An experimental investigation of surface roughness in 3D-printed PLA items using design of experiments

This work investigates the effect of 3D-printing parameters on surface roughness in polylactic acid printed material by adopting Taguchi's design of experiments approach. The control parameters under study were: number of shells, printing temperature, infill rate, and printing pattern. As the response, mean surface roughness (Ra) was selected. The control parameters were assigned to an L9 orthogonal array to organize the experiments and obtain the mean surface roughness results. It is concluded that printing temperature is the dominant parameter that affects surface roughness when it comes to 3D printing of polylactic acid material followed by printing pattern, infill rate, and the number of shells.

Microscopic analysis on dimensional capability of fused filament fabrication three-dimensional printing process

Fused Filament Fabrication (FFF) has been the most widely used three-dimensional printing (3DP) technology due to its cost-effectiveness, easy application, and material readiness. FFF, to date, has been used to fabricate polymer components for rapid prototyping and increasingly for some end-user applications. Thus, there is a pressing need to optimize 3DP process parameters for FFF materials to achieve higher dimensional accuracy, especially in functional components for final use applications. Therefore, to ensure desired geometries with reasonable accuracy, precise measurements are required to validate the FFF process’s dimensional capability under different process conditions. This study presents the dimensional measurement and statistical analysis to evaluate the effect of printing materials, speed, and layer heights on dimensional accuracy and repeatability of the commercial FFF process. A benchmark part model was designed with different external and internal features commonly used in manufacturing processes. Taguchi’s design of experiments (DOE) was employed to obtain the experiments scheme, followed by the 3DP, dimensional measurement, and analysis of 3DP samples. Results revealed polylactic acid (PLA) material provided better dimensional control in most of the features. Higher printing speeds and layer heights were found optimum for external features/protrusions, whereas lower-to-medium speeds and layer heights were more appropriate for the fabrication of internal features.

Optimization of TIG Process Parameter in Improving Mechanical Properties of S304 Stainless Steel Using a Grey Based Taguchi Method

Tungsten inert gas welding is popular known welding technique for ferrous & nonferrous. Stainless steel grade 3HQ (S30430) is a specialized wire grade with very wide usage for manufacturer of stainless steel fastener. It has now totally replaced Grade 384 and 305 for heading application. The stable austenitic structure makes 302HQ nonmagnetic, even after substantial cold work, and also results in excellent toughness, even down to cryogenic temperatures. This paper attempts in optimizing the Tungsten Inert Gas (TIG) welding process parameter. The effect of various parameters and their influence is important to determine the strength of welded joint. To obtain a good quality weld, it is therefore, essential to control the input welding parameters. Therefore appropriate selection of input welding parameter is necessary in order to obtain a good quality weld and subsequently increase the productivity of manufacturing industry. This paper present multi objective optimization using grey relation analysis (GRA) for S30430 with TIG process to determine the suitable selection of parameters Experiment were conducted according to Taguchi's design of experiments (DOE) with orthogonal array L9 is used, mathematical model was developed using parameters such as speed (mm/min), current (Amp), voltage (V), depth of penetration (mm). After conducting experiment and collecting data, signal to noise ratio were determined by using Minitab18 and it is used to obtain optimum level for every input parameter.

Optimization the Machining Parameters of Electro Chemical Discharge Machining of NiTi Shape Memory Alloys

The electrochemical discharge machining (ECDM) is a combination effect of electrochemical machining in which metal is removed through the electrochemical process and electrical discharge machining in which metal is removed by rapid current discharges between two electrodes which are separated by a dielectric liquid and subject to an electric voltage. Difficulty of machining nickel titanium alloys by conventional methods such as; the significant tool wear, the need of highly experienced operators, and an excessive degradation in the material performance due to the high thermal and mechanical effects of these methods. For these, reasons non-conventional methods such as electrical discharge machining and electro chemical machining are often used to fabricate NiTi alloys with better machining results. The experiments were conducted with various conditions of voltage (50,60,70 and 80)V, dielectric solution concentration (30 and 40% of NaOH) and nanoparticles silver, and copper content (0.5% Cu, 0.5% Ag, 0.5% Cu and Ag) in the (55% Ni-45%Ti) alloy samples. The machining experiments were designed according to Taguchi's design of experiments (L32). Grey relational analysis was used to optimize the responses of the ECDM process. Material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra) represent the response parameters for machining of the alloy samples prepared by the powder metallurgy route. To achieve the objectives of this research work MiniTab17 software was employed. The optimal conditions were: voltage of 50V, solution concentration of 40% and the sample (NiTi+0.5%Cu+0.5%Ag) have the highest effect on machining characteristics with MRR value of 0.04991mg/sec., tool wear rate value of 0.00125mg/sec., and surface roughness of 0.0117μm.

Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments

This dissertation aims to develop an effective and practical method to forecast chaotic time series. Chaotic behaviour has been observed in the areas of marketing, stock markets, supply chain management, foreign exchange rates, weather forecasting and many others. An effective forecasting model can reduce the potential risks and uncertainty and facilitate planning and decision making in chaotic systems. In this study, residual analysis using a combination of the embedding theorem and ensemble artificial neural networks is adopted to forecast chaotic time series. Based on the embedding theorem, the embedding parameters are determined and the time series is reconstructed into proper phase space points. The embedded phase space points are fed into the first neural network and trained. The weights and biases are kept to predict the future values of phase space points and accordingly to obtain future values of chaotic time series. The residual of the predicted time series is further analyzed; and, if a chaotic behaviour is observed, then the residuals are processed as a new chaotic time series and predicted. This iterative residual analysis can be repeated several times depending on the desired accuracy level and computational efficiency. Finally, the last neural network is trained using neural networks' result values of the time series and the residuals as input and the original time series as output. The initial weights and biases of the neural networks are improved using genetic algorithms. Taguchi's design of experiments is adopted to identify appropriate factor-level combinations to improve the result of the proposed forecasting method. A systematic approach is proposed to improve the combination of ensemble artificial neural networks and their parameters. The proposed methodology is applied to a number of benchmark and some real life chaotic time series. In addition, the proposed forecasting method has been applied to financial sector time series, namely, the stock markets and foreign exchange rates. The experimental results confirm that the proposed method can predict the chaotic time series more effectively in terms of error indices when compared with other forecasting methods in the literature.

Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments

This dissertation aims to develop an effective and practical method to forecast chaotic time series. Chaotic behaviour has been observed in the areas of marketing, stock markets, supply chain management, foreign exchange rates, weather forecasting and many others. An effective forecasting model can reduce the potential risks and uncertainty and facilitate planning and decision making in chaotic systems. In this study, residual analysis using a combination of the embedding theorem and ensemble artificial neural networks is adopted to forecast chaotic time series. Based on the embedding theorem, the embedding parameters are determined and the time series is reconstructed into proper phase space points. The embedded phase space points are fed into the first neural network and trained. The weights and biases are kept to predict the future values of phase space points and accordingly to obtain future values of chaotic time series. The residual of the predicted time series is further analyzed; and, if a chaotic behaviour is observed, then the residuals are processed as a new chaotic time series and predicted. This iterative residual analysis can be repeated several times depending on the desired accuracy level and computational efficiency. Finally, the last neural network is trained using neural networks' result values of the time series and the residuals as input and the original time series as output. The initial weights and biases of the neural networks are improved using genetic algorithms. Taguchi's design of experiments is adopted to identify appropriate factor-level combinations to improve the result of the proposed forecasting method. A systematic approach is proposed to improve the combination of ensemble artificial neural networks and their parameters. The proposed methodology is applied to a number of benchmark and some real life chaotic time series. In addition, the proposed forecasting method has been applied to financial sector time series, namely, the stock markets and foreign exchange rates. The experimental results confirm that the proposed method can predict the chaotic time series more effectively in terms of error indices when compared with other forecasting methods in the literature.

Taguchi’s method of optimization of fracture toughness parameters of Al-SiCp composite using compact tension specimens

The objective of this work is to investigate the process parameters which influence the fracture toughness of aluminum-silicon carbide particulate composite prepared using the stir casting technique. The Taguchi’s design of experiments is conducted to analyze the process parameters. Three parameters considered are composition of material, grain size and a/W ratio. From the Taguchi’s analysis, on compact tension specimens, aluminum 6061 reinforced with 9 wt% of the silicon carbide particles composite and a/W ratio of 0.45 are considered to be optimized parameters. Taguchi's technique result shows that the increment in the a/W ratio causes decrement in the load carrying capacity of the composite. Whereas the fine grain size of silicon carbide have better toughness values. From the ANOVA outcomes it is clear that the composition and a/W ratio of the geometry has more influence on the fracture toughness than the grain size of reinforcement.

EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS FOR ALUMINIUM MATRIX COMPOSITE BY USING TAGUCHI METHOD WITH DECISION TREE ALGORITHM

The intend of this research work is to explore the effect of various parameters in a CNC turning process like cutting speed ([Formula: see text]), feed ([Formula: see text]), and depth of cut ([Formula: see text]) on surface roughness (Ra) of turning AA7075 filled with 10[Formula: see text]wt.% of TiO2 composite fabricated through stir casting method. Taguchi method and decision tree (DT) algorithm were utilized to foresee the surface roughness (Ra) of the proposed composite. The microstructure of composite was ensured with the presence of TiO2 particles dispersed in a homogeneous manner within the matrix material. The machining of composite was carried out by using the CNC turning center and tungsten carbide insert as tool material. This experimental work was designed on L27 (33) orthogonal array using Taguchi’s design of experiments. From its signal-to-noise (S/N) ratio study, the minimum surface roughness (Ra) was obtained at the optimum level of parameters with the cutting speed at 1500[Formula: see text]rpm, feed at 0.15[Formula: see text]mm/rev and depth of cut at 0.3[Formula: see text]mm. Analysis of variance (ANOVA) and decision tree (DT) algorithm were used to identify the significant effect of parameters. The experimental result shows that depth of cut was the major significant parameter on surface roughness (Ra) when compared to cutting speed and feed.

EXPERIMENTAL INVESTIGATION OF COCONUT OIL WITH NANOBORIC ACID DURING MILLING OF INCONEL 625 USING TAGUCHI-GREY RELATIONAL ANALYSIS

This study is based on Taguchi’s design of experiments along with grey relational analysis (GRA) to optimize the milling parameters to minimize surface roughness, tool wear, and vibration during machining of Inconel-625 while using coconut oil as cutting fluid (CF). The experiments were conducted based on Taguchi’s L9 orthogonal array (OA). Taguchi’s S/N was used for identifying the optimal cutting parameter for individual response. Analysis of variance (ANOVA) was employed to analyze the outcome of individual parameters on responses. The surface roughness was mostly influenced by feed. Flank wear was influenced by speed and the vibration was mostly influenced by the depth of cut as well as speed. The multi-response optimization was done through GRA. From GRA, the optimal parameters were identified. Further, nanoboric acid of 0.5 and 0.9[Formula: see text]wt.% was mixed with coconut oil to enhance lubricant properties. Coconut oil with 0.5[Formula: see text]wt.% of nanoboric acid minimizes the surface roughness and flank wear by 3.92% and 6.28% and reduces the vibration in the [Formula: see text]-axis by 4.85%. The coconut oil with 0.5[Formula: see text]wt.% of nanoboric acid performs better than coconut oil with 0.9[Formula: see text]wt.% of nano boric acid and base oil.