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Super-duplex stainless steel (SDSS) 2507 is well known for its complex microstructure and alloying components, which contribute to its exceptional mechanical qualities and resistance to corrosion. Nevertheless, these features provide considerable difficulties during machining. In order to address these challenges and advance sustainability, this work presents a novel machining technique that combines minimal quantity lubrication (MQL) with surface-textured tools. Two different kinds of texture tools were examined: one with linear grooves and the other with a hybrid texture that combined both circular pits and holes. A CNC center lathe machine operating under MQL conditions was used for the experiments. It had two distinct cooling conditions, different tool textures, and cutting parameters like depth of cut, feed rate, and speed. Cutting speed, followed by feed rate, was the most important factor affecting cutting force and tool flank wear, according to analysis of variance (ANOVA). Comparing the hybrid-textured tool under MQL to dry machining conditions, the cutting force was reduced by 40%. A cutting speed of 40m/min, feed rate of 0.1mm/rev, and depth of cut of 0.4mm were found to be the optimal machining parameters employing MQL and hybrid-textured tools for minimizing cutting force and tool wear, the optimal values are 119.57N and 113.875$\mu$m respectively. These findings provide notable enhancements in machinability and sustainability for SDSS 2507, demonstrating the efficiency and usefulness of the suggested approach in MQL machining.

Norway spruce is fumigated with O₃, SO₂, and NO₂ in open-top chambers. The spruce needles from the first two years of fumigation were analysed by means of PIXE. A total of 14 elements were found in concentrations above the detection limits in the major part of the around 150 samples analyzed. The results were used in an analysis of variance showing, slightly increased levels of S and Zn in needles from trees exposed to fumigation with all three gases.

This paper uses the Taguchi method on the tolerance design of a hinge mechanism with the aim of obtaining a design that is insensitive to variations in manufacturing tolerance and joint clearance. The contribution of each control factor in the mechanism to the variations was also quantified. From the analysis of the response table and diagram, it was concluded that the dimension r₁, r₂, r₄ and β had a significant effect on the quality of the mechanism, contributing a total of 82.36% to product variation and were consequently named the key dimensions. The tolerance of these factors must therefore be tightened to improve the quality of the mechanism. Through a series of confirmation experiments, it was revealed that tightening the tolerance resulted in an increase in the S/N (signal to noise) ratio by 1.851 db and a reduction in product variation by 19.25% from the original 80.75%. The proposed method does not require complex mathematical derivatives, but simply the input and output relationship of the system. The method proposed in this study can be applied to all types of mechanism.

The present work aims at numerical approximation in combination with experimental validation of some of the important performance measures in turning of commercially pure titanium (CP-Ti) with uncoated carbide inserts. A three-dimensional (3D) finite element model was developed based on Lagrangian criterion. Simulation of the turning operation was performed using DEFORM 3D software in order to approximate the responses viz. feed force ($F_x)$, radial force ($F_y)$, tangential force ($F_z)$, flank wear (Vb) and machining temperature ($T_m)$. Usui’s tool wear model was used to predict the flank wear. Morphology of the free and back surfaces of the chips was examined under a field emission electron microscope (FESEM). Turning experiments were carried out on a heavy duty lathe equipped with a 3D dynamometer. Secondly, a quadratic model was acquired for all the aforementioned quality characteristics using response surface methodology (RSM). Analysis of variance (ANOVA) test was performed to confirm the adequacy of the developed quadratic model. The results obtained from simulation and quadratic model were compared with the experimental data sets. Finally, an error analysis was done to determine the percentage inaccuracy of both the models. The percentage error for all the turning responses, was observed within 6% which showed the satisfactoriness of the proposed approximation tools. However, the simulation model exhibited lower prediction error when compared with the quadratic counterpart.

The wear behavior of untreated and WC-12% Co laser alloyed nodular cast iron samples were analyzed using a high temperature pin-on-disc tribometer. Response surface methodology (RSM) based Box–Behnken technique was employed to minimize the sum of trials and to develop a mathematical model for the input process parameters namely sliding velocity, temperature and load. The interaction effects of the process parameters were investigated using the developed model. Further, the developed model was analyzed using analysis of variance. Scanning electron microscopy and energy dispersive spectrum were used for characterization. Laser alloyed samples showed a minimum wear rate ($0.3379\times 10^{-3}$ to $0.9537\times 10^{-3}$mm³/m) and friction coefficient (0.10 to 0.39) compared to untreated samples. The results of the validation test showed that the experimental results agreed well with predicted results of the developed mathematical model.

The AA7075 composites reinforced with 3, 6, and 9 volume fractions (vol.%) of ZrB₂ particles have been successfully fabricated using in situ fabrication method. In this technique, the inorganic salts K₂ZrF₆ and KBF₄ were added to the molten aluminum and retained the melting at 900^∘C to form the required vol.% of ZrB₂ particles. The aluminum-reinforced metal matrix composites (MMCs) are commonly known as aluminum matrix composites (AMCs). This work carries the effects of various wire electrical discharge machining (WEDM) input process parameters like pulse-on-time ($T_{\mathrm{\text{on}}}$), pulse-off-time ($T_{\mathrm{\text{off}}}$), wire feed rate (WFR), and gap voltage (GV) on the output responses such as material removal rate (MRR), surface roughness (SR), and Kerf-width ($K_W$) of the fabricated AA7075/(3, 6, and 9) vol.% ZrB₂in situ AMCs. Based on Taguchi’s L9 orthogonal array method, the design of experiments (DoE) was used to carry out the operations on the fabricated AMCs. The optimal level of process parameters was found by the main effects plot for signal-to-noise (S/N) ratio, contour plots, and the analysis of variance (ANOVA). The effect of these process parameters on MRR and SR was analyzed and it is found that the maximum MRR was attained in the optimal parametric combination of GV $=60$V; $T_{\mathrm{\text{on}}}=4$$\mu$s; $T_{\mathrm{\text{off}}}=6$$\mu$s; WFR $=12$mm/s; Reinforcement $=3$ vol.%, and the minimum SR was attained in the optimal parametric combination of GV $=60$V; $T_{\mathrm{\text{on}}}=2$$\mu$s; $T_{\mathrm{\text{off}}}=9$$\mu$s; WFR $=8$mm/s; Reinforcement $=3$ vol.%.

Spectral power (SP) of EEG alpha and beta-2 frequencies in different cortical areas has been used for neurofeedback training to control a graphic interface in different scenarios. The results show that frequency range and brain cortical areas are associated with high or low efficiency of voluntary control. Overall, EEG phenomena observed in the course of training are largely general changes involving extensive brain areas and frequency bands. Finally, we have demonstrated EEG patterns that dynamically switch with a specific feature in different tasks within one training, after a relatively short period of training.

Flux-assisted tungsten inert gas welding process, also known as activated tungsten inert gas (A-TIG) welding, is extensively used in order to improve the performance of the conventional TIG welding process. In this study, the orthogonal array Taguchi (OA-Taguchi) method, regression modeling, analysis of variance (ANOVA) and simulated annealing (SA) algorithm have been used to model and optimize the process responses in A-TIG welding process. Welding current (I), welding speed (S) and welding gap (G) have been considered as process input variables for fabricating AISI316L austenitic stainless steel specimens. Depth of penetration (DOP) and weld bead width (WBW) have been taken into account as the process responses. In this study, SiO₂, nano-particle has been considered as an activating flux. To gather required data for modeling, statistical analysis and optimization purposes, OA-Taguchi based on the design of experiments (DOE) has been employed. Then the process responses have been measured and their corresponding signal-to-noise (S/N) ratio values have been calculated. Different regression equations have been applied to model the responses. Based on the ANOVA results, the most fitted models have been selected as an authentic representative of the process responses. Furthermore, the welding current has been determined as the most important variable affecting DOP and WBW with 68% and 88% contributions, respectively. Next, the SA algorithm has been used to optimize the developed models in such a way that WBW is minimized and DOP is maximized. Finally, experimental performance evaluation tests have been carried out, based on which it can be concluded that the proposed procedure is quite efficient (with less than 4% error) in modeling and optimization of the A-TIG welding process.

Magnetic field-assisted electrochemical spark drilling (MA-ECSD) is a cost-effective triplex hybrid machining technique that has been developed to enhance the machining depth and surface roughness of insulated and hard-to-scribe materials. The study presented adopts a reformed approach for creation of dynamic magnetic field during drilling of Sodalime glass where a 34 AWG copper wire coiled electromagnet has been installed in the in-house designed and fabricated setup of MA-ECSD. The experimental plan is based on Box–Behnken design (BBD) of Response Surface Methodology (RSM) and significance of parameters is determined using ANOVA. Multi-objective optimization (MOO) is performed by applying Grey Relational Analysis (GRA). A noncontact optical profilometer measures the machining depths and surface roughness of drilled holes. The installed electromagnet generated dynamic magnetic field intensity (MFI) ranging between 0.00 and 0.18 Tesla. Preliminary experiments were conducted to select and set the range of input parameters. Significant effect of voltage, NaOH concentration and MFI on machining depth and surface roughness is found and optimal parameter settings obtained are 24V, 30wt% and 0.09 Tesla. Machining depth increased by about 13.03% with rise in voltage-NaOH concentration and surface roughness improved by 25.3% with elevation in voltage-MFI. Dynamic MFI generated from electromagnet helped in smooth motion of electrolyte in the fine space amidst cathode and glass slide due to magnetohydrodynamic effect (MHD) which resulted in enhanced machining depth and surface roughness. The experimental and predicted results obtained after confirmatory test are appreciable which is evident from SEM images and images obtained from Optical profilometer.

We consider the testing problem in a fixed-effects functional analysis of variance model. We test the null hypotheses that the functional main effects and the functional interactions are zeros against the composite nonparametric alternative hypotheses that they are separated away from zero in L²-norm and also possess some smoothness properties. We adapt the optimal (minimax) hypothesis testing procedures for testing a zero signal in a Gaussian "signal plus noise" model to derive optimal (minimax) non-adaptive and adaptive hypothesis testing procedures for the functional main effects and the functional interactions. The corresponding tests are based on the empirical wavelet coefficients of the data. Wavelet decompositions allow one to characterize different types of smoothness conditions assumed on the response function by means of its wavelet coefficients for a wide range of function classes. In order to shed some light on the theoretical results obtained, we carry out a simulation study to examine the finite sample performance of the proposed functional hypothesis testing procedures. As an illustration, we also apply these tests to a real-life data example arising from physiology. Concluding remarks and hints for possible extensions of the proposed methodology are also given.

The freight logistics includes all the processes needed to supply industry, retailers and wholesalers and final customers with goods. Such processes generate a flow of goods that, in the global supply chain, mainly relies on the activities carried out within worldwide container terminals. In this paper, the authors present a simulation model of a real container terminal. After some preliminary analyses, the simulation model is first used with Design of Experiments and Analysis of Variance to investigate the effects of different resources allocations (i.e., number of forklifts and tractors) and some parameters (i.e., inter-arrival times, container unloading time) on the container terminal performances in terms of total number of handled containers per day. Then, based on the results achieved through the Design of Experiments and Analysis of Variance, the simulation model is used with genetic algorithms to carry out a range allocation optimization on berth assignment to incoming ships and number of tractors serving each quay crane. The aim of the optimization is the minimization of the average time spent by each ship in the port area (decreasing, as consequence, costs and increasing service level provided to final customers).

This article proposes a computer-aided diagnostic system for feature-based selection classification (CAD-FSC) to detect arrhythmia, atrial fibrillation and normal sinus rhythm. The CAD-FSC methodology encompasses of ECG signal processing phases: ECG pre-processing, R-peak detection, feature extraction, feature selection and ECG classification. Digital filters are used to pre-process the ECG signal and the R-peak is detected by using the Pan-Tompkin’s algorithm. The heart rate variability (HRV) features are extracted in time and frequency domains. Among them, the prominent features are selected with analysis of variance (ANOVA) using Statistical Package for the Social Sciences (SPSS) tool. Cubic support vector machine (C-SVM), coarse Gaussian support vector machine (CG-SVM), cubic k-nearest neighbor (C-kNN) and weighted k-nearest neighbor (W-kNN) classifiers are utilized to validate the CAD-FSC system for three-stage classification. The C-SVM outperforms all other classifiers by giving higher overall accuracy of 98.4% after feature selection of time domain and frequency domain.

This research describes a strategy for evaluating the measurement uncertainty of the feature dimensions and forms of products using a coordinate measuring machine, which is widely used in manufacturing industry. The proposed strategy outputs a task-specific measurement uncertainty, where the task includes the specification of the distribution of measurement points. Being executable with a small number of measurement trials, the strategy is practical for industrial use.

In this chapter, we consider the use of dummy variables or indicator variables as explanatory variables, and show how they are used in financial pricing.