Investigation of Mechanical Loads Distribution for the Process of Generating Gear Grinding
Investigation of Mechanical Loads Distribution for the Process of Generating Gear Grinding
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DOI：
10.3390/jmmp5010013
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发表时间：
2021-03-01
影响因子：
3.2
通讯作者：
Bergs, Thomas
中科院分区：
文献类型：
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作者：
de Oliveira Teixeira, Patricia;Brimmers, Jens;Bergs, Thomas
在磨削中，工件材料和旋转的研磨工具之间的相互作用在接触区域中产生高的热机械载荷。如果这些载荷达到临界高值，工件材料性能恶化。为了防止材料变质，已经开发了几种用于磨削过程热力学分析的模型。在这些模型中，在温度分布计算中通常将热流源视为均匀的。然而，众所周知，磨削中的热通量是由工件材料与来自工具的每个颗粒之间的相互作用期间的摩擦加热以及塑性变形产生的。要考虑这些因素，在未来的耦合热力学模型，特别是齿轮展成磨削的过程中，在考虑过程运动学的颗粒和工件材料之间的相互作用的机械载荷分布的调查是第一个需要的。研究了齿轮展成磨削加工中工艺参数和磨粒形状对单磨粒机械载荷沿着分布的影响。对于这项调查，提出了一个适应的单晶粒能量模型，考虑到芯片的形成机制。磨削能和法向力可以通过测量值或仅基于预测模型来确定。
In grinding, interaction between the workpiece material and rotating abrasive tool generates high thermo-mechanical loads in the contact zone. If these loads reach critically high values, workpiece material properties deteriorate. To prevent the material deterioration, several models for thermomechanical analysis of grinding processes have been developed. In these models, the source of heat flux is usually considered as uniform in the temperature distribution calculation. However, it is known that heat flux in grinding is generated from frictional heating as well as plastic deformation during the interaction between workpiece material and each grain from the tool. To consider these factors in a future coupled thermomechanical model specifically for the process of gear generating grinding, an investigation of the mechanical load distribution during interaction between grain and workpiece material considering the process kinematics is first required. This work aims to investigate the influence of process parameters as well as grain shape on the distribution of the mechanical loads along a single-grain in gear generating grinding. For this investigation, an adaptation of a single-grain energy model considering the chip formation mechanisms is proposed. The grinding energy as well as normal force can be determined either supported by measurements or solely based on prediction models.