# A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network
###### Abstract  
This study presents an innovative methodology for preform design in metal forging processes based on the convolution neural network (CNN) algorithm. The proposed approach extracts the features of inputted forging product geometries and utilizes them to derive the corresponding preform shapes by employing weight arrays (filters) determined during the convolutional operations. The filters are progressively updated during the training process, emulating the learning steps of a process engineer responsible for the design of preform shapes for the forging processes. The design system is composed of multiple three-dimensional (3D) CNN sub-models, which can automatically derive individual 3D preform design candidates. It also implies that the 3D surfaces of preforms are easily acquired, which is important for the forging industry. The proposed preform design methodology was validated by applying it to two-dimensional (2D) axisymmetric shapes, one-quarter plane-symmetric 3D shapes, and two other industrial cases. In all the considered cases, the design methodology achieved substantial reductions in the forging load without forging defects, proving its reliability and effectiveness for application in metal forging processes. forging process; preform design; convolution neural network (CNN); CNN sub-models 202117948[[https://doi.org/10.3390/app1117948](https://doi.org/10.3390/app1117948)]([https://doi.org/10.3390/app1117948](https://doi.org/10.3390/app1117948))  
Academic Editor: [PERSON]  
Received: 5 July 2021  
Accepted: 25 August 2021  
Published: 28 August 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **  
**Key words:**  
* **2021** _Applied_  
method is user-biased. Moreover, a designed preform is also influenced by the number of iterations and the loading path, which are not included in the design requirements. [PERSON] and [PERSON] [14] introduced a linear sensitivity analysis (LSA)-based preform design method for axisymmetric forging. LSA approaches consider the change in the node position and compute the finite-dimensional gradients of the objective functions. [PERSON] et al. [15] designed a preform for H-shaped forging considering the sensitivities of the objective functions and nodal velocities. However, because the governing equations in LSA approaches are strongly related to the forging shape, they become overcomplicated in the case of complex geometries. More recently, [PERSON] et al. [16] applied evolutionary structural optimization (ESO) by considering progressive element elimination during the procedure and applied it to the preform design of H-shaped geometry. [PERSON] et al. [17] and [PERSON] et al. [18] considered a bidirectional ESO method in which the elements eliminated in a certain step could be recovered in subsequent steps. However, this ESO method also requires an initial guess for the preform geometry, which becomes user-biased and is affected by aleatoric uncertainty. To suggest a preform design shape without any initial design guess, [PERSON] et al. [19] utilized an artificial neural network (ANN) algorithm for H-shape forging. The ANN-based model utilizes the weight vectors and links the values of the forging geometry image to those of the preform image in a one-to-one relationship. [PERSON] et al. [20] also applied the ANN method to a cylindrical billet upsetting process to reduce the barreling at the free surface of the workpiece. However, as the ANN method assigns the weight considering a one-to-one relationship for the user-defined training data, this method yields a poor level of detectable details and becomes impracticable for untrained complex forging shapes. In contrast, a convolutional neural network (CNN) algorithm can extract the geometrical features of input data arrays through convolutional operations with weight arrays (filters) [21]. As this algorithm does not require a one-to-one link between data, it has been widely employed in several engineering problems [22; 23].