Optimal Parameter Combination Research Articles

Computational modelling of scleral photocrosslinking: from rat to minipig to human.

Selective scleral crosslinking has been proposed as a novel treatment to increase scleral stiffness to counteract biomechanical changes associated with glaucoma and high myopia. Scleral stiffening has been shown by transpupillary peripapillary scleral photocrosslinking in rats, where the photosensitizer, methylene blue (MB), was injected retrobulbarly and red light initiated crosslinking reactions with collagen. Here, we adapted a computational model previously developed to model this treatment in rat eyes to additionally model MB photocrosslinking in minipigs and humans. Increased tissue length and subsequent diffusion and light penetration limitations were found to be barriers to achieving the same extent of crosslinking as in rats. Per cent inspired O2, injected MB concentration and laser fluence were simultaneously varied to overcome these limitations and used to determine optimal combinations of treatment parameters in rats, minipigs and humans. Increasing these three treatment parameters simultaneously resulted in maximum crosslinking, except in rats, where the highest MB concentrations decreased crosslinking. Additionally, the kinetics and diffusion of photocrosslinking reaction intermediates and unproductive side products were modelled across space and time. The model provides a mechanistic understanding of MB photocrosslinking in scleral tissue and a basis for adapting and screening treatment parameters in larger animal models and, eventually, human eyes.

Prediction of the swelling rate of irradiated type 316 stainless steels via machine learning methods

Irradiation-induced void swelling typically leads to the degradation of mechanical properties of metals and the significant decrease in material density, which influences the safety of nuclear reactors. It is essential to understand the irradiation-induced swelling behavior of alloys. In this work, machine learning (ML) methods are adopted to make a prediction on the swelling rate of type 316 stainless steels. Based on the defined target variable and potentially influential factors, the dataset which contains 333 samples and 23 features is created with excavating previous studies. The correlation analysis between all pairs of variables is performed firstly. Based on the grid search technology, k-fold cross validation is used to obtain the optimal combination of parameters for ML models. Five ML models are fitted and their predictive performances are compared. Because GradientBoosting (GB) model exhibits the best prediction, it is used to identify the most controlling factors in the prediction of the irradiation-induced swelling behavior of alloys. The GB model gives the results which are in agreement with the established conventional understanding of factors affecting the swelling rate of type 316 stainless steels. As a summary, the application of ML to the prediction of the irradiation-induced swelling behavior of alloys provides a new insight to the development of irradiation resistance steels.

A study on optimal parameter combinations for austempered ductile iron

Nodular cast iron possesses a spherical graphite shape and exhibits mechanical properties closely resembling those of steel after heat treatment. The austempering method provides a means of enhancing the mechanical properties of nodular cast iron. This study aimed to investigate the optimal parameter combinations for Austempered Ductile Iron (ADI). The experiments involved varying the austenitization temperature and austempering time. Multiple tests were conducted to analyze the effects of austenitization temperature and tempering time on the microstructure, hardness, and impact toughness. The experimental samples were obtained from Y-block nodular cast iron austenitized at 850, 900, and 950°C for 90 min in an electric furnace. Subsequently, they were quenched in a salt bath at a temperature of 350°C and held for 60, 90, and 120 minutes before being cooled to room temperature. Mechanical tests and microstructure observations were performed on both the as-cast sample and after austempering. The microstructure was observed using an optical microscope before and after the etching. The research findings indicated that austempering increases the hardness and impact toughness of nodular cast iron. The highest hardness (46.8 HRC) was achieved from a parameter combination of an austenitization temperature of 850°C and an austempering time of 90 minutes. This particular sample also exhibited a relatively higher impact energy (3.8 J) compared to the others. The results of this study suggest that the austenitization temperature and tempering time exert an influence on the mechanical properties.

Bearing fault diagnosis based on POA-VMD with GADF-Swin Transformer transfer learning network

To address the challenges posed by early weak fault signals hindering fault feature extraction and leading to diminished recognition accuracy in deep learning models, we present a method for diagnosing bearing faults. This method integrates the Pelican Optimization Algorithm (POA), Variational Mode Decomposition (VMD), Gramian Angular Difference Fields (GADF), and the Swin Transformer network. Initially, the VMD’s key parameter are optimized through POA to dynamically yield the optimal parameter combination. Subsequently, the refined VMD is employed to decompose the original signal. The intrinsic modal function (IMF) is subsequently filtered, and the signal is reconstructed using the weighted composite kurtosis(WCK) index. Following this step, the reconstructed 1D signal is converted into a 2D image using GADF and is then input into the constructed Swin Transformer transfer learning network model for the purpose of training. In conclusion, a fault diagnosis experiment was conducted using the measured signal, which showed the successful extraction of the signal’s characteristics and octave frequencies through the method presented in this paper. Moreover, concerning fault identification, an identification accuracy exceeding 95% was achieved.

Electrocoagulation treatment for Remazol Brilliant Blue R (Reactive Blue 19) removal of textile dye simulated wastewater using iron-aluminum electrodes

Abstract The presence of non-biodegradable dyes, such as Remazol Brilliant Blue R (Reactive Blue 19), in wastewater poses a significant environmental challenge. This study investigates the application of electrocoagulation using iron-aluminum (Fe-Al) electrodes to efficiently remove Reactive Blue 19 dye from simulated textile dye wastewater. It considers various parameters such as electrode material, current, stirring speed, as well as the influence of sodium chloride acting as a supporting electrolyte to enhance conductivity. Additionally, it evaluates the economic feasibility through electrode consumption analysis. The experimental setup involved a designed reactor with aluminum and iron electrodes arranged at bipolar parallel connection, along with a 30-minute electrocoagulation process conducted at ambient temperature and subsequent filtration of the treated wastewater for UV-VIS analysis. The optimal parameter combination obtained consists of Fe-Al electrode material, a 3 A current, and a 200-rpm stirring speed, achieving 96.87% dye removal. Furthermore, extending the operating time to 60 minutes improved the removal by 99.15%. Under these optimal conditions, the energy consumption measured was 36 kWh/m3. The high dye removal rate obtained shows the effectiveness of this electrocoagulation process in treating Reactive Blue 19 simulated wastewater. A t-test, employed as a hypothesis testing technique with a significance level of 0.05, confirmed the optimal levels of the factors and interactions within the treatment process. A remarkable coefficient of determination of 0.9794 in the dye removal sensitivity analysis signifies a strong goodness of fit, indicating that these three (3) parameters collectively explain how they affect the outcome while accounting for dye removal efficiency variation. These results showed that electrocoagulation is a promising technology for treatment of Reactive Blue 19 dye wastewater.

The impact of nozzle parameters and water injection pressure on the application effect of hydraulic punching enhanced permeability technology in high ground stress coal seam

High-stress coal seams are different from ordinary coal seams in terms of their low hardness, low permeability, and certain degree of fluidity, making them more difficult to enhance permeability. Hydraulic punching enhanced transmission technology can quickly create voids in coal seams, but existing hydraulic punching techniques are suitable for shallow coal seams, and the effect of creating voids is greatly reduced when applied to high-stress coal seams. To optimize the application effect of hydraulic punching enhanced transmission technology in high-stress coal seams, theoretical research was conducted on the parameters of hydraulic punching nozzles and injection water pressure. A simulation model was established using COMSOL numerical simulation software to optimize the nozzle parameters and explore the combined effect of nozzle parameters and injection water pressure on enhancing permeability. Field experiments were conducted in the Zhaotong Tunnel of the China high-speed railway to determine the optimal combination of nozzle parameters and injection water pressure, thereby increasing the overall permeability of the coal seam and improving gas extraction efficiency. The results indicate that: (1)Through theoretical calculations, numerical simulations, and experimental verification, it has been proven that the perforation effect of a jetting device with three the long oblique nozzle with an oblique length of 15 mm and a size of 3 mm × 6mm is the best among the comparative results under a water injection pressure of 5 MPa；(2)Compared to the perforation effect before optimization, the water consumption for perforation after optimization is reduced to 0.6 times that before optimization, and the perforation efficiency after optimization is 3.556 times that before optimization. The perforation efficiency has been effectively improved；(3)The similarity between the theoretical results, simulation results, and experimental results in this study is all above 85%. The consistency of the results provides mutual verification and has certain engineering guidance significance.

Application of support vector machines and genetic algorithms to fluid identification in Offshore Granitic subduction hill reservoirs

YL8 area granite submarine is located in the Songnan low uplift of Qiongdongnan Basin, submarine reservoir fracture development, non-homogeneous strong. Geophysical logging and gas logging do not show a clear pattern of response to the fluid properties of submarine reservoirs, and it is difficult to use logging information to linearly classify granite submarine reservoir fluids in two dimensions. Support Vector Machines (SVM) can map multi-dimensional data into high-dimensional space and construct hyperplanes in high-dimensional space to categorize the samples, which meets the need to incorporate logging curves and mud gas parameters that show complex response relationships with the fluid types of subducted reservoirs into the model.Therefore, in this paper, a support vector machine (GA-SVM) granite subduction reservoir fluid prediction model based on genetic algorithm optimisation is constructed. Firstly, the petrophysical characteristics of different reservoirs in the granite subduction hill were analysed in detail, acquisition and standardisation of logging and mud gas logging multi-dimensional parameters sensitive to reservoir fluids, inputting sensitive parameters into GA-SVM mapping multi-dimensional parameters to higher dimensional space using Gaussian radial kernel functions, using genetic algorithms to perform crossover and mutation operations on the kernel parameter g and the penalty parameter C to obtain the optimal parameter combinations, optimising the number of GA-SVM iterations and model accuracy. The GA-SVM predictions were compared with two other machine learning predictions. As demonstrated through actual case studies in the YL8 area, fluid type recognition compliance rate of 88.2% by GA-SVM, GA-SVM effectively improve the compliance rate of fluid identification in granite subduction reservoirs, provides a new idea for logging interpretation of complex reservoirs in granite subduction reservoirs.

Influence of operating parameters on the silt erosion performance of bare and coated SS304 steel

Purpose The purpose of this paper is to investigate the silt erosion performance of Bare, 75%Cr2O3 + 25%Al2O3 and 85%Cr2O3 + 15Al2O3-coated SS304 under various control parameters such as rotation speed, concentration of silt and particle size of silt used for making slurry. This can provide insight for using chromia and alumina-based coatings for hydro-turbines. Design/methodology/approach Taguchi approach was used to identify the effect of three input parameters on the bare and coated alloys. L16 orthogonal array is used for determining the signal-to-noise (S/N) ratio for each process parameter. For each level of parameters taken into consideration about the erosion wear, the arithmetic mean of the S/N ratio is calculated. On the essence of the results of S/N ratios, it is possible to determine the effect of the most dominating parameters of the erosion wear. Findings Results show that the erosion increases with an increase in silt concentration (Wt.%). It has been analyzed that the rotational speed has the most significant effect followed by the particle size and concentration on erosion wear for all uncoated and coated SS-304 samples. Maximum resistance to erosion is provided by 85%Cr2O3 + 15%Al2O3. The least erosion wear for process parameters has occurred at the optimal parametric combination of rotational speed (N) = 415 rev/min, concentration (C) = 15 Wt.% and particle size range as <53 µm for uncoated and coated stainless steel. Originality/value The study clearly shows the silt erosion performance of chromia and alumina coatings of different compositions at different input parameters. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0028/

Multi-Objective Optimization of the Recovery of Base and Precious Metals from Waste Printed Circuit Boards by Two-Stage Hydrometallurgical Process Using Taguchi-Based Grey Relationship Analysis

AbstractThis study focused on determining the optimum conditions for the maximum recovery of base and precious metals from printed circuit boards of end-of-life desktop computer motherboards using Taguchi-based grey relation analysis. In the first stage of the two-stage study, optimum conditions were investigated for the dissolution of base metals (copper and zinc) in waste printed circuit boards under high-pressure leaching. The dissolution of base metals was performed based on the L25 orthogonal array designed by Taguchi method. In the second step, designed according to Taguchi L9 orthogonal array to recover gold and silver from the solid remaining from the pressure-leaching process. Optimum combinations of parameters in both stages were determined using the multi-criteria optimization technique grey relationship analysis. In the experiments carried out in the determined optimum combinations, 99.62% of copper, 98.76% of zinc, 99.15 of silver and 85.82% of gold in waste printed circuit boards were recovered. Graphical Abstract

Sandstone breaking performances and mechanisms of swirling abrasive waterjet during radial jet drilling process

Radial jet drilling (RJD) is one of the emerging hydrocarbon drilling technologies. And, the swirling abrasive waterjet (SAWJ) is expected to drill larger diameter laterals during RJD. Here, the performances and mechanisms of SAWJ breaking sandstone were studied by laboratory experiments. Results showed that the SAWJ could drill a smooth (surface roughness was 0.043 mm) & large (diameter was in 52.0–73.0 mm) circular hole on sandstone. The hole depth/volume increased as the jetting pressure, abrasive mass concentration and exposure time increased. Conversely, they decreased as the standoff distance increased. The optimal parameter combination under our experimental conditions was 30 MPa, 0 mm, 12% and 1 min. The SAWJ sandstone breaking mechanism were the erosion of cements, the integrally peeled off and broken of crystal grain. Failure mode of sandstone was mainly the tensile fracture. The key findings will provide guidance for the application of SAWJ in RJD technology.

Parameter calibration of discrete element model for gluten densification molding.

The densified powder material is convenient for storage and transportation, with broad market application prospects. In this study, the discrete element model parameters required for simulating gluten densification were calibrated using the Hertz-Mindlin with JKR contact model. Initially, physical testing techniques were utilized to assess the size distribution, density, and angle of repose (AoR) of gluten particles. Following this, the Plackett-Burman test, the steepest ascent test, and the Box-Behnken test were conducted, and the significant factors were obtained: The coefficient of rolling friction (P-P) was 1.038, the coefficient of static friction (P-P) was 0.071, and the surface energy (P-P) was 0.047. Finally, the AoR and densification simulations were performed under the optimal parameter combination, along with validation tests. The results showed that the relative error between the simulated and tested AoR was 0.52%. The compression ratio and compression force curves of simulated and actual were similar.

Development of a Bionic Picking Device for High Harvest and Low Loss Rate Pod Pepper Harvesting and Related Working Parameter Optimization Details

Addressing the issues of low harvesting efficiency and high losses in current pod pepper harvesters, this study presents the design of a bionic comb finger pod pepper picking device and an inclined double-spiral bell pepper harvester to improve the harvest rate and reduce loss rate. Through the utilization of discrete element simulation software EDEM, a discrete element model for pod peppers is established. Additionally, a simulation platform for ground drop loss during pod pepper picking is developed, enabling exploration of the movement trajectory and velocity changes of the pod pepper elements. The study also conducts an analysis on the impact of the speed of the picking rollers X1, the feeding speed of pod peppers X2, and the spacing between the two picking rollers X3 on ground drop losses. Based on the results of the single-factor test, the Box–Behnken response surface test was used to optimize the working parameters of the picking device, which resulted in the optimal combination of the working parameters of the picking device: the speed of the picking rollers was 680.41 rpm, the feeding speed of the pod peppers was 0.5 m/s, and the spacing between the two picking rollers was 12 mm, which resulted in the loss rate of pod peppers on the floor of the ground being 3.526%.

Prediction of Suitable Habitat of Alien Invasive Plant Ambrosia trifida in Northeast China under Various Climatic Scenarios

Ambrosia trifida is an invasive alien plant species, which has very high reproductive and environmental adaptability. Through strong resource acquisition ability and allelopathy, it could inhibit the growth and reproduction of surrounding plants and destroy the stability of an invasive ecosystem. It is very important to predict the change of suitable distribution area of A. trifida with climate change before implementing scientific control measures. Based on 106 A. trifida distribution data and 14 points of environmental data, the optimal parameter combination (RM = 0.1, FC = LQ) was obtained using the MaxEnt (version 3.4.1) model optimized by Kuenm package, and thus the potential suitable areas of A. trifida in Northeast China under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) with different emission intensities in the future (2050, 2070) were predicted. The changes of A. trifida suitable area in Northeast China under three climate scenarios were compared, and the relationship between the change of suitable area and emission intensity was analyzed. In general, the suitable area of A. trifida in Northeast China will expand gradually in the future, and the area of its highly suitable area will also increase with the increasing emission intensity, which is unfavorable to the control of A. trifida.

Design and Experimentation of Rice Seedling Throwing Apparatus Mounted on Unmanned Aerial Vehicle

In order to further exploit the production advantages of rice throwing, this paper proposes a systematically designed throwing device suitable for integration with unmanned aerial vehicles (UAVs). The device primarily comprises a seedling carrying and connection system, a seedling pushing mechanism, and a seedling guiding device. The operational principles and workflow of the device are initially elucidated. Subsequently, an analysis of factors influencing rice throwing effectiveness is conducted, with throwing height, working speed, and the bottom diameter of the seedling guide tube identified as key factors. Seedling spacing uniformity and seedling uprightness are designated as performance indicators. A three-factor, three-level response surface experiment is conducted, yielding regression models for the experimental indicators. Through an analysis of the response surface, the optimal parameter combination is determined to be a throwing height of 142.79 cm, a working speed of 55.38 r/min, and a bottom diameter of the seedling guide tube of 43.51 mm. At these parameters, the model predicts a seedling spacing uniformity of 88.43% and a seedling uprightness of 88.12%. Field experiments validate the accuracy of the optimized model results. Experimental data indicate that under the optimal operational parameters calculated by the regression model, the seedling spacing uniformity is 86.7%, and the seedling uprightness is 84.2%. The experimental results meet the design requirements, providing valuable insights for UAV rice-throwing operations.

The Simulation and Parameter Optimization of the Hole-Forming Process of a Duckbilled Hole-Forming Device

This paper addresses the hole-forming process of a duckbilled hole-forming device. Based on a coupled simulation using the multi-body dynamics software RecurDyn and the discrete element software EDEM, the hole-forming mechanism of a duckbilled hole-forming device and the influence of control parameters on the hole-forming performance of the hole-forming device were studied. In this paper, we analyze the direction and speed of soil particles transported under soil disturbance by a hole-forming device through the simulation and study of the hole-forming mechanism of the hole-forming device. By controlling parameters such as the traction angle, forward speed, and mass of the hole-forming device, the influence of the control parameters on the hole-forming trajectory of the duckbilled hole-forming device was investigated. Orthogonal tests determined the optimal combination of control parameters. The results show that the hole-forming process of the hole-forming device mainly comprises squeezing and shearing the soil to form holes, and the hole-forming performance of the hole-forming device was optimal when the traction angle was 17.3°, the forward speed was 1.11 m/s, and the mass of the hole-forming device was 17.9 kg.

Design and simulation of a new type of fin-and-tube heat exchanger with trapezoidal slit fins

Slit fin-and-tube heat exchangers are extensively utilized in industry. The design of fin structure is crucial for improving the performance of heat exchanger and reducing energy losses. In this study, a new type of fin-and-tube heat exchanger with trapezoidal slit fins was proposed in this study. To determine the performance of the fins, physical and mathematical models of heat exchanger with the fin spacing Fp of 2.65 mm were established. The effects of structural parameters on heat exchanger performance were investigated by numerical simulation, which includes the slit height Sh, slit width Sw, transverse tube spacing Pt, and longitudinal tube spacing Pl. The effects of the four structural parameters on the comprehensive performance were analyzed by the evaluation index. The results revealed that the comprehensive performance of trapezoidal slit fins had increased by 9.6 % compared to flat fins. Furthermore, the influence degree of structural parameters on heat transfer performance was obtained based through the orthogonal experiments. The order of influence degree was as follows: Pl > Pt > Sh > Sw. Additionally, the optimal parameter combination was also obtained as follows: Sh = 1.47 mm, Pt = 42 mm, Pl = 27.5 mm, and Sw = 4 mm.

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis.Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established.Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model.Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

Parametric analysis, modeling and optimization of the process parameters in electric discharge machining of aluminium metal matrix composite

Optimizing electric discharge machining (EDM) for aluminum/SiCp metal matrix composites poses challenges due to intricate machine parameters and process complexity, impacting process economy and elevating product costs. The research aims to find the optimal combination of process parameters which include pulse on-time, pulse current, duty cycle (%), gap voltage, sensitivity and flushing pressure for EDM of Al/SiCp-MMC using a copper electrode for the selected response factors such as material erosion rate and surface roughness, Ra. The experiments were designed using the central composite design of response surface methodology and an advanced optimization technique known as Teaching–learning-based optimization (TLBO), is applied to find the optimal combination of process parameters to obtain maximum material erosion rate subject to the desired range of surface roughness (SR), Ra. The combination of the high pulse on-time (i.e. 150 μs) and high pulse current (i.e. 12A) results in high material removal rate with deep craters on the machined surface clearly visible in SEM images contrasting the minimized surface roughness at lower values of pulse on-time (50 μs) and the pulse current (4A). Pulse on - time (Ton) is found to be the most significant factor for material erosion rate and surface roughness with percentage contribution of 70.86 and 54.9 respectively for optimization of the response. The regression models were developed at 95% confidence level for material removal rate and surface roughness with R2 value of 0.93 and 0.95 respectively signifying high degree of accuracy in predicting the response. Confirmation tests conducted to check the adequacy of the established models revealed that the percentage error between the predicted and experimental responses is found to be within acceptable levels. Electron discharge machining of the aluminium metal matrix composite at the optimized conditions could provide economical aspect in the aerospace and automobile industry.

Parameter Design of Sand Casting to Minimize Shrinkage Defects, Porosity Defects and Incomplete Defects

Casting is one of the most commonly used manufacturing method for geometrically complex product. Among the casting technologies, sand casting is often found in application. Beside the advantages of the sand-casting process, it also has a drawback that is often encountered, namely product defects. Product defects can be caused by a lack of attention to sand casting parameters. This work aims to minimize those defects, including porosity defects, shrinkage defects and incomplete defects by optimizing process parameters using the Taguchi method. The identified sand-casting process parameters include gating location, riser location, molding conditions and pouring temperature with each parameter consisting of 3 levels. This research using the fractional factorial L9 (34). Data processing is carried out by analysis of mean (ANOM) to obtain plot effects. The results showed that the optimal combination of parameters that resulted in shrinkage, porosity and minimal incomplete defects were at gating location level 2, riser location level 1, mold condition level 2 and pouring temperature level 3. The results of the comparison before and after optimization were an increase of 0.77, where previously it was 1.30 to 2.07 based on normalized data.