1,720,974 research outputs found
Machine learning approaches for real-time process anomaly detection in wire arc additive manufacturing
No full textIn gas metal arc welding (GMAW) processes, including wire arc additive manufacturing (WAAM), machine learning (ML) is emerging as a powerful tool for monitoring both process and product anomalies. However, a significant challenge in real industrial environments is the reliance on large, balanced datasets for training supervised learning models. To address this issue, a shift toward unsupervised learning is gaining attention in this research field, offering the potential to work effectively with small and unbalanced datasets. However, different materials, sensors, and welding technologies have been used in the literature, making complex the comparison of the results. This work fills that gap by presenting a comprehensive comparison of both supervised and unsupervised learning methods. An experimental campaign was conducted on Invar 36 alloy—a material with limited WAAM research—where 15 wall structures were deposited with varying process parameters using the natural dip transfer process, aiming to identify the optimal parameters for this alloy. Data on welding current and voltage were captured, and during the qualification procedure, anomalies were detected, some of which led to product defects. Supervised, unsupervised, and semi-supervised ML approaches, along with a detailed frequency domain analysis of the collected signals, were applied to process the obtained unbalanced dataset. The results provide key insights: while supervised learning models can be applied to anomaly detection in small and unbalanced datasets, they are prone to overfitting, which limits their practical use due to the prevalence of normal cases over anomalies in the dataset, resulting in higher number of missed anomalies. In contrast, unsupervised models, with their lower generalization capability, tend to exhibit higher false alarm rates but better performance to identify anomalous data. This work not only compares in depth these data analytics methodologies but also offers guidance on selecting the appropriate ML algorithm based on specific industrial objectives and provides insights into the printability of Invar 36 for WAAM applications under natural dip transfer process
Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing
No full textNowadays, artificial intelligence (AI) has become a crucial Key Enabling Technology with extensive application in diverse industrial sectors. Recently, considerable focus has been directed towards utilizing AI for the development of optimal control in industrial processes. In particular, reinforcement learning (RL) techniques have made significant advancements, enabling their application to data-driven problem-solving for the control of complex systems. Since industrial manufacturing processes can be treated as MIMO non-linear systems, RL can be used to develop complex data-driven intelligent decision-making or control systems. In this work, the workflow for developing a RL application for industrial manufacturing processes, including reward function setup, development of reduced order models and control policy construction, is addressed, and a new process-based reward function is proposed. To showcase the proposed approach, a case study is developed with reference to a wire arc additive manufacturing (WAAM) process. Based on experimental tests, a Reduced Order Model of the system is obtained and a Deep Deterministic Policy Gradient Controller is trained with aim to produce a simple geometry. Particular attention is given to the sim-to-real process by developing a WAAM simulator which allows to simulate the process in a realistic environment and to generate the code to be deployed on the motion platform controller
Hybrid statistical process monitoring of wire Arc additive manufacturing with frequency informed deep learning
No full textArc welding is classified as a special process under ISO standards, making process monitoring a critical
component of the welding and Additive Manufacturing (AM) certification procedure. Nowadays, the advancements
in data analysis have led to the growing use of Machine Learning (ML) techniques for real-time
weld quality assessment. However, due to their simple design and minimal data requirements, traditional
Statistical Process Monitoring (SPM) methods, such as control charts, remain widely used for evaluating
process quality and detecting anomalies. Despite their significance, traditional SPM techniques struggle
when dealing with multi-variate and high-frequency data typical of Industry 4.0 contexts, making their application
challenging and highlighting the need for new approaches to data analysis. Therefore, in this study,
we propose an innovative hybrid deep learning-based SPM technique for in situ monitoring of theWire Arc
Additive Manufacturing (WAAM) process, with the aim of making SPM more effective in this setting. In
particular, an experimental campaign was conducted using the Invar36 alloy, and an online anomaly detection
application was developed using ML methods to improve the performance of SPM. Specifically, a
Frequency-Informed Convolutional AutoEncoder (FICA) is used as a sensor fusion technique for welding
current and welding voltage data. The obtained latent space across additional temporal dimensions – which
fuse the high-frequency information in a low dimensional space - is then analysed using an Exponentially
Weighted Moving Average (EWMA) chart to detect anomalies during production. The results demonstrate
that the proposed methodology improves anomaly detection performance compared to conventional SPM
techniques, with the F2-score improving from 71.1% to 81.3%
Energy Efficiency Optimisation in Wire arc Additive Manufacturing of Invar 36 Alloy via Intelligent Data-Driven Techniques
No full textNowadays, sustainability of manufacturing processes is a major concern which calls for special efforts to reduce their environmental impact and energy consumption. In additive manufacturing, this issue is even more challenging due to the usually high energy demands of these processes. However, in the era of Industry 4.0, machine learning (ML) techniques, combined with metaheuristic optimization algorithms, offer a powerful solution to explore new, unproven combinations of process parameters that better align with sustainability goals of manufacturing. These methodologies can minimize the need for extensive experimental campaigns and provide a valuable decision-making support tool for goal-oriented process parameters optimization. In line with such approach, this research work introduces an intelligent data-driven methodology using ML to optimize wire arc additive manufacturing (WAAM) of Invar 36 alloy considering both the resulting layer geometry and quality as well as the process energy consumption. An experimental campaign involving WAAM deposition of 15 walls made of Invar 36 alloy using a natural dip transfer welding process was carried out. The data acquired from the WAAM experimental tests were used to develop and train an artificial neural network (ANN) which, on the basis of the process parameters, was able to predict the layer geometry, the specific energy consumption and a specified quality score indicative of the presence of defects. The ANN achieved a high accuracy with 100% F2 score for quality classification, 0.4 mm mean absolute error for layer geometry, and 20 J/mm MAE for specific energy consumption. A genetic algorithm (GA) was then used to identify optimal process parameters able to minimize the specific energy consumption while maintaining quality and smoothness of the deposited layer. The experimental validation carried out using the GA-optimized process parameters in the WAAM process confirmed the reliability of the model, resulting in energy-efficient and defect-free walls
Reinforcement learning as data-driven optimization technique for GMAW process
No full textWelding optimization is a significant task that contributes to enhancing the final welding quality. However, the selection of an optimal combination of various process parameters poses different challenges. The welding geometry and quality are influenced differently by several process parameters, with some exhibiting opposite effects. Consequently, multiple experiments are typically required to obtain an optimal welding procedure specification (WPS), resulting in the waste of material and costs. To address this challenge, we developed a machine learning model that correlates the process parameters with the final bead geometry, utilizing experimental data. Additionally, we employed a reinforcement learning algorithm, namely stochastic policy optimization (SPO), with the aim to solve different optimization tasks. The first task is a setpoint‐based optimization problem that aims to find the process parameters that minimize the amount of deposited material while achieving the desired minimum level of penetration depth. The second task is an optimization problem without setpoint in which the agent aims to maximize the penetration depth and reduce the bead area. The proposed artificial intelligence-based method offers a viable means of reducing the number of experiments necessary to develop a WPS, consequently reducing costs and emissions. Notably, the proposed approach achieves better results with respect to other state-of-art metaheuristic data-driven optimization methods such as genetic algorithm. In particular, the setpoint‐based optimization problem is solved in 8 min and with a final mean percentage absolute error (MPAE) of 2.48% with respect to the 42 min and the final 3.42% of the genetic algorithm. The second optimization problem is also solved in less time, 30 s with respect to 6 min of GA, with a higher final reward of 5.8 from the proposed SPO algorithm with respect to the 3.6 obtained from GA
Frequency informed convolutional autoencoder for in situ anomaly detection in wire arc additive manufacturing
No full textIn the context of Industry 4.0, the importance of anomaly detection is growing, particularly in Additive Manufacturing, as itallows for the detection and localization of defects, thereby reducing waste and costs. However when normal and anomalysignals have similar shapes in time this task is particularly challenging. Despite that, the frequency content of time seriessignals often holds valuable information that, when integrated into the learning process, can greatly improve the recognitionof hidden patterns in the data and enhance feature separability. In this study, we propose an unsupervised anomaly detectiontechnique for Wire Arc Additive Manufacturing (WAAM) based on deep learning, namely 1D-Convolutional AutoEncoder.By integrating frequency-regularization terms based on wavelet analysis of defect-free welding signals during the trainingphase, the results demonstrated a significant 54.8% improvement in anomaly detection performance compared to similarmethods. This improvement enables the effective use of unsupervised learning for anomaly detection in WAAM, minimizingthe need for labeled data and making it suitable for industrial applications, even when dealing with unbalanced datasets
Vision-based defect localisation and automated planning for robotic spray coating systems
No full textThis work presents a novel approach to improving the robotic quality inspection of the spray coating process in the aerospace
industry by integrating computer vision with robotic systems. While spray coating is essential for providing protective and
aesthetic coatings, due to the challenges and complexity of the aerospace industry, it frequently encounters issues such as
incomplete coverage, paint defects, and surface imperfections, which can compromise quality and increase the need for
rework. To address this, a methodology that utilises a cutting-edge computer vision technique based on YOLOv10 for realtime defect localisation is proposed, targeting issues such as uneven thickness and missed areas. Once the camera is calibrated,
the results of defect localisation achieve a multi-class mean Average Precision of 99%. Furthermore, this work presents a
framework that demonstrates how positional information and classification results can be utilised to automatically generate
path planning and control actions for an intelligent spray coating system. This innovation advances the state of knowledge
in the field, which has previously relied only on image classification
Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
Full text linkWelding is one of the most complex industrial processes because it is challenging to model, control, and inspect. In particular, the quality inspection process is critical because it is a complex and time-consuming activity. This research aims to propose a system of online inspection of the quality of the welded items with gas metal arc welding (GMAW) technology through the use of neural networks to speed up the inspection process. In particular, following experimental tests, the deviations of the welding parameters—such as current, voltage, and welding speed—from the Welding Procedure Specification was used to train a fully connected deep neural network, once labels have been obtained for each weld seam of a multi-pass welding procedure through non-destructive testing, which made it possible to find a correspondence between welding defects (e.g., porosity, lack of penetrations, etc.) and process parameters. The final results have shown an accuracy greater than 93% in defects classification and an inference time of less than 150 ms, which allow us to use this method for real-time purposes. Furthermore in this work networks were trained to reach a smaller false positive rate for the classification task on test data, to reduce the presence of faulty parts among non-defective parts
Improving the Interpretability of Data-Driven Models for Additive Manufacturing Processes Using Clusterwise Regression
No full textWire Arc Additive Manufacturing (WAAM) represents a disruptive technology in the field of metal additive manufacturing. Understanding the relationship between input factors and layer geometry is crucial for studying the process comprehensively and developing various industrial applications such as slicing software and feedforward controllers. Statistical tools such as clustering and multivariate polynomial regression provide methods for exploring the influence of input factors on the final product. These tools facilitate application development by helping to establish interpretable models that engineers can use to grasp the underlying physical phenomena without resorting to complex physical models. In this study, an experimental campaign was conducted to print steel components using WAAM technology. Advanced statistical methods were employed for mathematical modeling of the process. The results obtained using linear regression, polynomial regression, and a neural network optimized using the Tree-structured Parzen Estimator (TPE) were compared. To enhance performance while maintaining the interpretability of regression models, clusterwise regression was introduced as an alternative modeling technique along with multivariate polynomial regression. The results showed that the proposed approach achieved results comparable to neural network modeling, with a Mean Absolute Error (MAE) of 0.25 mm for layer height and 0.68 mm for layer width compared to 0.23 mm and 0.69 mm with the neural network. Notably, this approach preserves the interpretability of the models; a further discussion on this topic is presented as well
Utilising unsupervised machine learning and IoT for cost-effective anomaly detection in multi-layer wire arc additive manufacturing
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