Study on Parametric Optimization of Fused Deposition Modelling (FDM) Process

Rapid prototyping (RP) is a generic term for a number of technologies that enable fabrication of physical objects directly from CAD data sources. In contrast to classical methods of manufacturing such as milling and forging which are based on subtractive and formative principles espectively, these processes are based on additive principle for part fabrication. The biggest advantage of RP processes is that an entire 3-D (three-dimensional) consolidated assembly can be fabricated in a single setup without any tooling or human intervention; further, the part fabrication methodology is independent of the mplexity of the part geometry. Due to several advantages, RP has attracted the considerable attention of manufacturing industries to meet the customer demands for incorporating continuous and rapid changes in manufacturing in shortest possible time and gain edge over competitors. Out of all commercially available RP processes, fused deposition modelling (FDM) uses heated thermoplastic filament which are extruded from the tip of nozzle in a prescribed manner in a temperature controlled environment for building the part through a layer by layer deposition method. Simplicity of operation together with the ability to fabricate parts with locally controlled properties resulted in its wide spread application not only for prototyping but also for making functional parts. However, FDM process has its own demerits related with accuracy, surface finish, strength etc. Hence, it is absolutely necessary to understand the shortcomings of the process and identify the controllable factors for improvement of part quality. In this direction, present study focuses on the improvement of part build methodology by properly controlling the process parameters. The thesis deals with various part quality measures such as improvement in dimensional accuracy, minimization of surface roughness, and improvement in mechanical properties measured in terms of tensile, compressive, flexural, impact strength and sliding wear. The understanding generated in this work not only explain the complex build mechanism but also present in detail the influence of processing parameters such as layer thickness, orientation, raster angle, raster width and air gap on studied responses with the help of statistically validated models, microphotographs and non-traditional optimization methods. For improving dimensional accuracy of the part, Taguchi‟s experimental design is adopted and it is found that measured dimension is oversized along the thickness direction and undersized along the length, width and diameter of the hole. It is observed that different factors and interactions control the part dimensions along different directions. Shrinkage of semi molten material extruding out from deposition nozzle is the major cause of part dimension reduction. The oversized dimension is attributed to uneven layer surfaces generation and slicing constraints. For recommending optimal factor setting for improving overall dimension of the part, grey Taguchi method is used. Prediction models based on artificial neural network and fuzzy inference principle are also proposed and compared with Taguchi predictive model. The model based on fuzzy inference system shows better prediction capability in comparison to artificial neural network model. In order to minimize the surface roughness, a process improvement strategy through effective control of process parameters based on central composite design (CCD) is employed. Empirical models relating response and process parameters are developed. The validity of the models is established using analysis of variance (ANOVA) and residual analysis. Experimental results indicate that process parameters and their interactions are different for minimization of roughness in different surfaces. The surface roughness responses along three surfaces are combined into a single response known as multi-response performance index (MPI) using principal component analysis. Bacterial foraging optimisation algorithm (BFOA), a latest evolutionary approach, has been adopted to find out best process parameter setting which maximizes MPI. Assessment of process parameters on mechanical properties viz. tensile, flexural, impact and compressive strength of part fabricated using FDM technology is done using CCD. The effect of each process parameter on mechanical property is analyzed. The major reason for weak strength is attributed to distortion within or between the layers. In actual practice, the parts are subjected to various types of loadings and it is necessary that the fabricated part must withhold more than one type of loading simultaneously.To address this issue, all the studied strengths are combined into a single response known as composite desirability and then optimum parameter setting which will maximize composite desirability is determined using quantum behaved particle swarm optimization (QPSO). Resistance to wear is an important consideration for enhancing service life of functional parts. Hence, present work also focuses on extensive study to understand the effect of process parameters on the sliding wear of test specimen. The study not only provides insight into complex dependency of wear on process parameters but also develop a statistically validated predictive equation. The equation can be used by the process planner for accurate wear prediction in practice. Finally, comparative evaluation of two swarm based optimization methods such as QPSO and BFOA are also presented. It is shown that BFOA, because of its biologically motivated structure, has better exploration and exploitation ability but require more time for convergence as compared to QPSO. The methodology adopted in this study is quite general and can be used for other related or allied processes, especially in multi input, multi output systems. The proposed study can be used by industries like aerospace, automobile and medical for identifying the process capability and further improvement in FDM process or developing new processes based on similar principle

A Comparative Study on Electroplating of FDM Parts

Electroplating on fused deposition modeling parts through two different routes is presented in the study. One route follows the conventional method of electroplating using chromic acid for surface preparation or etching and the other route uses the novel method of electroplating using aluminium charcoal (Al-C) paste for surface preparation. Same plating conditions are used for both the routes employed. The result proposes that instead of shell cracking in few electroplated samples, Al-C route is also capable of producing good copper deposition on FDM samples. Cracks may develop in few samples electroplated through Al-C route, because of dissolution of paste at high operating condition during electroplating. Proper drying of electrolessly plated samples and adaptation of suitable operating condition reduces the risk of electroplated shell cracking

Investment Casting – Rapid Tooling Approach

Investment casting is synonym with producing precision components and investment casters usually take up the order if the volume is huge enough. This is due to high and unjustifiable tooling cost and long lead times associated with the development of metal moulds for producing wax (sacrificial) patterns for customized single casting or small quantity production. One of the feasible solutions is opt rapid tooling. In so- called rapid investment casting the most critical process will be building the investment shell and protecting it from thermal crack. This work investigates the shell crack and counter. In this work pattern are modeled on rapid prototyping machine. During the ceramic shell preparation cracking is reported. Hence a remedy is explored as to control the thermal expansion of the pattern build material-acrylonitrile butadiene styrene (ABS) by using different build styles. Thermal expansion of five distinct styled ABS rapid samples were tested on Dilatometer. The investigation indicates the possibility of 63% reduction in thermal expansion of the pattern, which is very very encouraging

An Investigation on Material Removal Rate of EDM Process: A Response Surface Methodology Approach

In the present work response surface methodology (RSM) based central composite design (CCD) is used for analyzing the electrical discharge machining (EDM) process. For experimentation, mild steel is selected as work piece and copper is used as electrode. Three machining parameters namely current (I), spark on time (Ton) and spark off time (Toff) are selected as the input variables. The output or response chosen is material removal rate (MRR) which is to be maximized. To reduce the number of runs face centered central composite design (FCCCD) was used. ANOVA was used to determine the significance of parameter and interactions. The suitability of model is tested using Anderson darling (AD) plot. The results conclude that different parameters considered i.e. current, pulse on and pulse off time; all have dominant effect on the MRR. At last, the optimized parameter setting for maximizing MRR is found through main effect plot analysis

FEM-Based Simulative Study for Multi-Response Optimization of Powder Bed Fusion Process

Laser powder bed fusion (LPBF) is an additive manufacturing technology which uses a heat source (laser) to sinter or fuse atomized powder particles together. A new layer of powder is spread over the previous layer using a roller, and then the laser power fuses them. This mechanism is repeated until the part model is completed. To reduce the time, effort, and cost, the present study incorporated the design of an experimental approach conjoined with finite element analysis (FEA) to simulate the LPBF process. A three-dimensional (3D) bi-material model was subjected to FEA with variations in temporal and spatial material characteristics. A Gaussian moving heat source model for the multi-scanning of a single layer was developed to understand the effect of process parameters, namely laser power, scan speed, and scan pattern on melt pool dimensions. Although, similar simulation models have been reported in the literature, the majority of these did not consider parametric variations. A few studies adopted multiple parameters which varied simultaneously, but the major limitation of these studies was that most of them did not consider multiple characteristics under a constrained environment. In the present research, the multi-parameter multi-level simulation study was performed to understand the process mechanism with fewer simulations. Results showed that the studied dimensions were sensitive to parameter setting, and that temperature variation within the melt pool was dependant on the material phase in the vicinity of the melt pool. This research proposed that melt pool dimensions must be accurately controlled for optimum process performance to achieve proper overlap between the adjacent scan lines and sufficient depth to complete bonding with the bottom layer. Since the involved criteria were of a conflicting nature, the problem of determining a single factor setting to obtain the desired results was solved using grey relational analysis (GRA). It was found that, among all the considered process parameters, scan velocity was the most significant one. This research recommended a maximum scan velocity i.e., v = 1.5 m/s, with a minimum laser power i.e., P = 80 W. In addition, it was also suggested that low energy density be used to melt the powder layer properly

Application of the Combined ANN and GA for Multi-Response Optimization of Cutting Parameters for the Turning of Glass Fiber-Reinforced Polymer Composites

Glass fiber-reinforced polymer (GFRP) composites find wide applications in automobile, aerospace, aircraft and marine industries due to their attractive properties such as lightness of weight, high strength-to-weight ratio, high stiffness, good dimensional stability and corrosion resistance. Although these materials are required in a wide range of applications, their non-homogeneous and anisotropic properties make their machining troublesome and consequently restrict their use. It is thus important to study not only the machinability of these materials but also to determine optimum cutting parameters to achieve optimum machining performance. The present work focuses on turning of the GFRP composites with an aim to determine the optimal cutting parameters that yield the optimum output responses. The effect of three cutting parameters, i.e., spindle rotational speed (N), feed rate (f) and depth of cut (d) in conjunction with their interactions on three output responses, viz., Material Removal Rate (MRR), Tool Wear Rate (TWR), and Surface roughness (Ra), is studied using full factorial design of experiments (FFDE). The statistical significance of the cutting parameters and their interactions is determined using analysis of variance (ANOVA). To relate the output response and cutting parameters, empirical models are also developed. Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) is employed for multi-response optimization to simultaneously optimize the MRR, TWR and Ra