Alessandra Caggiano

Assessment of laser-based reverse engineering systems for tangible cultural heritage conservation

The process of acquiring the geometry and shape of a part and reconstructing its digital model is known as reverse engineering (RE). This approach is usefully employed in fields as diverse as product design, design modification, geometrical inspection, worn or damaged parts repair or remanufacturing, when physical object drawings, documentation or computer models are not available. The recent scientific and technical developments of RE methods and tools have broadened the possibilities of applications in the field of cultural heritage conservation ranging from reproduction (e.g. via rapid prototyping), maintenance (e.g. computer-aided repair), multimedia tools for education and dissemination (e.g. virtual museums), to artefact condition monitoring (e.g. computer-aided inspection) and many more. The first stage of the RE procedure is digital data acquisition that can be carried out by means of several different tools. The selection of the 3D digitising system is crucial as it directly affects the process time and the quality of the point cloud, which determines the final digital model. In this research work, following the EC FP7 open topic on ‘Equipment assessment for laser based applications’ compiled in Horizon 2020, two non-contact laser-based RE systems, respectively, based on a coordinate measuring machine and a portable 3D scanning equipment, are utilised for the digitisation and reconstruction of a free-form tangible cultural heritage artefact to comparatively assess the RE systems performance in terms of process time, accuracy and ease of use.

Machining of Fibre Reinforced Plastic Composite Materials

Fibre 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.

Elucidating Grinding Mechanism by Theoretical and Experimental Investigations

Grinding is one of the essential manufacturing processes for producing brittle or hard materials-based precision parts (e.g., optical lenses). In grinding, a grinding wheel removes the desired amount of material by passing the same area on the workpiece surface multiple times. How the topography of a workpiece surface evolves with these passes is thus an important research issue, which has not yet been addressed elaborately. The present paper tackles this issue from both the theoretical and the experimental points of view. In particular, this paper presents the results of experimental and theoretical investigations on the multi-pass surface grinding operations where the workpiece surface is made of glass and the grinding wheel consists of cBN abrasive grains. Both investigations confirm that a great deal of stochasticity is involved in the grinding mechanism, and the complexity of the workpiece surface gradually increases along with the number of passes.

Digital factory technologies for robotic automation and enhanced manufacturing cell design

Abstract 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.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

Machining 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.

Investigation of Laser Welding of Ti Alloys for Cognitive Process Parameters Selection

Laser welding of titanium alloys is attracting increasing interest as an alternative to traditional joining techniques for industrial applications, with particular reference to the aerospace sector, where welded assemblies allow for the reduction of the buy-to-fly ratio, compared to other traditional mechanical joining techniques. In this research work, an investigation on laser welding of Ti–6Al–4V alloy plates is carried out through an experimental testing campaign, under different process conditions, in order to perform a characterization of the produced weld bead geometry, with the final aim of developing a cognitive methodology able to support decision-making about the selection of the suitable laser welding process parameters. The methodology is based on the employment of artificial neural networks able to identify correlations between the laser welding process parameters, with particular reference to the laser power, welding speed and defocusing distance, and the weld bead geometric features, on the basis of the collected experimental data.

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning

Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace.

Characterization of a New Dry Drill-Milling Process of Carbon Fibre Reinforced Polymer Laminates

Carbon 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.

Comparison of drilled hole quality evaluation in CFRP/CFRP stacks using optical and ultrasonic non-destructive inspection

Abstract In 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.