Understanding Mist Flow Dynamics in Through-tool Minimum Quantity Lubrication Drilling

该奖项发展了设计刀具和选择用于最小数量润滑(MQL)加工的流动参数所需的知识。与传统的湿法相比，MQL以显著减少水浪费、能源消耗和总成本而闻名。该研究项目将研究MQL加工背后的科学原理，并通过与福特汽车公司的合作，将结果迅速实施到生产级操作中。由于该项目对汽车、航空航天、电子和国防工业具有广泛的适用性，它直接和积极地影响了美国的经济福利和国家安全。此外，该项目还计划通过课程整合、暑期实习和本科生研究体验(REU)计划，为本科生提供先进机械加工工艺的实践培训和研究经验。通过利用德克萨斯农工大学正在进行的教师研究经验(RET)计划，该项目还包括向高中开展现代刀具设计的外联活动，以促进STEM领域的发展。与工业伙伴的密切合作也提供了一个独特的环境，使这些计划与制造业的需求保持一致。这项研究结合了实验和数值方法，解决了最小数量润滑的基础科学。钻探是目标过程，因为它在制造业中很常见，而钻探是复杂的，不能用肉眼观察。该项目有三个主要目标：目标1根据几何参数、系统参数和加工参数来表征内部通道的流动。MQL流动试验台将被用来模拟加工中的雾流，并允许通过高速摄像机直观地进入切削区。目标2解决了流动结构和钻头-工件运动之间的相互作用，从而将预测刀具边缘和表面上的流体覆盖率。流动测试结果将与使用单相流和两相流分析的计算流体力学(CFD)进行比较。目标3将确定雾流对钻井性能的影响，包括扭矩、推力、堆积边缘和磨损，对福特AME MQL实验室生产系统的影响。生成的知识将使特定于MQL的工具设计和选择、自适应MQL系统和流程开发成为可能。更广泛的影响包括改善制造业的MQL实施，以及行业和学术界之间为劳动力发展而开展的合作活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award develops knowledge required to design cutting tools and select flow parameters for minimum quantity lubrication (MQL) machining. MQL is known for its advantages of significant reduction in water waste, energy use, and overall costs as compared to traditional wet processes. This research project will investigate the science behind MQL machining and rapidly implement the results into production-level operations through the collaboration with Ford Motor Company. Since the project has broad applicability to automotive, aerospace, electronics and defense industries, it directly and positively impacts American economic welfare and national security. Additionally, the project includes plans for hands-on training and research experience on advanced machining processes for undergraduate students via course integration, summer internship, and Research Experience for Undergraduates (REU) programs. By leveraging an on-going Research Experience for Teachers (RET) program at Texas A&M, this project also includes outreach activities to high schools on design of modern cutting tools to promote STEM field. The close collaboration with the industrial partner also provides a unique environment to align these plans with the needs in the manufacturing industry.This research incorporates experimental and numerical approaches that address the fundamental science of minimum quantity lubrication. Drilling is the target process as it is common in manufacturing, whereas drilling is complex and cannot be observed visually. The project has three main objectives: Objective 1 characterizes the flow out of internal channels based on geometrical, system, and machining parameters. An MQL flow testbed will be used to emulate the mist flow in machining and allow visual access to the cutting zone with a high-speed camera. Objective 2 addresses interactions between flow structure and drill-workpiece motion and thus will predict the fluid coverage on the tool edges and faces. The flow test results will be compared with computational fluid dynamics (CFD) using both single- and dual-phase flow analyses. Objective 3 will determine the effects of mist flow on drilling performance, including torque, thrust force, built-up edge, and wear, on production systems at the Ford AME MQL laboratory. The knowledge generated will enable MQL-specific tool design and selection, adaptive MQL systems, and process development. The broader impacts include improved MQL implementation in the manufacturing industry and collaborative activities between the industry and academia for the workforce development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.