GOALI: Investigation of High-Speed Face Milling of Difficult-to-Cut Materials with Minimum Quantity Lubrication Using High Oleic Soybean Oil-Based Nanofluids

这项学术与工业联络资助机会（GOALI）奖支持的研究将建立有关航空航天和汽车工业中使用的更难切割材料的更可持续和环保的加工方法的知识。这些材料，如Inconel 718，由于刀具和工件之间的高摩擦产生的剪切力和热量，导致刀具磨损快，生产率低，因此对加工有问题。用于机械加工的传统冷却技术使该区域充斥着乳化液冷却剂，导致诸如高冷却剂消耗和处理问题等问题。该奖项支持基于最少量润滑方法和使用纳米颗粒填充的豆油基液体的替代冷却和润滑策略的基础研究。最小量润滑采用少量润滑剂和压缩空气形成气溶胶，通过喷嘴以雾状喷射，润滑和冷却切割区，避免了传统乳液驱法相关的问题和成本。新的切削液可以取代传统的切削液，从而实现更环保的加工过程，润滑剂来自当地的大豆种植者。这项研究将产生新的界面工程知识和新的切削力模型。切削力模型将集成到远程教育的虚拟加工软件中，该奖项将扩大未被充分代表的群体在研究和工程教育中的参与。低油和高油大豆油将被用作制备纳米流体的基础液，利用具有不同亲水-亲脂平衡的表面活性剂和四种不同的纳米颗粒，包括石墨烯纳米片。纳米流体的性质——包括粘度、导热性、分散稳定性、摩擦系数和雾流——将被评估为温度、纳米颗粒类型和浓度的函数，使用一套材料表征技术，包括光谱方法、动态光散射、流变学、摩擦流变学和导热学。该配方纳米流体的性能将在使用最少量润滑方法加工Inconel 718和压实石墨铁时进行评估，并与传统的乳化液驱油方法进行对比。可加工性参数，包括切削力成分、刀具磨损和寿命、切屑形态和表面完整性，将被研究。利用特定的切削力和刃力系数，建立机械切削力预测模型，以了解和预测最佳冷却和润滑策略。该团队在机械和化学工程方面的跨学科专业知识，以及工业合作伙伴在机械加工和流体工程方面的经验，将得到充分利用，而GOALI合作伙伴关系将促进研究成果向工厂车间的转移。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Grant Opportunity for Academic Liaison with Industry (GOALI) award supports research that will build knowledge about a more sustainable and eco-friendly method of machining difficult-to-cut materials used in aerospace and automotive industries. These materials, such as Inconel 718, are problematic to machine due to the shear force and heat generated by the high friction between tool and workpiece, which leads to rapid tool wear and low productivity. Conventional cooling techniques used for machining flood the area with an emulsion coolant, causing issues like high coolant consumption and disposal problems. This award supports fundamental research to investigate alternative cooling and lubrication strategies based on a minimum quantity lubrication approach and using nanoparticle-filled soybean oil-based fluids. Minimum quantity lubrication employs a small amount of lubricant and compressed air to form an aerosol, which is sprayed in mist form through a nozzle to lubricate and cool the cutting zone, avoiding the problems and costs associated with the conventional emulsion flood method. The new cutting fluids can replace conventional ones, resulting in a more eco-friendly process with lubricant sourced from local soybean farmers. This research will result in new interfacial engineering knowledge and new cutting force models. The cutting force model will be integrated into virtual machining software for tele-education, and the award will broaden the participation of underrepresented groups in research and engineering education.Low-oleic and high-oleic soybean oils will be employed as base fluids to formulate nanofluids, utilizing surfactants with varying hydrophilic-lipophilic balance and four different nanoparticles, including graphene nanoplatelets. The properties of the nanofluids—including viscosity, thermal conductivity, dispersion stability, coefficient of friction, and mist flow—will be evaluated as a function of temperature, nanoparticle type, and concentration using a suite of material characterization techniques, including spectroscopic methods, dynamic light scattering, rheometry, tribo-rheometry, and thermal conductimetry. The performance of the formulated nanofluids will be evaluated for machining Inconel 718 and compacted graphite iron using a minimum quantity lubrication approach, and benchmarked against the conventional emulsion flood method. Machinability parameters, including cutting force components, tool wear and life, chip morphology, and surface integrity, will be investigated. A mechanistic cutting force prediction model will be established, using specific cutting and edge force coefficients, to understand and predict the best cooling and lubrication strategies. The interdisciplinary expertise of the team in mechanical and chemical engineering, and the industrial partners’ experience in machining and fluid engineering, will be leveraged, while the GOALI partnership will facilitate transfer of research outcomes to the factory floor.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.