GOALI: Reducing Manufacturing Cost for the Energy Industry through Predictive Process Modeling

目标：通过预测过程建模降低能源行业的制造成本

• 批准号: 1561221
• 负责人: Tony Schmitz
• 金额: $ 26.59万
• 依托单位: University of North Carolina at Charlotte
• 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: 2016-05-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561221&HistoricalAwards=false
• 关键词: GOALI; Reducing; Manufacturing; Cost; Energy

This Grant Opportunity for Academic Liaison with Industry (GOALI) award supports fundamental research on the link between machining dynamics and process performance. Research results can lead to machining high-quality parts under conditions that are traditionally avoided because they are considered unstable. This new approach can provide increased material removal rates and, consequently, reduced machining times, while simultaneously producing high quality parts. Collaboration with a partner from the energy industry will help ensure the technology transfer to this and other manufacturing industries. The award also supports two education outreach efforts: (1) Project Engineering for Me, one-day events for middle school girls in Charlotte-Mecklenburg schools, that includes a discussion of engineering careers, a team competition, group brainstorming to discuss how engineers can impact society, and assessment through questionnaires; and (2) the Pigskin Professor Advanced Manufacturing video series that describes advanced manufacturing concepts in simple language. The research focus is period-n bifurcations (i.e., a special category of chatter, or self-excited vibration) in low radial immersion milling. The research objective is to establish relationships: (1) between the period-n behavior (which describes the number of periods, n, between repetitions of the vibration state) and milling parameters (spindle speed, axial and radial depths of cut, and feed per tooth); (2) between the period-n behavior and geometric accuracy of machined parts; and (3) between the period-n behavior and surface roughness of machined parts. The approach to achieve the objective is to derive a comprehensive time domain model by solving a set of second order delay differential equations that describe the milling process. This time domain model will be used to predict these relationships. In addition, the time domain model will be used to select experimental conditions that exhibit period-n bifurcations. Machining experiments will then be conducted under these conditions using aluminum, steel, and nickel alloys. For each machining experiment, the period-n behavior will be measured by a laser vibrometer, part accuracy will be measured using coordinate metrology, and surface roughness will be measured by coherence scanning interferometry. The measurement data will then be compared with the model predictions, including period-n behavior, part accuracy, and surface roughness.

该授予学术与工业联络机会 (GOALI) 奖项支持关于加工动力学与工艺性能之间联系的基础研究。研究成果可以在传统上因被认为不稳定而避免的条件下加工出高质量的零件。这种新方法可以提高材料去除率，从而减少加工时间，同时生产高质量的零件。与能源行业合作伙伴的合作将有助于确保向能源行业和其他制造业的技术转让。该奖项还支持两项教育推广工作：(1)“我的项目工程”，为夏洛特-梅克伦堡学校的中学生举办的为期一天的活动，其中包括工程职业讨论、团队竞赛、小组头脑风暴，讨论工程师如何能够影响社会，并通过问卷进行评估； (2) Pigskin 教授先进制造视频系列，用简单的语言描述先进制造概念。研究重点是低径向浸入式铣削中的 n 期分岔（即一种特殊类别的颤振或自激振动）。研究目标是建立以下关系：(1) 周期 n 行为（描述振动状态重复之间的周期数 n）与铣削参数（主轴转速、轴向和径向切削深度以及进给量）之间的关系每颗牙齿）； (2) 加工零件的n周期行为和几何精度之间的关系； (3) 周期 n 行为与加工零件的表面粗糙度之间的关系。实现该目标的方法是通过求解一组描述铣削过程的二阶延迟微分方程来导出综合时域模型。该时域模型将用于预测这些关系。此外，时域模型将用于选择表现出n周期分岔的实验条件。然后将在这些条件下使用铝、钢和镍合金进行加工实验。对于每个加工实验，周期 n 行为将通过激光振动计测量，零件精度将使用坐标计量测量，表面粗糙度将通过相干扫描干涉测量测量。然后将测量数据与模型预测进行比较，包括周期 n 行为、零件精度和表面粗糙度。