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In the current context, an attempt is being made to improve the electrical discharge machining (EDM) process by using powder particles in a suitable combination. To improve the quality of such procedures, the process parameters should be optimized. The present study proposes to utilize Taguchi–Grey relational analysis to discover the optimal combination of process parameters for SKD61 die steel specimens using titanium powder-mixed EDM (PMEDM). Among the machining parameters chosen, the optimal combination of current (3 A), pulse on-time (37 μs), pulse off-time (37 μs), and powder concentration (4 g/L) was determined experimentally. Due to its relevance in spark energy production, peak current is a more significant factor in PMEDM processes. A superior surface topography was obtained with increased microhardness and fewer microfractures over machined specimens with optimal process parameter in PMEDM. The titanium particles can effectively enhance the surface performance measures during PMEDM-based machining owing to tiny craters and pores with better lubrication.

With modern production, Minimum Quantity Lubricant (MQL) technology has emerged as an alternative to conventional liquid cooling. The MQLs is an environmentally friendly lubricant method with low cost while meeting the requirements of machining conditions. In this study, the experimental and analytical results show that the obtained acoustic emission (AE) and vibration signal components can effectively monitor various circumstances in the SCM440 steel turning process with MQL, such as surface quality and chip formation as cutting tool conditions. The AE signals showed a significant response to the tool wear processes. In contrast, the vibration signal showed an excellent ability to reflect the surface roughness during turning with MQL. The chip formation process through the cutting mode parameters (cutting speed, feed and depth of cut) was detected through analysis amplitude of the vibration components Ax, Ay and Az and the AE signal. Finally, Gaussian process regression and adaptive neuro-fuzzy inference systems (GPR-ANFIS) algorithms were combined to predict the surface quality and tool wear parameters of the MQL turning process. Tool condition monitoring devices assist the operator in monitoring tool wear and surface quality limits, stopping the machine in case of imminent tool breakage or lower surface quality. With the unique combination of AE and vibration analysis model and the training and testing samples established by the experimental data, the corresponding average prediction accuracy is 97.57 %. The highest prediction error is not more than 3.8 %, with a confidence percentage of 98 %. The proposed model can be used in industry to predict surface roughness and wear of the tools directly during turning

The primary goal of this paper is to study a nonlinear fuzzy fractional dynamic system (FFDS) involving a time-dependent variational inequality. We use the monotone argument and Knaster–Kuratowski–Mazurkiewicz (KKM) theorem to prove that the variational system of FFDS is solvable and its solutions become a bounded, closed and convex set. Employing this result together with Bohnenblust–Karlin fixed point theorem and Filippov implicit function, we show the existence of a mild solution to FFDS.

In electrical discharge machining (EDM), the productivity and machined surface quality is directly influenced by the properties of the electrode surface material layer used. In the present study, a research attempt was made to find the optimal technological parameters in EDM process with AlCrNi coated electrode using Taguchi-Data Envelopment Analysis based Ranking (DEAR) based multi-response optimization approach. From the experimental investigation, the optimal technological parameter combination in EDM using AlCrNi coated electrode was found as peak current (40 A), voltage (55 V), and pulse-on-time (1000 μs) on machining Ti-6Al-4V titanium alloy. The current was found as more principal factor in EDM process with coated tool due to the electrical conductance of the tool coating. The better surface topography with lower surface roughness and micro cracks can be obtained on the machined specimens with proposed parameter combination.

In this study, rupture of notched SENT specimens fabricated from a novel lead-free solder alloy is investigated. The lead-free solder alloy, focused on in this study, is particularly used as interconnect material in power modules of electric vehicles. Its commercial denomination is InnoLot and it can be used in harsh environments thanks to its improved reliability. Up to now, studies on their resistance to rupture remain relatively limited. Yet the comprehension of fracture behavior is essential for the correct design of the electronic packages which must be robust against fatigue and vibrations loads. The tests are performed with the help of a micro-tensile testing machine equipped with an optical system for full-field measurements with Digital Image Correlation. The images are taken at successive steps of deformation and the displacement field is measured in a region of interest which is the singularity dominated zone surrounding the plastic zone at the crack tip. The procedure consists then in comparing the measured field with the theoretical field given by the Williams' solution. The stress intensity factor is calculated by fitting the analytical fields to the experimental data. The effects of the size and shape of the zone of data collection, as well as that of the number of terms considered in the Williams's expansion series, are examined in the study. A method is also proposed for the automatic crack tip detection. From these finding, it is easy to predict the crack propagation and failure mechanism of solder joint. In addition, the theoretical solution of displacement, given by the Williams series, is compared with measurements to identify the coefficients of these series, including the stress intensity factor. Finally, a 5-order truncation of the Williams series seems sufficient to obtain a correct estimate of the stress intensity factor.

This paper presents a study of the roughness prediction of the workpiece when using CBN (Cubic Boron Nitride) grinding wheel to process C45 steel on a surface grinder. On the basis of inheriting the previously published studies on modeling surface roughness of workpiece when grinding, this study was conducted to develop one of these models and build a new model of surface roughness that consider many factors affecting surface roughness. Cutting parameters, parameters of grinding wheel, characteristics of materials, characteristics of contact of grinding wheel with workpiece have been included in the roughness model of the surface roughness of workpiece. This new surface roughness model was used to predict surface roughness of workpiece when grinding C45 steel with CBN grinding wheel. The results showed that the surface roughness value when predicting was very suitable for the experiment and the average deviation between the predicted results and the experimental results was only about 9.84%. The surface roughness model proposed in this study also confirms that the roughness can be predicted more accurately than the previous surface roughness models. And then, the development direction for further research is also mentioned in this paper.

In this paper, undisturbed specimen of a silty clay constituting of Phu Bai formation (ambQ21-2 pb) was collected from boreholes in Hue city and surrounding areas. The soil, under both undisturbed and disturbed conditions, was then subjected to standard one-dimensional consolidation tests with 7 loading increments. It is shown from the experimental results that the time to the end of primary consolidation (EOP), determined by Log Time method (tLT) and 3-t method (t3T), decreases with the load increment and under the same vertical stress, the primary consolidation of disturbed silty clay finish at a shorter time than those of the undisturbed one. The coefficient of secondary consolidation (Cα) increases with the vertical stress and reaches the maximum values before decreasing. The obtained values of Cα = 0.005 - 0.020 suggest a relatively low secondary compressibility of the silty clay constituting of Phu Bai formation.

The aim of the study is to predict the cutting force when surface milling with a face milling tool. Based on theoretical published research on cutting force modeling, this study predicted the cutting force when milling 40Cr steel with a face milling tool. The results show that the deviation of predicted cutting force was approximately 15.7% in comparison with the test, and about 9.9% compared to the predicted value using regression model, while the cutting force predicted using regression model deviated 9.6% from the test results. Also, development directions for further studies are suggested.

This paper presents a study on the prediction of surface roughness in surface grinding. Based on the results of several previous studies, the relation between the abrasive grain tip radius and the standard systems of grinding wheels and the surface roughness were predicted. Also, the surface roughness when surface grinding SKD11, SUJ2, and 3X13 steel by Al2O3 and CBN grinding wheels was anticipated. The predicted surface roughness values were found to be close to the experimental values. In addition, the average deviation between the predictive results and the experimental results was 15.11% for the use of Al2O3 grinding wheels and 24.29% for the case of using CBN grinding wheels.