Investigation of the Robotized Incremental Metal-Sheet Forming Process with Ultrasonic Excitation.

During the single-point incremental forming (SPIF) process, a sheet is formed by a locally acting stress field on the surface consisting of a normal and shear component that is strongly affected by friction of the dragging forming tool. SPIF is usually performed under well-lubricated conditions in order to reduce friction. Instead of lubricating the contact surface of the sheet metal, we propose an innovative, environmentally friendly method to reduce the coefficient of friction by ultrasonic excitation of the metal sheet. By evaluating the tool-workpiece interaction process as non-linear due to large deformations in the metal sheet, the finite element method (FEM) allows for a virtual evaluation of the deformation and piercing parameters of the SPIF process in order to determine destructive loads.

Investigation of Advanced Robotized Polymer Sheet Incremental Forming Process.

The aim of this work is to evaluate the possibility of inexpensively producing small-batch polymer sheet components using robotized single point incremental forming (SPIF) without backing plate support. An innovative method of thermal and ultrasound assisted deformation of a polymer sheet is proposed using a tool with a sphere mounted in a ring-shaped magnetic holder, the friction of which with the tool holder is reduced by ultrasound, and the heating is performed by a laser. The heated tool moving on the sheet surface locally increases the plasticity of the polyvinyl chloride (PVC) polymer in the contact zone with less deforming force does not reducing the stiffness of the polymer around the tool contact area and eliminating the need for a backing plate. The free 3D rotating ball also changes the slip of the tool on the surface of the polymer sheet by the rolling, thereby improving the surface quality of the product. The finite element method (FEM) allowed the virtual evaluation of the deformation parameters of the SPIF. Significant process parameters were found, and the behavior of the heated polymer sheet was determined.

Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force.

This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models.