AbstractIn order to reduce fuel consumption and thus pollutant emissions, the automotive industry is increasingly developing lightweight construction concepts that are accompanied by an increasing usage of aluminum materials. Due to poor weldability of aluminum in combination with other materials, mechanical joining methods such as clinching were developed and established in series production. In order to predict the relevant characteristics of clinched joints and to ensure the reliability of the process, it is simulated numerically during product development processes. In this regard, the predictive accuracy of the simulated process highly depends on the implemented friction model. In particular, the frictional behavior between the sheet metals as well as between the sheet metal and clinching tools has a significant impact on the geometrical formation of the clinched joint. No testing methods exist that can sufficiently investigate the frictional behavior in sheet materials, especially under high interface pressures, different relative velocities, and long friction paths, while allowing a decoupled consideration of the test parameters. This paper describes the development of further testing concepts based on a proven tribo-torsion test method for determining friction coefficients between sheet metal materials for the simulation of clinching processes. For this purpose, the correlation of interface pressure and the relative velocity between aluminum and steel sheet material in clinching processes is investigated using numerical simulation. Based on these findings, the developed concepts focus on determining friction coefficients at interface pressures of the above materials, yield stress, as well as the reproduction of the occurring friction conditions between sheet metal materials and tool surfaces in clinching processes using tool substitutes. Furthermore, wear investigations between sheet metal material and tool surface were carried out in the friction tests with subsequent EDX analyses of the frictioned tool surfaces. The developed method also allows an optical deformation measurement of the sheet metal material specimen by means of digital image correlation (DIC). Based on a methodological approach, the test setups and the test systems used are explained, and the functionality of the concepts is proven by experimental tests using different sheet metal materials.
Optimization of Sheet Metal Forming Process Parameters by Artificial Neural Network and Orthogonal Test Method
