Data-driven Modeling and Optimization for Energy-Smart Manufacturing

能源智能制造的数据驱动建模和优化

• 批准号: 1634867
• 负责人: Ran Jin
• 金额: $ 30万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1634867&HistoricalAwards=false
• 关键词: Data; driven; Modeling; Optimization; Energy

The concept of energy-smart manufacturing is to deliver customized products while simultaneously optimizing energy consumption, product performance (e.g., product functionality, quality and process variability) and equipment maintenance cost. Many previous attempts with similar goals focus on one objective at a time. In reality, because energy consumption, product performance, and equipment maintenance are correlated, decisions related to one aspect will often affect other aspects. This award will support fundamental research to discover the interactions among energy efficiency, product performance, and equipment maintenance. The knowledge thus gained will be used to optimize the manufacturing processes and maintenance operations for high energy efficiency and low cost. The methodology is intended to be widely applicable to many different types of manufacturing operations. The research results will be broadly disseminated to equip the current and future manufacturing engineers with the new methodologies through joint workshops with an industrial collaborator, technical training sessions, and case studies. Summer outreach workshops will be organized to engage high school students from underrepresented groups. The objective of this research is to customize data-driven modeling and optimization methodology to achieve high energy efficiency, excellent product performance, and low maintenance cost in manufacturing. A data-driven decision-making framework will be developed with the following intellectual merits: (1) dynamic models will be developed to quantify the product performance by considering equipment degradation effects and the change of product types; (2) a degradation index will be constructed from multivariate degradation measurements and a cumulative damage model will be used to predict equipment degradation; and (3) energy efficiency and maintenance cost will be optimized through customization of optimization algorithms at both the manufacturing system and the enterprise levels. These methodologies will be validated in a plasma spray coating process in the aero-engine manufacturing industry, and it will be designed to be broadly applicable to other high-energy-consumption manufacturing operations.

能源智能制造的概念是提供定制的产品，同时优化能源消耗、产品性能（例如，产品功能、质量和工艺可变性）和设备维护成本。以前有许多类似目标的尝试一次只关注一个目标。在现实中，由于能源消耗、产品性能和设备维护是相互关联的，因此与一个方面相关的决策往往会影响其他方面。该奖项将支持基础研究，以发现能源效率，产品性能和设备维护之间的相互作用。由此获得的知识将用于优化制造工艺和维护操作，以实现高能效和低成本。该方法旨在广泛适用于许多不同类型的制造业务。研究成果将广泛传播，通过与工业合作者的联合研讨会，技术培训课程和案例研究，为当前和未来的制造工程师提供新的方法。将组织夏季外联讲习班，让代表性不足群体的高中生参与。 本研究的目的是定制数据驱动的建模和优化方法，以实现高能源效率，卓越的产品性能和低维护成本的制造。将建立一个数据驱动的决策框架，该框架具有以下智力优势：（1）通过考虑设备退化效应和产品类型的变化，建立动态模型来量化产品性能;（2）将从多变量退化测量构建退化指数，并使用累积损伤模型来预测设备退化;以及（3）将通过在制造系统和企业级别上定制优化算法来优化能量效率和维护成本。这些方法将在航空发动机制造业的等离子喷涂工艺中得到验证，并将被设计为广泛适用于其他高能耗制造业务。

项目成果

Clustering-based Data Filtering for Manufacturing Big Data System

基于聚类的制造大数据系统数据过滤

• 发表时间: 2020
• 期刊: Journal of quality technology
• 影响因子: 2.5
• 作者: Li, Y.;Deng, X.;Jin, R.;Ba, S.;Myers, W.
• 通讯作者: Myers, W.

Semiparametric Models for Accelerated Destructive Degradation Test Data Analysis

用于加速破坏性降解测试数据分析的半参数模型

• DOI: 10.1080/00401706.2017.1321584
• 发表时间: 2017
• 期刊: Technometrics
• 影响因子: 2.5
• 作者: Xie, Yimeng;King, Caleb B.;Hong, Yili;Yang, Qingyu
• 通讯作者: Yang, Qingyu

Big data and reliability applications: The complexity dimension

• DOI: 10.1080/00224065.2018.1438007
• 发表时间: 2018-01-01
• 期刊: JOURNAL OF QUALITY TECHNOLOGY
• 影响因子: 2.5
• 作者: Hong, Yili;Zhang, Man;Meeker, William Q.
• 通讯作者: Meeker, William Q.

Profile monitoring based quality control method for fused deposition modeling process

• DOI: 10.1007/s10845-018-1424-9
• 发表时间: 2019-02-01
• 期刊: JOURNAL OF INTELLIGENT MANUFACTURING
• 影响因子: 8.3
• 作者: He, Ketai;Zhang, Qian;Hong, Yili
• 通讯作者: Hong, Yili

Family learning: A process modeling method for cyber-additive manufacturing network

• DOI: 10.1080/24725854.2020.1851824
• 发表时间: 2021-02
• 期刊: IISE Transactions
• 影响因子: 2.6
• 作者: Lening Wang;Xiaoyu Chen;D. Henkel;R. Jin