STTR Phase I: An Inference Engine for an Intelligent Imaging System for Detecting and Eliminating Hot Rolled Seams

STTR 第一阶段：用于检测和消除热轧接缝的智能成像系统的推理引擎

• 批准号: 0512749
• 负责人: Tzyy-Shuh Chang
• 金额: --
• 依托单位: OG TECHNOLOGIES, INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0512749&HistoricalAwards=false
• 关键词: STTR; Phase; Inference; Engine; Intelligent

This Small Business Technology Transfer Research (STTR) Project will develop an imaging system with intelligence so that it can be used as an intelligent advisor for process design and control. Integrating the inspection system with an inference engine provides the intelligence in the imaging system. This inference engine will be based on software for predicting the formation of seams in the multi-pass hot rolling process, a probabilistic approach for predicting defect frequency and a framework for design optimization and on-line process control. The project will develop the predictive finite element algorithm and to provide it the capability of handling material, process and design uncertainties. An innovative model based inspection and control system, could integrate inspection and measurement systems with an inference engine based on probability of on fault generation. This could provide a tool to determine the severity and frequency of defects and permit changes to be made to correct the process for defect elimination. This inference engine can also be used for off-line design of robust rolling schedules that are insensitive to uncontrolled noise factors. The broader (commercial) impacts of this technology will be the ability to identify control parameters to detect surface defects in seams. The ability to identify defects will provide the steel rolling industry to have the ability to increase productivity and have products with improved surface qualities.

这个小企业技术转移研究（STTR）项目将开发一个智能成像系统，使其可以用作过程设计和控制的智能顾问。将检查系统与推理机集成提供了成像系统中的智能。该推理机将基于用于预测多道次热轧过程中接缝形成的软件、用于预测缺陷频率的概率方法以及用于设计优化和在线过程控制的框架。该项目将开发预测有限元算法，并提供处理材料，工艺和设计不确定性的能力。一个创新的基于模型的检测和控制系统，可以集成检测和测量系统与推理机的基础上的概率故障产生。 这可以提供一种工具来确定缺陷的严重性和频率，并允许进行更改以纠正消除缺陷的过程。该推理机也可用于离线设计的鲁棒轧制计划，是不受控制的噪声因素。该技术的更广泛（商业）影响将是识别控制参数以检测接缝中的表面缺陷的能力。 识别缺陷的能力将为轧钢行业提供提高生产率的能力，并使产品具有改进的表面质量。