1,721,094 research outputs found
Artificial Neural Networks for Tool Wear Prediction Based on Sensor Fusion Monitoring of CFRP/CFRP Stack Drilling
No full textAn intelligent sensor monitoring procedure was implemented to monitor the drilling of carbon fiber reinforced
plastic (CFRP)/CFRP stacks used in the assembly of aircraft fuselage panels; the signals from these sensors were then used to develop an artificial neural network-based cognitive paradigm to predict tool wear, which would allow on-line decision making regarding tool replacement. A multiple sensor system, capable of acquiring signals relative to thrust force, torque, and acoustic emission RMS, was employed during experimental drilling tests, under different rotational speed and feed conditions. Advanced sensor signal processing techniques, including signal conditioning and segmentation, as well as statistical feature extraction and data fusion, were implemented on the acquired signals. Selected statistical features extracted from the multiple sensor signals in the time domain were combined via sensor fusion techniques to construct sensor fusion pattern vectors. These were then fed to artificial neural networks for pattern recognition, with the goal of finding correlations which would allow the prediction of the corresponding tool wear. The tool wear prediction performed by the artificial
neural network can be utilized to support decision making at the appropriate time for worn tool replacement,
which is extremely useful for drilling automation, as well as for estimating the quality of the drilled holes
Comparison of drilled hole quality evaluation in CFRP/CFRP stacks using optical and ultrasonic non-destructive inspection
No full textIn aeronautical industry, stringent requirements relate to the quality of drilled holes in carbon fiber reinforced plastic (CFRP) composite laminates as low hole quality determines poor assembly tolerance, structural properties reduction, and risk for long-term part performance. Non-destructive quality control techniques were applied to drilled CFRP laminate stacks for aeronautical applications to characterize the material damage induced by drilling in order to assess the hole quality for product acceptability. Experimental metrology procedures, including optical measurements and ultrasonic non-destructive evaluation, were employed to appraise both external and internal induced material damage in holes machined under diverse drilling conditions. The optical inspection procedure, comparable to the visual inspection method regularly utilized in industry, provided delaminated area evaluations that are underestimated in the case of severe drilling conditions by up to 7% for hole exit and up to 5% for hole entry. In the case of less severe drilling conditions, the underestimation was limited to <2.5% for both hole exit and hole entry, which can be considered a practically negligible disparity
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
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
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
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