Anosov M.S., Shatagin D.A., Baevsky A.A., Poduvaltsev A.A., Arkhipov M.V., Yamgulin R.I. Development of an Integrated Approach to the Diagnostics of Welding and Wire Arc Additive Manufacturing Processes

DOI: 10.18503/1995-2732-2026-24-3-156-163

Abstract

Problem Statement (Relevance). The paper addresses the pressing issue of ensuring the stability and quality of the wire arc additive manufacturing (WAAM) process, which limits its widespread implementation in critical manufacturing applications. Conventional non-destructive testing methods, applied after the completion of the process, do not allow timely process adjustment. Objectives. The study is aimed at developing an integrated diagnostic approach based on in-situ monitoring for predictive process control. For this purpose, a method for synchronous multiparametric recording of key physical fields has been proposed, including electrical arc characteristics (voltage and current) and acoustic emission (AE) signals. During the experimental studies, wall-shaped specimens have been deposited while the corresponding data have been recorded simultaneously. The analysis has included the construction of spectrograms and phase portraits (energy-acoustic attractors), as well as the evaluation of their fractal dimensions to identify indicators of process stability and defect formation. Originality. The novelty of the study lies in the integration of multiparametric data and the application of deep neural network models for real-time data analysis. A comparative analysis of various architectures, including convolutional and recurrent neural networks, has been performed to classify process states based on raw and processed signals. The best result, with an accuracy of 91%, has been achieved by a bidirectional recurrent neural network (BiLSTM) using pre-calculated nonlinear signal parameters (fractal dimension and spectral entropy) as key features. This enables the model to effectively identify subtle patterns preceding the loss of process stability. Practical Relevance. The study provides a basis for an intelligent predictive control system capable of forecasting process instability within fractions of a second. This paves the way for closed-loop adaptive control systems, which are critical for the industrial implementation of WAAM, enabling both quality control and real-time optimization of process parameters. Further development will involve integrating the trained model into the process control system and validating the proposed approach across a wider range of materials and deposition parameters.

Keywords

wire arc additive manufacturing (WAAM), predictive control, in-situ monitoring, acoustic emission, deep learning, fractal analysis, dynamic stability, nonlinear dynamics, multiparametric diagnostics, recurrent neural network (RNN), phase portrait, intelligent control, deposition quality.

For citation

Anosov M.S., Shatagin D.A., Baevsky A.A., Poduvaltsev A.A., Arkhipov M.V., Yamgulin R.I. Development of an Integrated Approach to the Diagnostics of Welding and Wire Arc Additive Manufacturing Processes. Vestnik Magnitogorskogo Gosudarstvennogo Tekhnicheskogo Universiteta im. G.I. Nosova [Vestnik of Nosov Magnitogorsk State Technical University]. 2026, vol. 24, no. 3, pp. 156-163. https://doi.org/10.18503/1995-2732-2026-24-3-156-163

Maxim S. Anosov – PhD (Eng.), Associate Professor, Deputy Director of Institute of Industrial Technologies of Mechanical Engineering, Nizhny Novgorod State Technical University named after R.E. Alekseev, Nizhny Novgorod, Russia. ORCID 0000-0001-8150-9332.

Dmitry A. Shatagin – PhD (Eng.), Associate Professor, Head of the Department of Technology and Equipment of Mechanical Engineering, Nizhny Novgorod State Technical University named after R.E. Alekseev, Nizhny Novgorod, Russia. ORCID 0000-0003-1293-4487

Anatoly A. Baevsky – Senior Lecturer of Institute of Industrial Technologies of Mechanical Engineering, Nizhny Novgorod State Technical University named after R.E. Alekseev, Nizhny Novgorod, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.. ORCID 0009-0003-1648-5276

Alexey A. Poduvaltsev – Postgraduate Student, Nizhny Novgorod State Technical University named after R.E. Alekseev, Nizhny Novgorod, Russia. ORCID 0000-0003-4285-8980

Maxim V. Arkhipov – Postgraduate Student, Nizhny Novgorod State Technical University named after R.E. Alekseev, Nizhny Novgorod, Russia.

Roman I. Yamgulin – Postgraduate Student, Nizhny Novgorod State Technical University named after R.E. Alekseev, Nizhny Novgorod, Russia. ORCID 0009-0009-5868-5579

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