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.