Ascertaining of chatter stability using wavelet denoising and artificial neural network

This paper presents experimental and analytical investigation on drilling of carbon fibre reinforced plastic and aluminium stacks. The experimental results conducted as per full factorial experimental design reveal that drill diameter and feed rate have significant effects in reducing thrust force and torque while spindle speed has the least effect. The analytical study is based on artificial neural network (ANN) training using feed-forward back propagation network. The correlations obtained by multi-variable regression analysis and ANN, indicate that ANN is more effective than regression analysis.

The rapid growth in population increases water demand thus resulting in scarcity of water which is due to improper management rather than lack of resources. Reservoir is the most important source for surface water. So, reservoir storage plays a crucial role in efficient reservoir management. Artificial neural network (ANN) is capable of simulating reservoir storage capacity. So, in present work cascade forward back propagation and feed forward back propagation network artificial neural network (ANN) models with five different architecture in each by changing the number of hidden layer neurons were developed to predict Harangi reservoir storage, Karnataka, India. The first two years (2010–2012) data on daily basis was used for supervised training and remaining (2013–2014) data on daily basis was used in prediction. The predictive accuracy using the statistical parameters like correlation coefficient (R) and mean absolute percentage error (MAPE) were found within well acceptable limit. Results shows that, compared to the cascade forward back propagation network, feed forward back propagation network architecture of 6-5-1 ANN model with correlation coefficient (R) of 0.9882 and absolute mean percentage error (MAPE) of 5.225%. is best suited for present study.

This study evaluates the impacts of machining parameters on the milling of general-purpose poly (methyl methacrylate) (PMMA) with respect to cutting point temperature, maximum machining temperature, and surface roughness. The machining parameters used in the analysis are spindle speed (rpm), depth of cut (mm), and feed rate (mm/min). The extreme ranges of the machining parameters for the material are obtained from trial experiments. From these experiments, four values of each parameter are adopted. This yields 12 experiments, which are divided into three sets. In the first set of experiments, the depth of cut and feed rate are held constant at 0.2 mm and 25 mm/min, while the spindle speed is varied from 1000 rpm to 4000 rpm. In the second set of experiments, the spindle speed and feed rate are held constant at 1000 rpm and 25 mm/min, respectively, while the depth of cut is varied from 0.2 mm to 1.1 mm. In the last set of experiments, the spindle speed and depth of cut are held constant at 1000 rpm and 0.2 mm, respectively, as the feed rate is varied from 25 mm/min to 100 mm/min. Thermal images are obtained during milling, where the cutting point and maximum machining temperatures are obtained. The milled surfaces are then investigated for surface roughness. The chips are also collected from each experiment and used in conducting chip morphology. From the results, it is observed that to obtain the least temperatures and best surface quality, the least machining parameters should be chosen. These parameters are identified as 1000 rpm, 0.2 mm, and 25 mm/min for the spindle speed, depth of cut, and feed rate, respectively. An increase in spindle speed seems to increase the milling temperatures and surface roughness, which is attributed to the reducing chip thickness. An increase in the depth of cut does not affect the generation of chips, and the thickness is relatively constant. However, an increase in milling feed increases the chip thickness, which increases the surface roughness.

After the discovery of probable human carcinogen namely "acrylamide" in thermally processed foods, a number of theoretical mechanisms have been proposed for its formation.Most probably, acrylamide in food results largely from the Maillard reaction between amino acids (primarily asparagine) and a reactive carbonyl, proceeding through intermediates that include a Schiff's base.Several intrinsic and extrinsic factors, such as the initial concentration of reactants and their ratio, temperature and time of processing, have been shown to influence the levels of acrylamide in thermally processed foods.In order to predict and to control the amount of acrylamide formed, kinetics of acrylamide in function of process and product variables need to be known.Considering the complexity of various possible formation mechanisms of acrylamide in Maillard reaction, it seems to be an almost impossible task to elucidate the kinetics of all the pathways involved.Artificial neural network (ANN) modeling, on the other hand, may be a viable alternative to the kinetic models for predicting the acrylamide formations.Artificial neural network (ANN) is a potent computer model that learns from the examples through iterations without requiring prior knowledge of the relationships of process parameters.Predictions with ANN are not like modeling simulation, but outputs are obtained from a learning algorithm based on experimental data.Here, ANN approach was used to predict acrylamide concentration and browning ratio of potato crisps as influenced by various parameters.Concentrations of asparagine (3500-5000 ppm), glucose (5000-12000 ppm), the temperature (160-180oC) and the frying time (1-5 min) were the parameters used as the input variables to build the ANN model.The experimental data (72 data points) were used for ANN training using a feed forward back propagation network algorithm.Decision on the use of a given number of hidden layer nodes is complex because it depends on the specific problem being solved.In our study, the architecture with two hidden (5 and 3 nodes) layers and one output layer (2 nodes) was selected.The linear regression coefficient of 0.999 and 0.992 were determined between measured and predicted acrylamide levels for the training data set and testing data set, respectively.

The article focuses on developing a predictive and optimization model for surface roughness in ultra-precision turning (UPT) using a diamond cutter on the spherical surface of Al6061 material. An experimental model with 30 experiments was established, considering three input parameters: spindle speed, feed rate, and depth of cut. The results from this model were collected to create an input dataset for an artificial neural network (ANN) to build a surface roughness prediction model. The ANN structure 3-5-10-1 provided the best prediction results, with Coefficient of Determination ( ) is 0.98, Mean Absolute Percent Error ( ) is 13.36%, Mean Square Error ( ) is 0.68, and Root Mean Square Error ( ) is 0.82. Additionally, the artificial bee colony (ABC) algorithm was employed to determine the optimal cutting parameters that minimize surface roughness. The results indicated that the minimum roughness value achieved was 0.76 nm with the cutting parameters: spindle speed of 823 rev/min, feed rate of 13 mm/min, and depth of cut of 1 µm. Moreover, the effects of different cutting parameter combinations on surface roughness were analyzed and evaluated. The integration of the ANN model with the ABC algorithm enables a reliable prediction model for surface roughness and demonstrates high efficiency in optimizing the objective function. This research contributes valuable insights into surface roughness prediction and optimization in ultra-precision turning of Al6061 material. Furthermore, the proposed modeling and optimization approach can be extended to other materials and the processing of aspherical and diffractive surfaces.

ABSTRACTIn this research paper, the development of an artificial neural network (ANN) model for the designing of a compact dual-band hybrid fractal antenna (CDHFA) that covers Wireless Medical Telemetry Service and industrial, scientific and medical (ISM) bands are described. The projected CDHFA is designed by using 1.6mm thick, FR4 substrate whose permittivity is 4.4 and obtained by the combination of different fractal geometries. The dimensions of the projected CDHFA are interlinked with each other. A set of 80 CDHFAs with different dimensions is designed to obtain a data dictionary for the implementation of ANN. From the experimental results, it is depicted that the resonant frequencies are 1.4290 and 2.4380 GHz with S(1,1) values −12.4 and −15.72 dB, respectively. A comparison between feed forward back propagation network, generalised regression neural network and desired outputs in terms of average absolute error is also done. The simulation, experimental and ANN results are well matched that indicates the effectiveness and accuracy of the developed ANN model for biomedical applications.

Single point incremental forming (SPIF) is an advanced, flexible, and cost-effective approach for producing complicated sheet metal objects quickly. Because of its low equipment cost, the process is best suited toward low or medium quantity batch production as the conventional stamping method remains cost-effective only for mass manufacture. During SPIF formation of titanium grade 5 materials, a response surface methodology and artificial neural network (ANN) model was created to optimize and estimate wall angle (Ømax) and average surface roughness ( Ra). The ANN model is developed using feed forward back propagation networks. Various combinations of transfer functions and number of neurons were used to create the ANNs (3- n-1, 3- n-2). The confirmation runs have been used to ensure that the ANN anticipated and practical findings have been in agreement. The generated ANN model (3- n-1) was capable of accurately forecasting the results of the experiment, with an overall R-value and Mean Square Error (MSE) of 0.99987 and 0.010905 for Ra, and 0.99999 and 0.00700 for wall angle. The best 3- n-2 models has an average R-value of 0.99992 and MSE of 0.05532, respectively. As a consequence of rapid ANN modeling technique, it became discovered that technical effort inside the SPIF process could be decreased.

For modeling and optimizing the process parameters of manufacturing problems in the present days, numerical and Artificial Neural Networks (ANN) methods are widely using. In manufacturing environments, main focus is given to the finding of Optimum machining parameters. Therefore the present research is aimed at finding the optimal process parameters for End milling process. The End milling process is a widely used machining process because it is used for the rough and finish machining of many features such as slots, pockets, peripheries and faces of components. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear resistance were taken as the output .Experimental design is planned using DOE. Optimum machining parameters for End milling process were found out using ANN and compared to the experimental results. The obtained results provβed the ability of ANN method for End milling process modeling and optimization.

In this study, the milling behavior of AISI D2 cold work tool steel was experimentally investigated on a CNC vertical machining center. The aim was to optimize surface roughness through the Taguchi L18 orthogonal array design, considering the effects of cooling method, spindle speed, feed rate, and depth of cut. A total of 18 experiments were conducted under both wet and dry machining conditions. The results revealed that the use of coolant generally led to higher roughness values compared to dry machining. Statistical analysis using signal-to-noise (S/N) ratios and analysis of variance (ANOVA) showed that coolant application had the highest impact on surface roughness with a contribution rate of 47.04%, followed by spindle speed (27.99%), feed rate (8.05%), and depth of cut (4.66%). Regression models were developed to predict surface roughness, achieving a high correlation with experimental values (R² = 0.90). Confirmation experiments verified the optimization, with the optimal parameter combination (dry machining, 4000 rpm, 750 mm/min feed rate, and 0.10 mm depth of cut) reducing the average surface roughness from 1.08 μm to 0.37 μm, corresponding to a 65.7% improvement. Additionally, quality loss analysis indicated that losses were minimized by nearly 88.3% under optimized conditions. The results indicate that surface roughness values obtained under dry machining conditions were lower than those achieved using coolant in the milling of AISI D2 tool steel.

Lung cancer classification using neural networks for CT images

The paper describes a quantitative structure-activity relationship (QSAR) study of IC 50 values of benzimidazole derivatives on escherichia coli methionine aminopeptidase. The activity of the 32 inhibitors has been estimated by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques. The results obtained using the MLR method indicate that the activity of derivatives of benzimidazoles on Co II -loaded escherichia coli methionine aminopeptidase depend on different parameters containing topological descriptors, Burden eigen values, 3D MoRSE descriptors and 2D autocorrelation descriptors. The best artificial neural network model is a fully-connected, feed forward back propagation network with a 5-4-1 architecture. Standard error for the training set using this network was 0.193 with correlation coefficient 0.996 and for the prediction set standard error was 1.41 with correlation coefficient 0.802. Comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive power. KEY WORDS : QSAR, Artificial neural network, Multiple linear regression, Molecular descriptors, Escherichia coli methionine aminopeptidase Bull. Chem. Soc. Ethiop. 2010 , 24(3), 317-325.

In this paper, a new attempt has been made in the area of tool-based micromachining for automated, non-contact, and flexible prediction of quality responses such as average surface roughness (Ra), tool wear ratio (TWR) and metal removal rate (MRR) of micro-turned miniaturized parts through a machine vision system (MVS) which is integrated with an adaptive neuro-fuzzy inference system (ANFIS). The images of machined surface grabbed by the MVS could be extracted using the algorithm developed in this work, to get the features of image texture [average gray level (Ga)]. This work presents an area-based surface characterization technique which applies the basic light scattering principles used in other optimal optical measurement systems. These principles are applied in a novel fashion which is especially suitable for in-process prediction and control. The main objective of this study is to design an ANFIS for estimation of Ra, TWR, and MRR in micro-turning process. Cutting speed (S), feed rate (F), depth of cut (D), Ga were taken as input parameters and Ra, TWR, MRR as the output parameters. The results obtained from the ANFIS model were compared with experimental values. It is found that the predicted values of the responses are in good agreement with the experimental values.

The impact of cutting parameters, rake angle, and coolant type on the surface quality of ASTM A36 steel during turning operations is examined in this study. Five criteria were taken into consideration when designing the trials utilizing a Taguchi L27 orthogonal array: feed rate (0.10, 0.20, 0.30 mm/rev), spindle speed (600, 900, 1200 rpm), rake angle (70°, 80°, 90°), coolant condition (oil, water, or dry), and depth of cut (0.50, 1.00, 1.50 mm). Four uniformly spaced sites along the machined cylindrical surface were used to assess surface roughness (Ra), and the mean Ra values were statistically examined. The findings show that surface finish is greatly impacted by coolant type and rake angle, with oil-based cooling and a 90° rake angle producing the greatest results. Ra typically decreases with increasing spindle speed, although it tends to increase with increased feed and depth of cut. ANOVA verified that feed rate and spindle speed were the next biggest contributors to Ra variation, after coolant type. Oil coolant, 90° rake angle, 1200 rpm spindle speed, 0.10 mm/rev feed, and 1.0 mm depth of cut were the ideal conditions for minimizing surface roughness, resulting in an average Ra of 3.13 µm. The results show that the surface quality of ASTM A36 components can be significantly improved by choosing the right coolant and tool shape, which is essential for increasing machinability and extending service life in industrial applications.

Fatigue crack growth and deformation in weldments of aluminium alloys Hermann, R. Mater. Sci. Eng. A (15 Oct. 1994) 186 (1–2), 45–53

As an innovative approach in this study, artificial neural network and cuckoo algorithm have been applied to estimate and optimize the main cutting forces of various Si brass alloys during turning operation due to economic reasons. Accordingly, the chemical composition (Cu, Zn and Si contents) and process parameters (cutting speed, feed rate and depth of cut) are simultaneously implemented as input variables and the main cutting force is adjusted as an output variable. Moreover, the genetic algorithm is used to determine the optimum condition of the input parameters to obtain the lowest amounts of the main cutting force. Coupling of the hybrid cuckoo algorithm with artificial neural network has resulted in decreasing the mean absolute percentage error of the optimum structure (6-10-7-1) from 9.025 to 1.59E–6%. The validation of the proposed model has been done by performing the new set of experimental tests. The measured and predicted main cutting forces are in good agreement. The Si brass alloys including Zn equivalent about 44.97 wt% has the lowest main cutting force due to the formation of the Widmanstäetten morphologies in the microstructure. The outcome of this study may be useful for machining industry of the free-cutting Si brasses.