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

这个学术联络与工业（GOALI）奖的资助机会支持加工动力学和工艺性能之间的联系的基础研究。研究结果可以导致在传统上避免的条件下加工高质量的零件，因为它们被认为是不稳定的。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该行业和其他制造业转让技术。该奖项还支持两项教育推广工作：（1）为我设计工程，为夏洛特梅克伦堡中学的女中学生举办为期一天的活动，包括讨论工程职业，团队比赛，集体头脑风暴，讨论工程师如何影响社会，并通过问卷进行评估;以及（2）猪皮教授先进制造视频系列，以简单的语言描述先进制造概念。研究的重点是周期n分叉（即，一种特殊类型的颤振或自激振动）。本研究的目的是建立以下关系：（1）期间-n行为之间（其描述振动状态的重复之间的周期数n）和铣削参数（主轴转速、轴向和径向切削深度以及每齿进给量）;（2）周期n行为与加工零件的几何精度之间的关系;以及（3）周期n行为与加工零件表面粗糙度之间的关系。实现该目标的方法是通过求解描述铣削过程的一组二阶延迟微分方程来导出全面的时域模型。该时域模型将用于预测这些关系。此外，时域模型将被用来选择实验条件，表现出周期n分叉。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，将通过激光振动计测量周期-n行为，将使用坐标计量法测量零件精度，并通过相干扫描干涉法测量表面粗糙度。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.