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

该学术联络机会奖（GOALI）支持机械加工动力学和工艺性能之间联系的基础研究。研究结果可以在传统上被认为不稳定而避免的条件下加工出高质量的零件。这种新方法可以提供更高的材料去除率，从而减少加工时间，同时生产高质量的零件。与能源行业的合作伙伴合作将有助于确保技术转移到该行业和其他制造业。该奖项还支持两项教育推广工作：(1)“我的工程项目”，为夏洛特-梅克伦堡学校的中学女生举办为期一天的活动，其中包括对工程职业的讨论、团队竞赛、讨论工程师如何影响社会的集体头脑风暴，以及通过问卷进行评估；(2)猪皮教授先进制造系列视频，用简单的语言描述先进的制造概念。研究的重点是低径向浸没铣削中的n周期分岔（即一种特殊的颤振或自激振动）。研究目标是建立：(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每齿进给量）之间的关系；(2)周期n行为与被加工零件几何精度之间的关系；(3)周期n行为与被加工零件表面粗糙度之间的关系。实现这一目标的方法是通过求解描述铣削过程的一组二阶时滞微分方程，推导出一个综合的时域模型。这个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周期n分岔的实验条件。然后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行为将由激光测振仪测量，零件精度将使用坐标测量法测量，表面粗糙度将通过相干扫描干涉测量法测量。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。

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.