Showing papers by "Pierpaolo Carlone published in 2018"

Multi-scale modeling and online monitoring of resin flow through dual-scale textiles in liquid composite molding processes

Abstract: This paper discusses a multi-scale approach for the simulation of preform impregnation and a dielectric flow monitoring system in liquid composite molding processes. A mesoscale unit cell was built based on image analysis of the microstructure of manufactured composite laminates. Bulk permeability and saturation rate were computed at the mesoscale and then introduced in the macroscale model modifying the governing mass and momentum equations. The model was used to simulate a unidirectional infusion test, in order to compare numerical results with experimental data from pressure measurements. Moreover, a non-invasive dielectric monitoring system for unsaturated and saturated flow tracking was developed. The good agreement exhibited by the numerical and experimental results points out the capability of the multiscale model as well as of the dielectric monitoring system.

A Semi-Analytical Model to Predict Infusion Time and Reinforcement Thickness in VARTM and SCRIMP Processes.

Abstract: In liquid composite molding processes, such as resin transfer molding (RTM) and vacuum assisted resin transfer molding (VARTM), the resin is drawn through fiber preforms in a closed mold by an induced pressure gradient. Unlike the RTM, where a rigid mold is employed, in VARTM, a flexible bag is commonly used as the upper-half mold. In this case, fabric deformation can take place during the impregnation process as the resin pressure inside the preform changes, resulting in continuous variations of reinforcement thickness, porosity, and permeability. The proper approach to simulate the resin flow, therefore, requires coupling deformation and pressure field making the process modeling more complex and computationally demanding. The present work proposes an efficient methodology to add the effects of the preform compaction on the resin flow when a deformable porous media is considered. The developed methodology was also applied in the case of Seeman's Composite Resin Infusion Molding Process (SCRIMP). Numerical outcomes highlighted that preform compaction significantly affects the resin flow and the filling time. In particular, the more compliant the preform, the more time is required to complete the impregnation. On the other hand, in the case of SCRIMP, the results pointed out that the resin flow is mainly ruled by the high permeability network.

Strain and temperature measurement in pultrusion processes by fiber Bragg grating sensors

Abstract: Injection Pultrusion (IP) is one of the most effective processes, in terms of productivity and costs, to manufacture fiber reinforced polymers. In IP roving of fiber are driven through an injection chamber in which they are impregnated by the resin and then formed in a shaped die. The die is heated in order to cure the resin. Pultruded products are in most cases characterized by constant cross-section profile, whereas unidirectional long fibers are mainly used as reinforcing material. Two relevant phenomena occur within the injection chamber and the heated die, namely the impregnation of the fibers and the polymerization of the resin. Furthermore, thermal expansion, resin chemical shrinkage and the interaction between the die and the impregnated fibers strongly influence the process [1]. Clearly, thermal and mechanical fields significantly impact on these strictly chained behaviours. The use of thermocouples to evaluate temperature within pultrusion die is already widespread, but they are not capable to acquire any information concerning stress-strain levels. In the present work Fibers Bragg Gratings (FBG) sensors were used to measure thermal and strain profiles in selected material location within the injection chamber and the curing die. Being the differences among the spectres transmitted and received are related to the variations in both temperature and strain, commercial FBG sensors were opportunely modified and calibrated. The optical fibers were hooked to the fibers entering into the injection pultrusion die. Taking the pulling speed into account, each waveform acquired was correlated to a position within the die. Obtained data highlight the effect of the heat generation due to resin reaction as well as longitudinal strains related to the pulling force, the thermal expansion and the chemical shrinkage of the resin system.

Thermo-Mechanical Finite Element Modeling of the Laser Treatment of Titanium Cold-Sprayed Coatings

Abstract: This paper implements a thermo-mechanical model to simulate the laser treatment effects on a cold-sprayed titanium coating and aluminum substrate. The thermo-mechanical finite element model considers the transient temperature field due to the laser source and applied boundary conditions, using them as input loads for the subsequent stress-strain analysis. Numerical outcomes highlighted the relevance of thermal gradients and the presence of thermally-induced stress-strain fields responsible for promoting damage in the coating.

Advances in Titanium on Aluminium Alloys Cold Spray Coatings

Abstract: Cold gas dynamic spraying (CGDS) is an emerging technique that involves the surface modification in order to provide enhanced surface properties on material substrates. Particles, with size in the range of 1–50 μm, are accelerated by a supersonic jet gas up to 1200 m/s and impact on the substrate surface. Under specific conditions, the metal powders undergo a severe plastic deformation and adhere to the substrate. In the last decades, the cold spraying of several materials, like copper, aluminium and iron, has been widely explored providing optimal processing windows for a wide range of material pairs. Titanium and its alloys are finding a widespread use in many strategic industries, namely, aeronautic and aerospace field, due to the lightweight, high corrosion resistance and compatibility with polymer-reinforced composites, as well as in the biomedical sector, due to their biocompatibility. However, the high cost of raw materials and the manufacturing issues put severe restrictions to their wider use. On the other hand, replacement of titanium bulk with multilayer material, consisting in a cold sprayed titanium coating on aluminium components, could be a promising alternative and an advantageous trade-off between the cost compression and the higher surface properties of titanium alloy. The present chapter deals with the analysis of the deposition of pure titanium coatings on aluminium alloy substrate by means of low-pressure cold gas spray technique and deals also with the study of the properties of multilayer material. A post-deposition process to further improve the properties of the coating itself was also analysed.

Artificial Neural Networks in Advanced Thermoset Matrix Composite Manufacturing

Abstract: Autoclave curing is a common practice to manufacture high temperature thermoset matrix composites. The cycle design and optimization of the temperature-time curve is a key issue for a competitive production. In this paper artificial neural networks (ANN), as a technique of artificial intelligence, were used for prediction of the composite temperature profile during the autoclave curing process. Different neural network models have been investigated regarding their capabilities for prediction of the composite temperature profile. The new neural network model has been developed able to predict the composite temperature profile in the wide range of manufacturing conditions changing.

Thermo-mechanical modeling of laser treatment on titanium cold-spray coatings

Abstract: Titanium coatings are very attractive to several industrial fields, especially aeronautics, due to the enhanced corrosion resistance and wear properties as well as improved compatibility with carbon fiber reinforced plastic (CFRP) materials. Cold sprayed titanium coatings, among the others deposition processes, are finding a widespread use in high performance applications, whereas post-deposition treatments are often used to modify the microstructure of the cold-sprayed layer. Laser treatments allow one to noticeably increase the superficial properties of titanium coatings when the process parameters are properly set. On the other hand, the high heat input required to melt titanium particles may result in excessive temperature increase even in the substrate. This paper introduces a thermo-mechanical model to simulate the laser treatment effects on a cold sprayed titanium coating as well as the aluminium substrate. The proposed thermo-mechanical finite element model considers the transient temperature field due to the laser source and applied boundary conditions using them as input loads for the subsequent stress-strain analysis. Numerical outcomes highlighted the relevance of thermal gradients and thermally induced stresses and strains in promoting the damage of the coating.

Process control for polymeric composite manufacture

Abstract: Polymer matrix composite materials (PMC) are increasingly used for advanced applications imposing high quality standards, as in the aerospace industry. In the processing route for PMC manufacturing, curing process is the most critical step. Indeed, during cure, several defects may arise, namely resin degradation, void growth, development of residual stress, and distortion. Therefore, sophisticated instruments are required to control the manufacturing process of fiber-reinforced plastic parts. The cure degree of matrix is generally monitored by indirect methods, for instance measuring the temperature inside the laminate, or by direct methods, exploiting the evolution of different properties of resin throughout curing process. This chapter discusses the techniques and equipment necessary for controlling a PMC manufacturing process, with particular reference to the curing step. First of all, the issues that can arise during the curing process are presented, then design and optimization methodologies for curing processes are introduced and explained, focusing the attention on process optimization and control. Then, several sensing systems for in-process cure monitoring are presented, such as optical fiber, elastic wave propagation sensing, dielectric sensing, and microelectromechanical system. Finally, process control strategies, architectures and tools, such as the FEM simulation, the artificial neural network, the fuzzy control, the function approximation, and the feature extraction are described.