Investigation of Mechanical Loads Distribution for the Process of Generating Gear Grinding

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 thermomecha...

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Bibliographic Details
Published in:Journal of Manufacturing and Materials Processing Vol. 5; no. 1; p. 13
Main Authors: de Oliveira Teixeira, Patricia, Brimmers, Jens, Bergs, Thomas
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-03-2021
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Summary: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.
ISSN:2504-4494
2504-4494
DOI:10.3390/jmmp5010013