Cloud manufacturing architecture for part quality assessment

In this work, a cloud manufacturing architecture aimed at offering on-demand services for part quality assessment is presented and demonstrated with reference to an aeronautical industry application. The developed architecture is based on a three-level structure and considers two non-contact metrological procedures to be integrated via cloud service: laser-based 3D metrology and ultrasonic non-destructive inspection. The combination of these two techniques allows to measure part features and detect possible defects associated with the outer part geometry as well as the inner material structure. The data coming from the two metrological procedures and pre-processed at fog level are sent to the cloud that performs their integration with the aim to allow for the 3D visualization and manipulation of the heterogeneous metrological data into a single-user interface for the holistic part quality evaluation. The validation of the cloud manufacturing architecture for part quality assessment is performed on a composite material component employed in the aeronautical industry. Through the cloud platform, the heterogeneous data from the two non-contact metrological techniques are integrated, and the newly developed user interface allows for the simultaneous visualization and analysis of the 3D metrology and ultrasonic information for detecting geometrical defects and internal flaws of the inspected component

Non-Destructive Testing of Low-Velocity Impacted Composite Material Laminates through Ultrasonic Inspection Methods

Low-velocity impact damages in composite material laminates, such as matrix cracks, delaminations and fibre breakage, usually develop inside the material and can be difficult to detect. As these flaws downgrade the structural integrity of the composite, the thorough damage evaluation is essential to assess the impact damage criticality. This chapter focuses on the ultrasonic non-destructive inspection of low-velocity impacted composite laminates for damage estimation and assessment. The impact damage generation mechanisms are described and characterised. Ultrasonic testing methods and their defect detection capabilities are illustrated. Recent research studies on ultrasonic non-destructive evaluation of low-velocity impacted composite materials are presented and discussed

quality assurance of brazed copper plates through advanced ultrasonic nde

Abstract Ultrasonic non-destructive methods have demonstrated great potential for the detection of flaws in a material under examination. In particular, discontinuities produced by welding, brazing, and soldering are regularly inspected through ultrasonic techniques. In this paper, an advanced ultrasonic non-destructive evaluation technique is applied for the quality control of brazed copper cells in order to realize an accelerometer prototype for cancer proton therapy. The cells are composed of two half-plates, made of high conductivity 99.99% pure copper, brazed one on top of the other. Full volume ultrasonic scanning based on the pulse-echo immersion testing method were carried out to allow for the ultrasonic 2.5 D axial tomography of the cell, realizing the quality assessment of the brazing process

Ultrasonic evaluation of induction heat treatment applied to thermoplastic matrix CFRP

Abstract Thermoplastic matrix carbon fibre reinforced polymers (CFRP) are extensively utilized for composites structures in the aerospace and aeronautical industries. Diverse techniques were currently applied to joining composite parts, the most promising method is the induction heat treatment. In this paper, experimental tests were performed on thermoplastic matrix CFRP specimens by varying the induction heat treatment parameters: power, frequency and current. An advanced ultrasonic (UT) non-destructive evaluation based on pulse-echo technique was employed for the investigation of the utilized process parameters through the UT evaluation of the process induced damage and its depth along the thickness of the thermoplastic matrix CFRP laminates

Tool Condition Monitoring in Composite Materials Machining through Neural Network Processing of Acoustic Emission

By their nature, composite materials are non-homogeneous, anisotropic, and reinforced with abrasive components. Because of their structure and component properties, composite materials are much more difficult to machine than metal alloys and fall under the category of difficult-to-machine-materials. The composite material workpiece can easily suffer intolerable damage during cutting and the tool wear rate can turn out to be unacceptably high. As the available knowledge on the machining of ductile metals is not suitable for composite materials, the need is felt for getting a deeper understanding of the mechanisms governing chip formation and tool wear development in these new materials. In this paper, tool condition monitoring during machining of the most common composite types, i.e. glass and carbon fibre reinforced polymer matrix composites, is carried out through acoustic emission based sensor monitoring, advanced sensor signal processing and neural network data analysis

Applications of Intelligent Sensor Monitoring for Machining Processes

Sensor-based systems for the monitoring of machining operations are becoming more commonplace in order to enhance productivity and performance in today’s manufacturing systems. Several methods have been proposed for process and tool condition monitoring in machining and a number of these are successfully employed in industrial applications. This paper aims at assessing the motivations for the application of sensor monitoring in conventional and innovative cutting operations, the advancements in sensor signal analysis and data processing, and the implementation of intelligent sensors and monitoring systems characterized by augmented decision making capabilities. To this purpose, applications of advanced intelligent sensor monitoring in diverse machining fields will be presented and critically discussed

Sensor Fusion of Acoustic Emission and Cutting Force for Tool Wear Monitoring during Composite Materials Machining

Sensor fusion for tool wear monitoring during machining of different plastic matrix fibre reinforced composite materials was carried out through the detection and analysis of cutting force and acoustic emission signals. Orthogonal cutting tests were performed using two different tool states: fresh and worn. Decision making on tool condition was achieved through a supervised neural network approach, using diverse back-propagation feed-forward neural network configurations, trained and testing with sensor fusion feature vectors