Machine learning approaches for predicting geometric and mechanical characteristics for single P420 laser beads clad onto an AISI 1018 substrate

Bita Mohajernia,R Jill Urbanic,Alireza Pasha,Seyedeh Elnaz Mirazimzadeh

doi:10.1007/s00170-021-08155-3

Bita Mohajernia, R Jill Urbanic + Show 2 more

Open Access

https://doi.org/10.1007/s00170-021-08155-3

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Abstract

The final mechanical and physical properties should be predicted in tandem with the bead geometry characteristics for effective additive manufacturing (AM) solutions for processes such as directed energy deposition. Experimental approaches to investigate the final geometry and the mechanical properties are costly, and simulation solutions are time-consuming. Alternative artificial intelligent (AI) systems are explored as they are a powerful approach to predict such properties. In the present study, the geometrical properties as well as the mechanical properties (residual stress and hardness) for single bead clads are investigated. Experimental data is used to calibrate multi-physics finite element models, and both data sets are used to seed the AI models. The adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward back-propagation artificial neural network (ANN) system are utilized to explore their effectiveness in the 1D (discrete values), 2D (bead cross-sections), and 3D (complete bead) domains. The prediction results are evaluated using the mean relative error measure. The ANFIS predictions are more precise than those from the ANN for the 1D and 2D domains, but the ANN had less error for the 3D scenario. These models are capable of predicting the geometrical and the mechanical properties values very well, including capturing the mechanical properties in transient regions; however, this research should be extended for multi-bead scenarios before a conclusive “best approach” strategy can be determined.

Highlights

1.1 Additive Manufacturing Additive manufacturing is the process of building parts from a computer-aided design (CAD) model by successively adding material layer by layer, realizing the part with minimal excess material
The directed energy deposition method, which is the focus of this research, is one of the metallic additive manufacturing processes where a machine tool or a robot with a deposition nozzle traverses around an object and deposits metal powder onto existing surfaces
A multi-perspective analysis has been performed by using the Artificial Neural Networks (ANN) and the Adaptive Neuro Fuzzy Inference System (ANFIS) models to predict geometric and mechanical properties

Summary

Introduction

1.1 Additive Manufacturing Additive manufacturing is the process of building parts from a computer-aided design (CAD) model by successively adding material layer by layer, realizing the part with minimal excess material. A heat source is applied to melt or cure the raw materials as they are being formed into the final component shape. Conventional fabrication methods for objects by removing material via milling or other machining processes introduces much waste, but there is no significant heat introduced into the process. There are seven main categories of AM technologies including vat photopolymerization, material jetting, binder jetting, material extrusion, powder bed fusion, directed energy deposition, and sheet lamination [1]. The directed energy deposition method, which is the focus of this research, is one of the metallic additive manufacturing processes where a machine tool or a robot with a deposition nozzle traverses around an object and deposits metal powder onto existing surfaces. Material is melted using a laser, electron beam or plasma arc upon deposition [1]

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