Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning

Aug 5, 2026

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Laser wire directed energy deposition (LW-DED) additive manufacturing targets high-throughput, near-net shape production, yet defects arise from stochastic process drifts when parameter control is insufficient. This study performs process mapping by demarcating processing regimes from parameters and process monitoring by detecting instabilities using in-situ meltpool imaging. Single-track experiments span 128 combinations of laser power, scanning velocity, and linear mass density, yielding stable, dripping, stubbing, and incomplete melting regimes. Interpretable meltpool features train lightweight machine learning that classifies regimes with fidelity near 90% (F1-score), closely matching deep image-based models.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Objectives and motivation

Process states and meltpool imaging overview

Methodology

Experimental design and parameter space

Process mapping with high-speed imaging

Process monitoring with process-aware machine learning

Results and benchmarking

Lightweight feature-based model performance

Comparison with black-box deep learning

Materials & Design 245 (2024) 113281 Contents lists available at ScienceDirect Materials & Design journal [homepage:](homepage: www.elsevier.com/locate/matdes)[ www.elsevier.com/locate/matdes](homepage: www.elsevier.com/locate/matdes) | Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning
Anis Assada,b,f, Benjamin D. Bevansa, Willem Potter c, Prahalada Rao a,g, Denis Cormier d, Fernando Deschamps b, Jakob D. Hamilton c,*, Iris V. Riveroe
a Grado Department of Industrial and Systems Engineering, Virginia Polytechnical Institute and State University (Virginia Tech), Blacksburg, VA, USA b Department of Industrial and Systems Engineering, Pontifical Catholic University of Parana, Curitiba, PR, Brazil
c Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA d Department of Industrial and Systems Engineering, Rochester Institute of Tech., Rochester, NY, USA e Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USAf Department of Technology and Innovation, University of Southern Denmark, Sønderborg, Denmark
g Mechanical Engineering, Virginia Polytechnical Institute and State University (Virginia Tech), Blacksburg, VA, USA | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
LW-DED process mapping Process stability
Meltpool imaging
Process-aware machine learning Deep learning | | This work concerns the laser wire directed energy deposition (LW-DED) additive manufacturing process. The objectives were two-fold: (1) process mapping – demarcating the process states as a function of the processing parameters; and (2) process monitoring – detecting process anomalies (instabilities) using data acquired from an in-situ meltpool imaging sensor. The LW-DED process enables high-throughput, near-net shape manufacturing. Without rigorous parameter control, however, LW-DED often introduces defects due to stochastic process drifts. To enhance scalability and reliability, it is essential to understand how LW-DED parameters affect processing regimes, and detect deleterious process drifts. In this work, single-track experiments were conducted over 128 combinations of laser power, scanning velocity, and linear mass density. Four process states were observed via high-speed imaging and delineated as stable, dripping, stubbing, and incomplete melting regimes. Physically intuitive meltpool features were used to train simple machine learning models for classifying the process state into one of the four regimes. The approach was benchmarked against computationally intense, black-box deep machine learning models that directly use as-received meltpool images. Using only six intuitive meltpool morphology and intensity signatures, the approach classified the LW-DED process state with statistical fidelity approaching 90 %(F1-score) compared to F1-score 87 % for deep learning models. | 1. Introduction 1.1. Objectives and motivation This work concerns the laser wire directed energy deposition (LWDED) metal additive manufacturing process [1]. The objectives were two-fold: (1) process mapping – demarcating the process state as a function of the processing parameters; and (2) process monitoring – detecting process anomalies (instabilities) using data acquired from an in-situ meltpool imaging sensor. A schematic of the LW-DED process is shown in Fig. 1(a). In LW-DED, material in the form of metal wire is melted using energy from a laser and deposited layer-upon-layer. The relative movement of the wire, laser beam, and build plate provided by a machine tool or robot enables creation of three-dimensional, geometrically complex, large volume parts [2]. The LW-DED process is one of a family of directed energy deposition (DED) additive manufacturing processes [3]. Other DED-based processes include, laser powder direc

FAQs

What are the two main objectives of the study on LW-DED?

The study aims to (1) map LW-DED process states as a function of processing parameters and (2) monitor the process by detecting anomalies or instabilities using in-situ meltpool imaging data.

How many experimental parameter combinations were tested and what variables were varied?

Single-track experiments were conducted across 128 combinations, varying laser power, scanning velocity, and linear mass density.

Which process regimes were identified and how were they classified?

Four regimes were observed: stable, dripping, stubbing, and incomplete melting. Regimes were classified by training machine learning models on physically intuitive meltpool morphology and intensity signatures.

Aug 5, 2026

Upload by Oliver