3D Digital Simulation for Material Damage Mechanism Identification in a Railway Carriage Pressure Vessel

Digital simulation approaches applied to railway engineering allow to investigate different railway scenarios via 3D digital twins of real objects, motion simulation, and collision detection to identify the root causes of critical damage and estimate the most likely sources of railway accidents. In this work, a digital simulation approach is applied to a real catastrophic train accident in which a railway carriage carrying a pressure vessel collided with an obstacle that generated a cut in the pressure vessel casing. This cut initiated a liquefied petroleum gas leakage that expanded in the environment and caused the explosion and blaze responsible for human casualties. Traditional railway accident reconstruction procedures identified two potential objects accountable for the cutting of the pressure vessel casing: a wing rail and a track reference stake. Based on digital terrain models and reconstructed models of the railway carriage, 3D digital simulation scenarios were created to detect every possible collision of the pressure vessel with the infrastructure environment and investigate whether the shape of the cut in the pressure vessel wall fits the damage visible on the obstacles and whether the interference between obstacle and pressure vessel wall could generate the chip through an interaction similar to metal cutting

digital factory technologies for robotic automation and enhanced manufacturing cell design

The fourth industrial revolution is characterised by the increased use of digital tools, allowing for the virtual representation of a real production environment at different levels, from the entire production plant to a single machine or a specific process or operation. In this framework, Digital Factory technologies, based on the employment of digital modelling and simulation tools, can be used for short-term analysis and validation of production control strategies or for medium term production planning or production system design/redesign. In this research work, a Digital Factory methodology is proposed to support the enhancement of an existing manufacturing cell for the fabrication of aircraft engine turbine vanes via robotic automation of its deburring station. To configure and verify the correct layout of the upgraded manufacturing cell with the aim to increase its performance in terms of resource utilization and throughput time, 3D Motion Simulation and Discrete Event Simulation are jointly employed for the modeling and simulation of different cell settings for proper layout configuration, safe motion planning and resource utilization improvement. Validation of the simulation model is carried out by collecting actual data from the physical reconfigured manufacturing cell and comparing these data to the model forecast with the aim to adapt the digital model accordingly to closely represent the physical manufacturing system

Hierarchical cluster analysis for pattern recognition of process conditions in die sinking EDM process monitoring

Abstract Die sinking EDM processes require continuous monitoring due to the typically severe application requirements, especially in advanced aerospace parts machining, where part quality and machining time are main concerns. As the process conditions cannot be recognized based on the behaviour of a single monitored value, it is necessary to consider a number of relevant sensor signals together. The aim of this research work is to recognize the machining conditions which lead to an improper process performance, e.g. by increasing machining time and causing unacceptable part quality, and to highlight the most relevant sensorial features. Using the Real Time Acquisition (RTAQ) module installed on an AgieCharmilles FORM P 600 sinker spark erosion machine, eight process parameters are acquired. Hierarchical cluster analysis is then applied to identify different groups of improper process conditions based on relevant features extracted from the EDM process parameters

Drilling of Fiber-Reinforced Composite Materials for Aeronautical Assembly Processes

Composite materials such as fiber-reinforced plastics (FRP) are increasingly employed in the aeronautical industry, where the reduction of aircraft weight is essential to meet environmental and cost requirements related to lower emissions and fuel consumption. Due to structural requirements, aeronautical assembly processes on FRP components are based on the wide use of mechanical joints such as rivets. As the latter require a former hole making process, drilling is extensively applied to FRP composites in the aeronautical industry. The main challenges in FRP composite drilling are related to rapid tool wear and damage generation which affects material integrity and surface quality, with particular reference to delamination damage generation. In this chapter, case studies of drilling of CFRP/CFRP stacks for aeronautical assembly are presented to investigate and discuss the influence of drilling parameters, tool type and geometry on tool wear development, hole quality and surface integrity, and the opportunity to implement advanced sensor monitoring procedures for tool condition monitoring based on the acquisition and processing of thrust force and torque signals

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

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

A deep learning based-decision support tool for solution recommendation in cloud manufacturing platforms

Abstract Industry 4.0 key enabling technologies such as cloud manufacturing allow for the dynamic sharing of distributed resources for efficient use at industrial network level. Interconnected users, i.e. suppliers and customers, offer and request manufacturing services over a cloud manufacturing platform, where an intelligent engine generates a number of solutions based on functional and geometrical requirements. A high number of suppliers leads to a higher number of solutions available for customers increasing the decision-making complexity from a customer perspective. Recommendation systems play a crucial role in expanding the opportunities in decision-making processes under complex information environments. In this scope, this paper proposes the conceptualization and the development of a recommendation decision support tool to be implemented in a cloud manufacturing platform to assist customers in appropriately selecting manufacturing services with reference to sheet metal cutting operations. In terms of solution selection, a Deep Neural Network (DNN) paradigm is adopted to allow for the automatic learning of optimal solution recommendation list based both on customers past experiences and new choices. In this respect, a virtual interaction environment is firstly built for system pre-training. Subsequently, users' data are inputted in the pre-trained model to predict a recommendation list. This is then subject to user interaction, i.e. selection, which will be fed back into the model to update the training parameters. This paper concludes with a simulated case study reported to exemplify the proposed methodology for a variety of decision-making scenarios

Optimal cutting parameters and tool geometry in drilling of CFRP/CFRP stack laminates for aeronautical applications

Abstract Drilling stands out as the most widespread machining process of carbon fibre reinforced plastic (CFRP) composite parts, primarily in the aerospace industry due to the extensive use of mechanical assembly using fasteners such as rivets or bolts. In this paper, drilling of CFRP/CFRP stacks for aeronautical applications is investigated using two different types of drilling tools, a traditional twist drill and an innovative step drill, under different spindle speed and feed rate conditions to evaluate the optimal drilling parameters and the most suitable drill bit geometry for one-shot stack drilling. Automatic image processing is applied to evaluate the hole quality parameters and the relationship between tool wear and hole quality is studied for both tool types

Dimensionality Reduction of Sensorial Features by Principal Component Analysis for ANN Machine Learning in Tool Condition Monitoring of CFRP Drilling

Abstract With the aim to perform sensor monitoring of tool conditions in drilling of stacks made of two carbon fiber reinforced plastic (CFRP) laminates, a machine learning procedure based on the acquisition and processing of thrust force, torque, acoustic emission and vibration sensor signals during drilling is developed. From the acquired sensor signals, multiple sensorial features are extracted to feed artificial neural network-based machine learning paradigms, and an advanced feature extraction methodology based on Principal Component Analysis (PCA) is implemented to decrease the dimensionality of sensorial features via linear projection of the original features into a new space. By feeding artificial neural networks with the PCA features, the diagnosis of tool flank wear is accurately carried out

Resource Efficiency Optimization Engine in Smart Production Networks via Intelligent Cloud Manufacturing Platforms

Abstract The aim of this paper is to develop an optimization engine to be implemented in a cloud manufacturing platform to promote resource efficiency in sharing of manufacturing services related to sheet metal cutting. The optimization engine allows to properly select the manufacturing service requests collected through the cloud platform, analyse the possible pairings with the supplier ongoing production orders and dynamically choose the best production strategy (e.g. incorporate, queue, prioritize or reject) considering the surface utilization rate of the metal sheets as key performance index. A simulation of different possible scenarios in terms of customer and supplier orders is reported to exemplify the diverse decision-making scheduling strategies proposed by the platform and the related quantification of resource efficiency improvement