Abstract
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.
Highlights
The forces exerted by the friction between the tool and the workpiece during the machining of materials play an important role in the quality of the product
The surface roughness results obtained were compared with the single point incremental forming (SPIF) results of lubricated surfaces reported in [1], which showed that lubricants have poor oxidative stability, which leads to changes in the physical properties of the lubricant, such as viscosity, acidity, etc
The K-Nearest Neighbors algorithm (KNN) and Support Vector Machine (SVM) models based on input three provide higher RMSE values, with an increase of 1.939% and 13.67%, respectively, three provide higher RMSE values, with an increase of 1.939% and 13.67%, respectively, compared to the five-input
Summary
The forces exerted by the friction between the tool and the workpiece during the machining of materials play an important role in the quality of the product. The effect of petroleum and vegetable oil-based lubricants on the friction coefficient, wear, forming forces, and surface roughness of metal sheets produced by the single point incremental forming (SPIF) process has been investigated in [1]. Lubricating oil was found to produce the surface roughness in the direction perpendicular to which the tool passes, with a value close to Ra = 1.45 μm, which exceeds the surface roughness of the undeformed sheet while degrading the quality of the final product. The authors in [2] present an ML algorithm-based method for predicting the occurrence of defects in the SPIF process of metal sheets due to material properties and sources of dispersion of process parameters. ANN models can be trained and developed for the final evaluation of processes and tools prior to production [4]
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