2,867 research outputs found
Advanced technologies in drilling of light alloys and CFRP hybrid stacks for airframe structure manufacturing in the aerospace industry
No full textBrevetto N° IT9029505 (U1) ― 1991-07-18 "PRESIDIO CHIRURGICO RADIOTRASPARENTE PER IL TRATTAMENTO DI PATOLOGIE E TRAUMI ORTOPEDICI"
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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
Characterization of a new dry drill-milling process of Carbon Fibre Reinforced Polymer laminates
No full textCarbon Fibre Reinforced Polymer (CFRP) composites are widely used in aerospace applications that require severe quality parameters. To simplify the assembly operations and reduce the associated costs, the current trend in industry is to optimize the drilling processes. However, the machining of CFRP composites is very challenging compared with metals, and several defect types can be generated by drilling. The emerging process of orbital drilling can greatly reduce the defects associated with the traditional drilling of CFRP, but it is a more complex process requiring careful process parameters selection and it does not allow for the complete elimination of the thrust force responsible for delamination damage. As an alternative to traditional and orbital drilling, this work presents a new hole making process, where the hole is realized by a combination of drilling and peripheral milling performed using the same cutting tool following a novel tool path strategy. An original tool design principle is proposed to realize a new drill-milling tool, made of a first drilling and a subsequent milling portion. Two different tool configurations are experimentally tested to evaluate the performance of the newly-conceived combined drill-milling process. This process is quick and easy, and the experimental results show an improvement in the drilled hole quality
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