Antonio Domenico Ludovico

3D Finite Element Analysis in the selective laser melting process

Selective Laser Melting (SLM) is actually the most attractive technique in an Additive Manufacturing (AM) technology because of the possibility to build layer by layer up nearly full density metallic components without needing for post-processing. One of the main problems in SLM processes is represented by the thermal distortion of the model during forming; the part tends to be deformed and cracked due to the thermal stress. Therefore, it is important to know the effect of the process parameters on the molten zone and consequently on the density of the consolidated material. Great advantage can be obtained from the prediction of temperature evolution and distribution. The aim of this study is to evaluate the influence of the process parameters on the temperature evolution in a 3D model. The developed code evaluates the distribution and evolution of the temperatures in the SLM process and simulates the powder-liquid-solid change by means of a check of the nodes temperature. (Received in June 2010, accepted in June 2011. This paper was with the authors 1 month for 1 revision.)

Capabilities and Performances of the Selective Laser Melting Process

The current market is in a phase of accelerated process of change, that leads companies to innovate in new techniques or technologies to respond as quickly as possible to the everchanging aspects of the global environment. The economy of a country is heavily dependent on new and innovative products with very short development time. Companies, currently, have considerable success, only if they develop the ability to respond quickly to changing of customer needs and to use new innovative technologies. In this context, the companies that can offer a greater variety of new products with higher performance resulting in advantage over the other. At the heart of this environment there is a new generation of customers, who forced organizations to research new technologies and techniques to improve business processes and accelerate product development cycle. As a direct result, factories are forced to apply a new philosophy of engineering as the Rapid Response to Manufacturing (RRM). The concept of the RRM uses products previously designed to support the development of new products. The RRM environment was developed by integrating the various technologies, such as CAD-based modelling, the knowledge-based engineering for integrated product and process management and the direct production concepts. Direct production uses prototyping, tooling and rapid manufacturing technologies to quickly test the design and build the part (Cherng et al., 1998). Among RRM technologies, Rapid (RP) and Virtual (VP) Prototyping are revolutionizing the way in which artefacts are designed. Rapid Prototyping (RP) technologies embraces a wide range of processes for producing parts directly from CAD models, with little need for human intervention; so, designers can produce real prototypes, even very complex, in a simple and efficient way, allowing them to check the assembly and functionality of the design, minimizing errors, product development costs and lead times (Waterman & Dickens, 1994). The SLS technology was developed, like other RP technologies, to provide a prototyping technique to decrease the time and cost of the product cycle design. It consists of building a three dimensional object layer by layer selectively sintering or partial melting a powder bed by laser radiation.

Manufacturing and Characterization of Ti6Al4V Lattice Components Manufactured by Selective Laser Melting

The paper investigates the fabrication of Selective Laser Melting (SLM) titanium alloy Ti6Al4V micro-lattice structures for the production of lightweight components. Specifically, the pillar textile unit cell is used as base lattice structure and alternative lattice topologies including reinforcing vertical bars are also considered. Detailed characterizations of dimensional accuracy, surface roughness, and micro-hardness are performed. In addition, compression tests are carried out in order to evaluate the mechanical strength and the energy absorbed per unit mass of the lattice truss specimens made by SLM. The built structures have a relative density ranging between 0.2234 and 0.5822. An optimization procedure is implemented via the method of Taguchi to identify the optimal geometric configuration which maximizes peak strength and energy absorbed per unit mass.

On the numerical modelling of the multiphysics self piercing riveting process based on the finite element technique

The development of reliable numerical models permits to investigate the manufacturing processes with very low incremental costs or prototyping efforts hence it provides a relevant help in process optimisation and gives great opportunity for making maximum use of sparse process data [Shercliff HR, Lovatt AM. Selection of manufacturing process in design and the role of process modelling. Prog Mater Sci 2001;46:429-59]. Among others the metal forming processes have heavily benefited from the finite element numerical computing technology [Chenot JL, Massoni E. Finite element modelling and control of new metal forming processes. Int J Machine Tool Manuf 2006;46:1194-200]. The self piercing riveting (SPR) is a cold forming process which creates a strong mechanical interlock between two or more sheets by means of a semi-tubular rivet, which, pressed by a punch, pierces the upper sheet and flares into the bottom one. It is governed by complex multiphysics phenomena whose governing equations can be resolved using the finite element method. In this paper all the governing equations are fully reported along with the mathematics of the resolving method needed for setting up and simulate a finite element model of the self piercing riveting of an aluminium alloy. A case study of the SPR of two sheets of the 6060T4 aluminium alloy using a steel rivet was investigated. The calculations were performed using the LsDyna finite element commercial code. The problems encountered and the solutions applied for the preparation of the model and the run of the calculation were presented and discussed. The obtained results were validated by comparison with data coming from a laboratory experiment.

An investigation of rapid prototyping of sand casting molds by selective laser sintering

Among the layer fabrication techniques, selective laser sintering (SLS), is widely used for manufacturing various products made of different materials (i.e., polycarbonates, nylons, polyamides, sand casting, metal powders, and others). The SLS of precoated foundry sands allows the aggregation of adjacent particles, which are then cemented by furnace heat treatment. Moreover, geometrically complex molds and cores not obtainable with conventional methods can be realized by this method. In this article, the optimization of laser parameters is reported for fabricating transitory molds for foundry applications. Lasercron sand, a quartz sand with a thin phenolic resin coating often used for SLS applications, was tested. The CO2 and diode lasers were used for this study. After some preliminary tests, experimental design techniques were applied to investigate the influence of some processing parameters, i.e., laser power, scan speed, and scan spacing (hatch). Their interactions were evaluated using response surfa...

Parameter selection by an artificial neural network for a laser bending process

Abstract Based on thermally induced plastic deformations produced by laser irradiation, metal sheet laser bending can be a valid alternative to dies for rapid prototyping and manufacturing. Some numerical models have been built in order to improve the understanding and prediction of mechanisms. Drawbacks entailed with those models have been found. Finite element model simulation has proved to be time and CPU (central processing unit) memory consuming. The analytical models have been cumbersome and unsatisfactory. Nowadays, it is possible to build a neural network model for process modelling directly from data collected during the experiments. In this paper a feed-forward neural network with a back-propagation learning function has been designed and its performances have been evaluated for metal sheet laser bending. This technique has proved to be effective and efficient, providing the process parameters that are necessary to achieve a desired bending angle.

Manufacturing of 18 Ni Marage 300 Steel Samples by Selective Laser Melting

Selective Laser Sintering (SLS), has become one of the most popular technique in the layer manufacturing processes because of the ability to build complex geometries models with a wide range of materials. Recently, the interest in SLS is mainly focused into metals because of the possibility of producing models not only for the prototyping step but also as functional parts. Driven by the need to process nearly full dense objects, with mechanical properties comparable to those of bulk materials and by the desire to avoid long post processing cycles, Selective Laser Melting (SLM) has been developed. SLM represents an evolution of the SLS process: in the first one the complete melting of powder occurs rather than sintering or partial melting of the second one. SLM, is mainly suitable to produce tools and inserts with internal undercuts and channels for conformal cooling for injection molding. A careful control of the parameters which influence the melting and the amount of energy density involved in the process is necessary to get parts with optimized quality. The aim of this paper was to study the effect of the main process parameters (laser power, scan speed, scan spacing, hatch spacing, scanning strategy) and of thermal treatments on the quality of built parts in terms of hardness, density, microstructure, and mechanical properties. The 18 Ni Marage 300 steel, one of the most used materials in the die industry was investigated, using a Nd:YAG laser with a maximum power of 100W.

Monitoring of the friction stir welding process by means of thermography

Abstract This work is a study of the thermal behaviour of aluminium alloy 5754-H111 sheets welded with the friction stir welding (FSW) process. In particular, the feasibility of infrared thermography for monitoring of the FSW process is presented. This process has different advantages compared to those of traditional welding, such as very low welding temperature and low mechanical distortion. Usually in the literature, destructive tests are carried out to evaluate the quality of joints, but this approach is time-consuming and off-line. Results have shown that the thermal behaviour of joints is correlated to process parameters and that thermography can be used to perform the online monitoring of the FSW process.

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

Optimization of Ni-Based WC/Co/Cr Composite Coatings Produced by Multilayer Laser Cladding

As a surface coating technique, laser cladding (LC) has been developed for improving wear, corrosion, and fatigue properties of mechanical components. The main advantage of this process is the capability of introducing hard particles such as SiC, TiC, and WC as reinforcements in the metallic matrix such as Ni-based alloy, Co-based alloy, and Fe-based alloy to form ceramic-metal composite coatings, which have very high hardness and good wear resistance. In this paper, Ni-based alloy (Colmonoy 227-F) and Tungsten Carbides/Cobalt/Chromium (WC/Co/Cr) composite coatings were fabricated by the multilayer laser cladding technique (MLC). An optimization procedure was implemented to obtain the combination of process parameters that minimizes the porosity and produces good adhesion to a stainless steel substrate. The optimization procedure was worked out with a mathematical model that was supported by an experimental analysis, which studied the shape of the clad track generated by melting coaxially fed powders with a laser. Microstructural and microhardness analysis completed the set of test performed on the coatings.