Predictive and experimental analysis of forces in die-less forming using artificial intelligence techniques

Abstract

The use of a novel technology for producing the components of lightweight materials and to reduce the requirements of power utilized during manufacturing processes can be a great aspect to decrease pollution and save resources. Single point incremental forming (SPIF) is the viable and novel approach for manufacturing the parts of high strength and lightweight materials without involving dedicated tools and dies economically. This die-less forming technique outperforms the conventional forming techniques by saving the energy and materials. In this work, the estimation and investigation of forming forces have been accomplished to ensure the secure uses for the SPIF machines for performing this process for the designed conditions on AA2024 sheets which is a lightweight aluminum alloy being widely used in aerospace and automotive sectors. To predict the peak deforming load, machine learning (ML) techniques are employed in the current work along with the artificial neural network (ANN) by taking experimental results as the input dataset. The proposed ML model revealed better accuracy (99%) than previous work performed using similar approaches. The proposed ANN model produced lower mean absolute percentage error 4.35 as compared to other models. Authors also calculated the computing time taken during estimation of forming force. Combination of the Flatend-R1 tool and the 1.6 mm blank thickness increased the deforming loads drastically and can become the limiting factor for forming machine which should be avoided whereas the combination of hemispherical tool and lower blank thickness (0.5 mm) reduced the deforming loads that are needed to manufacture the conical frustum. It was also noticed that as the tool shape was changed from hemispherical-end to Flatend-R1, the axial peak forces were increased by 13.16%, 16.59%, 20.43%, and 22.78% for the levels 1, 2, 3, and 4 of the blank thickness, respectively.