Predictive Modeling and Optimization of Machining Induced Surface Integrity with Applications in Titanium and Nickel-Based Alloyed End Products

加工引起的表面完整性的预测建模和优化及其在钛和镍基合金最终产品中的应用

• 批准号: 1130780
• 负责人: Tugrul Ozel
• 金额: $ 38.83万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-15 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1130780&HistoricalAwards=false
• 关键词: Predictive; Modeling; Optimization; Machining; Induced

The research objective of this award is to establish an integrated physics-based, predictive modeling approach to improve surface integrity and optimize machining operations in the manufacturing of titanium and nickel-based alloyed end products. The goal is to control surface integrity, machining-induced layer thickness, depth-of-work hardening, tensile layer thickness, residual stresses, and micro-hardness of the end product, as well as representing the cutting tool parameter (material, coating and edge geometry) effects, and the effects of cutting conditions on these results. The proposed research will be conducted in a three-pronged approach, including physics-based modeling, experimental analyses and validation, and probabilistic-predictive modeling on (1) determination of detailed friction between tool and workpiece, and tool wear, (2) physics-based finite element simulations using temperature-dependent flow softening based constitutive material models to compute process outputs, including surface properties, and validating them with experiments, and (3) probabilistic predictive modeling and multi-criteria optimization.If successful, the benefits and broader impacts of this research will be the use of predictive and physics-based simulation modeling approaches applied to machining-induced surface integrity predictions in titanium and nickel alloys. It is expected that this award will result in methods to improve surface integrity on machined titanium and nickel alloy metal components used in aerospace, medical devices and other related industries. This award will also provide exposure for graduate and undergraduate students to methods of predictive modeling and physics-based simulation modeling in manufacturing processes.

该奖项的研究目标是建立一种基于物理的集成预测建模方法，以提高钛和镍基合金最终产品制造过程中的表面完整性并优化加工操作。 目标是控制最终产品的表面完整性、加工引起的层厚度、加工硬化深度、拉伸层厚度、残余应力和显微硬度，以及表示切削刀具参数（材料、涂层和边缘几何形状）的影响，以及切削条件对这些结果的影响。拟议的研究将以三管齐下的方式进行，包括基于物理的建模、实验分析和验证以及概率预测建模：(1) 确定刀具和工件之间的详细摩擦以及刀具磨损，(2) 物理- 基于有限元模拟，使用基于温度相关流动软化的本构材料模型来计算过程输出，包括表面特性，并通过实验验证它们，以及（3）概率预测建模和多标准优化。如果成功，将带来好处和这项研究的更广泛影响将是使用基于物理的预测模拟建模方法来预测钛和镍合金中加工引起的表面完整性。预计该奖项将产生改善航空航天、医疗器械和其他相关行业中使用的机加工钛和镍合金金属部件表面完整性的方法。该奖项还将让研究生和本科生接触制造过程中的预测建模和基于物理的仿真建模方法。