FEM-ANN Sequential Modelling of Laser Transmission Welding for Prediction of Weld Pool Dimensions

In this chapter, a sequential modeling approach has been applied for modeling of laser transmission welding process using finite element method (FEM) and artificial neural network (ANN) technique to predict the weld pool dimensions in a shorter time frame. The scripting language, APDL (ANSYS® Parametric Design Language), is used to develop the three-dimensional FE model. During preprocessing, all the major physical phenomena of laser transmission welding process are incorporated into the model physics. Based on the temperature field predicted by the model, the weld pool dimensions (i.e., weld width and weld penetration depth) are calculated. The weld dimensions predicted by the developed FE model are further used for training a neural network model. It is found from the results of test data sets that the developed ANN model can predict the outputs with significant accuracy and takes less prediction time, which in turn saves time, cost, and the efforts for performing experiments.

Finite Element Analysis of Tool Wear in Hot Machining Process

Super alloys have been used widely in all sectors (e.g., automobile, aerospace, biomedical, etc.) for their properties like high hardness, high wear, and corrosion resistance. A central challenge is the significantly higher temperature and pressure on the cutting tool, hence rapid tool wear and bad surface finish. In the present study, a FEM analysis has been developed to calculate the effect of preheating temperature on the surface of the workpiece on tool wear on machining Inconel 718. Usui's tool wear model has been implemented in DEFORM software. In order to validate the results, an experimental investigation has been carried out with same cutting conditions. The evaluated results were also compared with the room temperature machining condition. It was observed that the heating temperature increased the tool life by reducing tool wear, tool temperature compared to room temperature machining condition. The predicted tool wear, tool temperature, and chip morphology have been compared with the experimental results and good correlation was found.

Modeling and Optimization of Ultrasonic Machining Process Using a Novel Evolutionary Algorithm

Ultrasonic machining (USM) is one of the non-conventional techniques for machining of hard and brittle materials like glass, ceramics, and ceramic matrix composites. The objective of the study includes the investigation of material removal rate (MRR), hole oversize (HOS), and circularity of holes (COH) during USM of soda lime glass and finding out the optimal parametric condition by an evolutionary algorithm. Taguchi philosophy was employed to carry out experiments using the process parameters such as power rating, abrasive slurry concentration, and static load. A novel optimization algorithm called imperialist competitive algorithm (ICA) was used to obtain maximum MRR and minimum HOS and COH. This algorithm is inspired by the imperialistic competition and has several advantages over other evolutionary algorithms like its simplicity, less computational time, and accuracy in predicting the results. The Technique for order of preference by similarity to ideal solution (TOPSIS) is utilized to convert these multiple performance characteristics to a single response. Moreover, the prediction outcomes of this TOPSIS integrated ICA methodology demonstrates excellent conformity with the experimental values and can be applied to solve complex problems.

Optimization of Surface Roughness Parameters by Different Multi-Response Optimization Techniques During Electro-Discharge Machining of Titanium Alloy

In this chapter, the EDM process is performed by taking titanium alloy as work piece and AlSiMg prepared by selective laser sintering (SLS) process as tool electrode along with copper and graphite. The EDM is performed by varying different process parameters like voltage (V), discharge current (Ip), duty cycle (τ), and pulse-on-time (Ton). The surface roughness parameters like Ra, Rt, and Rz are measured by the use of surface roughness measurement machine. To reduce the number of experiments, design of experiment (DOE) approach like Taguchi's L27 orthogonal array has been used. The surface properties of the EDM specimen are optimized by desirability function approach, TOPSIS and VIKOR method, and the best parametric setting is reported for the EDM process. All the optimization techniques convergence to the same optimal parametric setting. The type of tool is the most significant parameter followed by discharge current and voltage. Better surface finish of EDM specimen is produced with lower level of parametric setting along with the use of AlSiMg RP electrode during EDM.

Evaluation of Electrical Discharge Machining Performance on Al (6351)–SiC–B4C Composite

Electrical discharge machining (EDM) process is a non-conventional machining process used for the material which are difficult to machine. In this research work, an attempt has been made to determine the influence of Boron Carbide (B4C) particles on the machinablity of the Al (6351) alloy reinforced with 5 wt. % Silicon Carbide (SiC) Metal Matrix Composite (MMC) through EDM. Influence of machining parameters such as pulse current (I), pulse on time (Ton), duty factor (τ), and gap voltage (V) on affecting the output performance characteristics namely Electrode Wear Ratio (EWR), Surface Roughness (SR) and Power Consumption (PC) which are studied. The result shows that the addition of B4C particles significantly affects the machinablity of the composite, with a contribution of 1.6% on EWR, 3.5% on SR and 19.8% on PC. The crater, recast layer formation, and Heat Affected Zone (HAZ) in the machined surface of the composite are also reported in detail.

Non-Conventional Technologies Selection

Micro-engineering is nowadays a key industrial area with applications is a wide range of products and sectors. The need for a multiplicity of products fostered the development of several processes and combinations of processes in the world of micro-engineering. There are different feasible alternatives to produce the same kind of product. The manufacturing cost is usually the decision factor to select the best alternative among them. But cost is affected by dozens of factors and if not properly modelled causes controversy in so complex decisions. In this chapter, the application of process-based cost modelling is proposed as the engine to identify the best performance spaces for each alternative, using its potential for sensitive analysis of uncertain and/or critical parameters. To illustrate the approach, a case study is developed analyzing four alternatives for the production of a light diffuser with micro-features imbibed, involving micro-injection molding, hot-embossing, micromachining, and powder-injection molding.

An Investigation Into Non-Conventional Machining of Metal Matrix Composites

One of the recently developing fields is that of non-traditional machining of particle reinforced metal matrix composites. The complexity associated with traditional machining of particle reinforced metal matrix composite is very high, and therefore, the researchers have begun to show more focus towards non-traditional machining. In the present work, the investigation has been carried out for non-traditional machining such as laser beam machining, electro-chemical machining, abrasive water jet machining, and electro-chemical discharge. Material removal rate, surface finish, and the mechanism of machining has been studied for each of the aforementioned processes. The main material removal mechanisms as has been identified are melting, mechanical erosion, vaporization, and chemical dissolution. The investigation reveals that the major reasons for the damage of the machined surface are the presence of reinforcement particles and thermal degradation.

Recent Developments in Wire Electrical Discharge Machining

The tremendous growth of manufacturing industries and desired need of accuracy and precision has put a great importance on non-traditional machining processes. Metal and non-metals having properties like high strength, toughness, and hardness is generally machined by non-conventional machining methods. One of earliest non-traditional machining that is still in use and being effectively utilized in industries is wire electrical discharge machine. This machining technique gives a tough line of competition to conventional machining process like milling, grinding, broaching, etc. Cutting intricate and delicate shapes with accuracy and precision gives this machining technique an edge over other conventional machining and non-conventional machining processes. This chapter provides an insight to various research and prominent work done in field of WEDM by various scientists, researchers, and academicians. The chapter also emphasizes various advantages and disadvantages of different modelling and optimization methods used. The chapter concludes with some recommendations about trends for future WEDM researchers.

Optimization of Process Parameters for Silicon Carbide Powder Mixed EDM

The present chapter deals with the investigations on the effect of process parameters like powder concentration (Cp), peak current (Ip), pulse-on-time (Ton), duty cycle (DC), and gap voltage (Vg) on output responses like material removal rate (MRR), tool wear rate (TWR), electrode wear ratio (EWR), surface roughness (SR), recast layer thickness (RLT), and micro-hardness (HVN) for PMEDM of H-11 hot work tool steel. Multi-objective optimization using grey relational analysis (GRA) has been implemented to identify the optimum set of input parameters to achieve maximum MRR and HVN with minimum TWR, EWR, SR, and RLT at the same time. Predicted results on verification with confirmation tests improve the preference values by 0.09468 with GRA. The recommended settings of process parameters is found to be Cp=6g/l, Ip=3Amp, Ton=100µs, DC=70%, and Vg=30V from GRA. The microstructures were examined with scanning electron microscope (SEM) to find the presence of surface deformities and identify alterations on the surface in comparison to the base material.

Performance and Surface Evaluation Characteristics on Cryogenic-Assisted Abrasive Water Jet Machining of AISI D2 Steel

The chapter reports on the investigation of cryogenic-assisted abrasive water jet (CAAWJ) machining of AISI D2 steel with varying the jet impact angles and abrasive mesh sizes. The performance measurement is considered in this study such as depth of penetration and taper ratio. Also, the surface integrity characteristics are considered in the present study such as abrasive contamination, surface topography, XRD peaks, residual stress, and micro hardness. The CAAWJ machining process improves the performance measurement such as higher depth of penetration and lower taper ratio for the machining of D2 steel. Also, the CAAWJ cut surface consists of better surface integrity features over the AWJ cut surface. The phase transformation effect of target material under cryogenic cooling helps to turn the mode of the material removal mechanism from ductile to brittle erosion process and yield a better performance. The results also indicate that the oblique jet impact angles have been produced better performance characteristics than the jet impact angle of 90o at room temperature.