1,721,052 research outputs found
Machining of Fibre Reinforced Plastic Composite Materials
No full textFibre reinforced plastic composite materials are difficult to machine because of the anisotropy and inhomogeneity characterizing their microstructure and the abrasiveness of their reinforcement components. During machining, very rapid cutting tool wear development is experienced, and surface integrity damage is often produced in the machined parts. An accurate selection of the proper tool and machining conditions is therefore required, taking into account that the phenomena responsible for material removal in cutting of fibre reinforced plastic composite materials are fundamentally different from those of conventional metals and their alloys. To date, composite materials are increasingly used in several manufacturing sectors, such as the aerospace and automotive industry, and several research efforts have been spent to improve their machining processes. In the present review, the key issues that are concerning the machining of fibre reinforced plastic composite materials are discussed with reference to the main recent research works in the field, while considering both conventional and unconventional machining processes and reporting the more recent research achievements. For the different machining processes, the main results characterizing the recent research works and the trends for process developments are presented
Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition
No full textMachining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values
Cloud-based manufacturing process monitoring for smart diagnosis services
No full textA cloud-based manufacturing process monitoring framework for online smart diagnosis services has been developed with the aim of performing tool condition monitoring during machining of difficult-to-machine materials. The proposed architecture allows to share process monitoring tasks between different resources, which can be geographically dislocated and managed by actors with different competences and functions. Distributed resources with enhanced computation and data storage capability allow to improve the efficiency of tool condition diagnosis and enable more robust decision-making, exploiting large information and knowledge sharing. Diagnosis on tool conditions is offered as a cloud service, using an architecture where the computing resources in the cloud are connected to the physical manufacturing system realising a complex cyber-physical system using sensor and network communication. Based on sensorial data acquired at the factory level, smart online diagnosis on consumed tool life and tool breakage occurrence is carried out through knowledge-based
algorithms and cognitive pattern recognition paradigms. On the basis of the cloud diagnosis, the local
server activates the proper corrective action to be taken, such as tool replacement, process halting or
parameters change, sending the right command to the machine tool control
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
CBN Grinding Performance Improvement in Aircraft Engine Components Manufacture
No full textAbstractCubic Boron Nitride (CBN) grinding is extensively employed in the aerospace industry as it allows to effectively grind high performance aerospace metal alloys such as nickel base superalloys. With reference to a real industrial case of CBN grinding for the manufacture of aircraft engine components, the aim of this paper is to improve the CBN wheel tool life and optimize the grinding process while taking into account economic, environmental and social sustainability issues. Different types of CBN grinding wheels fabricated using diverse deposition procedures are tested to compare their behavior during grinding of Ni base superalloys and assess their tool life in terms of number of parts successfully ground by a single wheel. Tests are also performed to investigate alternative coolant type applicability as well as grain size influence on surface integrity as these factors can significantly affect process performance, final workpiece quality and grinding wheel life
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
Improving the performance of a real manufacturing cell through advanced digital simulation
No full textDecision Support Based on Digital Twin Simulation: A case study in Distillery Industry
No full textThis paper presents a complete digital twin framework with the purpose to aid manufacturing ergonomics and control operations in distillery industry. The focus is on the design of a digital twin framework that manages the material flow in the real system based on ergonomics and resource efficient parameters of the manual operations. Preliminary experiments are done by applying the digital twin framework on a lab-scale case study and demonstrate the applicability of the proposed approach
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