Chatter is a type of self-induced vibration that reflects fluctuations in both frequency and energy dispersion during the milling process, inevitably resulting in substandard part quality and diminished material removal rates. It is essential to employ a robust chatter detection method to anticipate its emergence in the early stages. This study introduces an efficient Product Function (PF) based multi-mode signal processing technique, specifically the spline-based local mean decomposition (SBLMD). This method is applied to decompose sound signals acquired through experimentation into a series of effective PF’s. Subsequently, selected PFs are employed to reconstruct a new chatter signal that is information-rich. Additionally, prediction models based on Artificial Neural Networks (ANN) are established to predict Chatter Indicator (CI) and Material Removal Rate (MRR) using three different activation algorithms: Tan Sigmoid (TANSIG), Log Sigmoid (LOGSIG), and Purely Linear (PURELIN). Statistical comparisons have been conducted in order to obtain the optimal activation algorithm and found out that data set trained with LOGSIG gives minimal error. Moreover, an optimal range of input parameters has been selected pertaining to minimum chatter and maximum MRR. Confirmation tests on the obtained set of parameters have been carried out in order to analyse and authenticate the proposed technique.

This paper presents a model to predict Material Removal Rate (MRR) in Micro Ultrasonic Machining (micro-USM). The proposed model is developed based on the ductile-mode of material removal in micro-USM process. The correlation between ductile material removal rate and process parameters including frequency and amplitude of the ultrasonic vibration, particle size, and slurry concentration is presented. The proposed predictive model is verified by performing micromachining experiments using two types of workpiece materials including silicon and quartz at various process parameters levels. The results show that the MRR increases with a rise in vibration amplitude for both silicon and quartz materials. The experimental MRR values follow a trend similar to that of predicted MRR values. However, the predicted MRR values are higher than the measured MRR values for both silicon and quartz materials. The measured MRR values for ductile removal mode were found to have a considerable increase at vibration amplitudes of 2  m and 2. 4  m for silicon and quartz, respectively, which is in favour of increasing the accuracy of the model prediction.

In this study, the prediction capabilities of the adaptive neuro-fuzzy inference system (ANFIS) and the particle swarm optimization (PSO)-based ANFIS were compared. First, the material removal rate (MRR), surface roughness (Ra), and tool wear ratio (TWR) were modeled using the ANFIS technique during the ultrasonic-assisted electrical discharge machining (US/EDM) process. The ANFIS model was developed to predict these output parameters, and subsequently, its parameters were optimized using PSO to reduce prediction error. The pulse-on time (Ton) and current (I) were selected as input factors. The models were trained, tested, and validated with experimental data, and statistical analyses were conducted to evaluate the effectiveness of both ANFIS and ANFIS–PSO approaches. The greatest reduction in the average prediction error percentage was observed for MRR, decreasing from 10.87% in the ANFIS model to 6.19% in the ANFIS–PSO model. Overall, the experimental results demonstrated that the ANFIS–PSO algorithm provided superior performance in estimating machining parameters compared with the conventional ANFIS model.

In this study, three series of electrical discharge machining experiments were carried out on γ-TiAl intermetallic. In the first series, by changing pulse current and pulse on time in EDM process and in the second series by changing the size and concentration of aluminum powder particles mixed in dielectric fluid, output characteristics including material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) are evaluated. In the third series of machining experiments, after determination of the optimum size and concentration of aluminum particles, current and pulse on time, are changed at levels as exactly the same levels in powder-free tests and MRR is evaluated. Finally, MRR of the EDM and optimum PMEDM processes, are compared. By increasing the aluminum particles concentration, TWR and SR will be decreased to an optimal level and then will be increased. For particle size of 2 μ m and 20 μ m, by increasing the aluminum powder concentration, MRR increases at first and then decreases, while for particle size of 63μ m, by increasing powder concentration, MRR decreases. The minimum and maximum increment of MRR in optimum PMEDM condition compared with EDM mode was 32% and 88%, respectively. With increase of electrical discharge energy, the addition of aluminum powder has less effect on increase of sparking frequency and consequently the MRR.

In this investigation, near dry EDM process was investigation. This process was studied at three levels of discharge energy and with two brass and copper electrode to investigate the effects of tool material on machining performance. Also, the Taguchi method of design of experiments technique was employed to study the effects of process parameters such as tool rotational speed, liquid flow rate, gas pressure and also discharge energy on material removal rate (MRR), electrode wear ratio (TWR) and surface roughness (SR) and also the analysis of variance (ANOVA) was employed to find the most important factors effecting MRR, TWR and SR. In addition, the optimum of MRR, TWR and SR was found by Taguchi method. The results showed that copper electrode has higher MRR and lower TWR as compared to brass electrode. Also the analysis of main effect plots obtained by Taguchi method indicated that MRR and SR is enhanced by increasing water flow rate and discharge energy and also increasing gas pressure leads to lower TWR. The ANOVA results showed that discharge energy is the most important factor influencing MRR, TWR and SR.

Background: Duane’ s retraction syndrome is a congenital eye movement anomaly with narrowing of the palpebral fissure and globe retraction on attempted adduction. There are several surgical approaches to treat the narrowing of the palpebral fissure. The purpose of the present study was to evaluate the efficacy of unilateral medial rectus recession (MRR) muscle combined lateral rectus (LR) muscle marginal myotomy (MM) with unilateral MRR alone in the management of narrowing of the palpebral fissure of patients with Type 1 Duane’ s retraction syndrome (DRS). Materials and Methods: Twenty‑ eight patients with unilateral DRS Type 1 were randomly divided into two groups (14 eyes of 14 patients in each group). Age ≥ 5 years with DRS Type 1 with <20 prism diopters in primary position who were candidates for surgery were consecutively enrolled in this randomized controlled trial. Patients were divided into treatment groups to receive unilateral MR recession with simultaneous MM group or with unilateral MR recession alone. The amount of deviation in primary position, abnormal head position, palpebral fissure width (PFW), and up/down shoot was evaluated before and 3 months after the surgery. This study was registered at the Iranian Registry of Clinical Trials under the registration code IRCT20131229015975N3. Results: PFW increased within MRR/MM group at the end of the study (8. 86 ± 1. 51) compared with the baseline (7. 79 ± 1. 48) (P < 0. 001). In contrast, in the MRR/MM group, PFW did not increase statistically significantly within the MRR group at the end of the study (8. 14 ± 1. 35) compared with the baseline (8. 07 ± 1. 38) (P = 0. 67). Mean ± standard deviation of PFW (mm) in MRR/MM group after surgery (8. 86 ± 1. 51) was statistically significantly higher than that in the MRR group (8. 14 ± 1. 35), (P = 0. 002). Conclusion: The results of our study demonstrate PFW significantly increased after unilateral MRR muscle combined LR muscle MM.

In this study EDM milling process parameters of AISI H13, have been investigated by using Response Surface Methodology (RSM). Current (16-32A), pulse-on time (100-700 ms) and depth of cut (1-3mm) were considered as independent variables, while surface roughness, tool wear ratio (TWR), and material removal rate (MRR) as process output responses. Results reveal that increases in the current and decreases in pulse-on time cause more MRR and more TWR and depth of cutting has no significant effect on them. Minimum surface roughness, minimum TWR and maximum MRR were considered as optimization criteria. Verification experiments were carried out in order to analyze the results via software. Optimized settings were used for EDM Milling and die sink EDM experiments to compare the results. The results indicate that using EDM milling has considerable economic savings than die sink EDM, better surface roughness, and higher MRR.

The present study is focused to investigate the effect of the various machining input parameters such as cutting speed (vc), feed rate (f), depth of cut, and nose radius (r) on output i.e. surface roughness (Ra and Rq) and metal removal rate (MRR) of the C40 steel by application of an artificial neural network (ANN) method.  ANN is a soft computing tool, widely used to predict, optimize the process parameters. In the ANN tool, with the help of MATLAB, the training of the neural networks has been done to gain the optimum solution. A model was established between the computer numerical control (CNC) turning parameters and experimentally obtained data using ANN and it was observed from the result that the predicted data and measured data are moderately closer, which reveals that the developed model can be successfully applied to predict the surface roughness and material removal rate (MRR) in the turning operation of a C40 steel bar and it was also observed that lower the value of surface roughness (Ra and Rq) is achieved at the cutting speed of 800 rpm with a feed rate of 0.1 mm/rev, a depth of cut of 2 mm and a nose radius of 0.4 mm.