将把工厂质量控制实践从减少尺寸变化转变为表面形状变化控制。如果成功,该项目将通过提高动力总成加工精度和质量来增强美国制造业的竞争力。本研究所建立的曲面造型与控制架构,主要针对面铣加工,并可应用于其他曲面加工领域。此外,在这项研究中建立的表面变化控制的方法将有助于开发一种新的光学表面测量系统,可以智能地选择计量分辨率和确定有缺陷的表面区域。表面变化监测方案还可以通过减少原子力显微镜和3D轮廓术等高清晰度计量的测量时间来扩展到微制造过程的质量控制。

项目成果

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Hui Wang其他文献

Exosomal CD44 cooperates with integrin α6β4 to support organotropic metastasis via regulating tumor cell motility and target host cell activation
外泌体 CD44 与整合素 α6β4 配合,通过调节肿瘤细胞运动和靶宿主细胞激活来支持器官转移
  • DOI:
    10.1016/j.eng.2020.08.013
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    12.8
  • 作者:
    Mu Wei;Xu Yajie;Gu Pengfei;Wang Wenbo;Li Jingquan;Ge Yang;Hui Wang
  • 通讯作者:
    Hui Wang
Burr formation in milling cross-connected microchannels with a thin slotting cutter
使用薄槽刀铣削交叉连接的微通道时毛刺的形成
Magnetically tuned photoelectric response observed in nanoscale Co-SiO2-Si structures
在纳米级 Co-SiO2-Si 结构中观察到的磁调谐光电响应
  • DOI:
    10.1088/1361-6528/aa85ff
  • 发表时间:
    2017-10
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Qian Zhang;Jieqiong Hu;Diyuan Zheng;Anhua Dong;Hui Wang
  • 通讯作者:
    Hui Wang
Modeling, simulation, and fabrication of electron optic system for application on 105 GHz high‐power gyrotron
适用于 105 GHz 高功率回旋管的电子光学系统的建模、仿真和制造
  • DOI:
    10.1002/jnm.2593
  • 发表时间:
    2019-03
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Guo Guo;Xinjian Niu;Yinghui Liu;Hui Wang;Jianwei Liu;Yanyu Wei
  • 通讯作者:
    Yanyu Wei
An Improved HOG Based Pedestrian Detector
一种改进的基于 HOG 的行人检测器
  • DOI:
    10.1007/978-3-642-37835-5_50
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chao Gao;Fengcai Qiao;Xin Zhang;Hui Wang
  • 通讯作者:
    Hui Wang

Hui Wang的其他文献

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{{ truncateString('Hui Wang', 18)}}的其他基金

Ligand Dynamics and Chemistry on Locally Curved Metallic Nanoparticle Surfaces
局部弯曲金属纳米颗粒表面的配体动力学和化学
  • 批准号:
    2202928
  • 财政年份:
    2022
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
VIPIRS - Virus Identification via Portable InfraRed Spectroscopy
VIPIRS - 通过便携式红外光谱仪进行病毒识别
  • 批准号:
    EP/V026488/2
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Research Grant
Multimodal Video Search by Examples (MVSE)
多模态视频搜索示例 (MVSE)
  • 批准号:
    EP/V002740/2
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Research Grant
Multimodal Video Search by Examples (MVSE)
多模态视频搜索示例 (MVSE)
  • 批准号:
    EP/V002740/1
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Research Grant
CAREER:Understanding Interfaces in Sulfide-based All-Solid-State Na Batteries
职业:了解硫化物基全固态钠电池中的界面
  • 批准号:
    2047460
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
VIPIRS - Virus Identification via Portable InfraRed Spectroscopy
VIPIRS - 通过便携式红外光谱仪进行病毒识别
  • 批准号:
    EP/V026488/1
  • 财政年份:
    2020
  • 资助金额:
    $ 30万
  • 项目类别:
    Research Grant
RII Track 4: Understanding Defect Chemistry in Sodium Chalcogenide Superionic Conductors by Advanced Neutron Technology
RII 轨道 4:通过先进中子技术了解硫属化钠超离子导体中的缺陷化学
  • 批准号:
    2033397
  • 财政年份:
    2020
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Excellence in Research: Reconfigurable Supply Chain Network Design and Assembly Planning for Factory-in-a-Box Manufacturing
卓越研究:盒装工厂制造的可重构供应链网络设计和装配规划
  • 批准号:
    1901109
  • 财政年份:
    2019
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
EAGER/Collaborative Research: Explore the Theoretical Framework of Engineering Knowledge Transfer in Cybermanufacturing Systems
EAGER/协作研究:探索网络制造系统中工程知识转移的理论框架
  • 批准号:
    1744131
  • 财政年份:
    2017
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Establishment of International Plant and Insect Pathogen Sequence Database (IPIPSD) Using Existing Deep Sequencing Data
利用现有深度测序数据建立国际植物和昆虫病原体序列数据库(IPIPSD)
  • 批准号:
    NE/L012863/1
  • 财政年份:
    2014
  • 资助金额:
    $ 30万
  • 项目类别:
    Research Grant

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Frontiers of Environmental Science & Engineering
  • 批准号:
    51224004
  • 批准年份:
    2012
  • 资助金额:
    20.0 万元
  • 项目类别:
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Chinese Journal of Chemical Engineering
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    2012
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Chinese Journal of Chemical Engineering
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  • 批准年份:
    2010
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Collaborative Research: Data-driven engineering of the yeast Kluyveromyces marxianus for enhanced protein secretion
合作研究:马克斯克鲁维酵母的数据驱动工程,以增强蛋白质分泌
  • 批准号:
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Rules of life in CO2-driven microbial communities: Microbiome engineering for a Net Zero future
二氧化碳驱动的微生物群落的生命规则:净零未来的微生物组工程
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  • 批准号:
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CAREER: HayaRupu: Accelerating Natural Hazard Engineering with AI-Driven Discovery Loops
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