Giuseppe Casalino

AN ANN AND TAGUCHI ALGORITHMS INTEGRATED APPROACH TO THE OPTIMIZATION OF CO2 LASER WELDING

Abstract Nowadays several numerical methods are widely used for either modelling or optimizing the performance of the manufacturing technologies. That has been advanced due to the large diffusion of the personal computer and the numerical algorithms. The knowledge of those methods and the ability in integrating their functions can make both the manufacturing engineer and the researcher ace their duties. In this paper, two of those methods have been employed, the backpropagation artificial neural network and the Taguchi approach to the design of the experiment. They were applied to find out the optimum levels of the welding speed, the laser power and the focal position for CO2 keyhole laser welding of medium carbon steel butt weld. The optimal solution is valid in the ranges of the welding parameters that were used for training the neural networks. Extrapolation over those limits would restrict the applicability of the found solution. The proposed approach would be extendable to other keyhole laser welding processes for different materials and joint geometries.

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.

Analysis and Comparison of Friction Stir Welding and Laser Assisted Friction Stir Welding of Aluminum Alloy

Friction Stir Welding (FSW) is a solid-state joining process; i.e., no melting occurs. The welding process is promoted by the rotation and translation of an axis-symmetric non-consumable tool along the weld centerline. Thus, the FSW process is performed at much lower temperatures than conventional fusion welding, nevertheless it has some disadvantages. Laser Assisted Friction Stir Welding (LAFSW) is a combination in which the FSW is the dominant welding process and the laser pre-heats the weld. In this work FSW and LAFSW tests were conducted on 6 mm thick 5754H111 aluminum alloy plates in butt joint configuration. LAFSW is studied firstly to demonstrate the weldability of aluminum alloy using that technique. Secondly, process parameters, such as laser power and temperature gradient are investigated in order to evaluate changes in microstructure, micro-hardness, residual stress, and tensile properties. Once the possibility to achieve sound weld using LAFSW is demonstrated, it will be possible to explore the benefits for tool wear, higher welding speeds, and lower clamping force.

A model for evaluation of laser welding efficiency and quality using an artificial neural network and fuzzy logic

Abstract For any welding process, efficiency and quality strongly depend on the energy input, which is the energy introduced per unit length of weld from a travelling heat source. The focused laser beam is one of the highest power density sources available to the welding industry today, which makes it possible to weld with very low energy input with respect to most of other welding processes. In this paper a number of stainless steel butt joints were produced by laser irradiation. The welding efficiencies were calculated as the melted volume-energy input ratio. Moreover, the weld crown and depth were measured in order to evaluate the joint quality. The collected data were interpolated and correlated to the process parameters using an artificial neural network. They were then clustered using a fuzzy C-means algorithm. During the training stage of the neural network algorithm, the design of experiment (DOE) technique was used for the selection of the optimized network parameters. In practice, using some artificial intelligence, a model was built to choose the most suitable laser welding process for producing high efficiency and good quality, and is now available for supporting design and research.

Deformation prediction and quality evaluation of the gas metal arc welding butt weld

Abstract The control of distortion and the overall quality are the main targets in the design and manufacturing of sound welds. In this paper a numerical approach is presented in order to support the choice of process parameters that can minimize thermal deformation and evaluate weld quality for gas metal arc welding (GMAW) operating with the short-circuiting transfer mode. This numerical approach is based on the integration of artificial intelligence (Al) techniques and the finite element method (FEM). The information to train the Al and to validate the FEM came from experimental trials. Firstly, a number of simple artificial neural networks were trained and validated. They linked the process parameters to the geometry of the molten zone of the welds. In this way it was possible to calculate the geometries throughout the range of the process parameters. Thereafter, the finite element model provided useful information about the residual stress and the shrinkage distortion of the welds. Finally, a concise evaluation of joint quality was possible using a fuzzy C-means clustering algorithm. The ‘minimum is the best’ rule was applied during the training phase. The numerical model for GMAW was constructed and validated for butt welds of thin plates made of mild steel.

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...

Characterization of Thermo-Mechanical and Fracture Behaviors of Thermoplastic Polymers

In this paper the effects of the strain rate on the inelastic behavior and the self-heating under load conditions are presented for polymeric materials, such as polymethyl methacrylate (PMMA), polycarbonate (PC), and polyamide (PA66). By a torsion test, it was established that the shear yield stress behavior of PMMA, PC, and PA66 is well-described by the Ree-Eyring theory in the range of the considered strain rates. During the investigation, the surface temperature was monitored using an infrared camera. The heat release appeared at the early stage of the deformation and increased with the strain and strain rate. This suggested that the external work of deformation was dissipated into heat so the torsion tests could not be considered isothermal. Eventually, the effect of the strain rate on the failure modes was analyzed by scanning electron microscopy.

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.

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.

Study of a fiber laser assisted friction stir welding process

Friction stir welding is a relatively new joining technique. This technique, which is considered a derivative of the more common friction welding method, was developed mainly for aluminum and its alloys. In recent years, this method has been used to join various other alloys. FSW has many advantages, including the following: the welding procedure is relatively simple with no consumables or filler metal; joint edge preparation is not needed; oxide removal prior to welding is unnecessary; high joint strength has been achieved in aluminum and magnesium alloys; FSW can be used with alloys that cannot be fusion welded due to crack sensitivity. The drawbacks of FSW include the need for powerful fixtures to clamp the workpiece to the welding table, the high force needed to move the welding tool forward, the relatively high wear rate of the welding tool, and weld speeds in FSW are slower, which can lead to longer process times. To overcome these drawbacks, a fiber laser-assisted friction stir welding system was designed (FLAFSW). The system combined a conventional commercial friction machine and a fiber pumped laser system. The scope is to investigate the influence of the laser assistance on the weld quality. A number of different aluminum plates, which are still mentioned to be difficult to be joint as intermetallic phases appear during melting welding techniques, were used. The evaluation of quality was performed through analysis of appearance, mechanical and microstructure characterization of the weld.