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In many of today’s manufacturing industries, such as automobile, aerospace, defence, die and mould making, medical and electrical discharge machining (EDM) has emerged as an effective material removal process. In this process, a series of discontinuous electric discharges is used for removing material from the workpiece in the form of craters generating a replica of the tool into the workpiece in a dielectric environment. Appropriate selection of the tool electrode material and combination of input parameters is an important requirement for performance enhancement of an EDM process. This paper aims to optimize an EDM process using single-valued neutrosophic grey relational analysis using Cu-multi-walled carbon nanotube (Cu-MWCNT) composite tool electrode.

This paper proposes the application of grey relational analysis (GRA) in a single-valued neutrosophic fuzzy environment to identify the optimal parametric intermix of an EDM process while considering Cu-MWCNT composite as the tool electrode material. Based on Taguchi’s L9 orthogonal array, nine experiments are conducted at varying combinations of four EDM parameters, i.e. pulse-on time, duty factor, discharge current and gap voltage, with subsequent measurement of two responses, i.e. material removal rate (MRR) and tool wear rate (TWR). The electrodeposition process is used to fabricate the Cu-MWCNT composite tool.

It is noticed that both the responses would be simultaneously optimized at higher levels of pulse-on time (38 µs) and duty factor (8), moderate level of discharge current (5 A) and lower level of gap voltage (30 V). During bi-objective optimization (maximization of MRR and minimization of TWR) of the said EDM process, the achieved values of MRR and TWR are 243.74 mm³/min and 0.001034 g/min, respectively.

Keeping in mind the type of response under consideration, their measured values for each of the EDM experiments are expressed in terms of linguistic variables which are subsequently converted into single-valued neutrosophic numbers. Integration of GRA with single-valued neutrosophic sets would help in optimizing the said EDM process with the Cu-MWCNT composite tool while simultaneously considering truth-membership, indeterminacy membership and falsity-membership degrees in a human-centric uncertain decision-making environment.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

The electric discharge machining (EDM) involves electrons discharged from the electrode and machining progresses due to the removal of the material from the component. This a thermal-based machining process primarily used for hard to machine components with conventional methods. This process is used to make intricate cavities and contours. The fabricated part is the replica of the tool material with high surface finish and good dimensional accuracy. This study aims to evaluate the comprehensive effect of process parameters on electric discharge machining of maraging steel.

Multiple criteria Decision making (MCDM) techniques are used to select the best parameters by comparing several responses to achieve the desired goal. There are different MCDM techniques available for optimization of machining parameters. In the current investigation, multi-objective optimization by data envelopment analysis based ranking (DEAR) approach was used for machining Maraging C300 grade steel.

The Taguchi L9 runs were planned with process parameters such as current (Amp), Tool diameter (mm) and Dielectric pressure (MPa). The effect of process parameters on the responses, namely, material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) were evaluated. High MRR is found at 15 A current, 14 mm tool diameter and dielectric pressure of 0.2 MPa. Optimum process parameters experiment showed reduced crack density.

An effort was made successfully to enhance the responses using the DEAR method and establish the decision making of selecting the optimal parameters by comparing the results obtained by machining maraging steel C300 grade.

This study aims to analyse the air traffic control (ATC)-related accidents and incidents. The paper aims to determine technical, operational and environmental factors that influence ATC-related accidents and incidents. This is important to reduce the number of these accidents.

This predictive and dipendence study investigated situational factors indicated in the data sets of National Transportation Safety Board (NTSB) and European Union Aviation Safety Agency (EASA) (for the period 2008–2018). The specific factors were time of day in which accident occurred, location, air traffic management (ATM) contribution, flight rules, ATC unit and outside factors. Logistic regression was used to differentiate factors between fatal ATC-related accidents versus ATC-related incidents. Further, by using Pearson’s chi-squared test, significant relationships between all factors in ATC-related accidents and ATC-related incidents were identified.

The results showed that five factors of total six factors – ATM contribution, flight rules, ATC unit, outside factors and aircraft location – influence to ATC-related accidents and incidents. Further, results showed significant relationships between each all factors in ATC-related accidents and incidents. According to that, differences and similitudes are presented.

After more than 20 years, study about ATC-related accidents and incidents was necessary to establish changements in thıs type of accidents. Belong of that, this is the first study that used data sets of both, NTSB and EASA, to determine the factors that affect ATC-related accidents and incidents.

This experimental study aims to deal with the improvement of process performance of electric discharge drilling (EDD) for fabricating true blind holes in titanium alloy Ti6Al4V. Micro EDD was performed on Ti6Al4V and blind holes were drilled into the workpiece.

The effects of input parameters (i.e. voltage, capacitance and spindle speed) on responses (i.e. material removal rate, tool wear rate and surface roughness [SR]) were evaluated through response surface methodology. The data was analyzed using analysis of variance and multi-optimization was performed for the optimized set of parameters. The optimized process parameters were then used to drill deeper blind holes.

Blind holes have few characteristics such as SR, taper angle and corner radius. The value of corner radius reflects the quality of the hole produced as well as the amount of tool roundness. The optimized process parameters suggested by the current experimental study lower down the response values (i.e. SR, taper angle and corner radius). The process is found very effective in producing finished blind holes.

This experimental study establishes EDD as a feasible process for the fabrication of truly blind holes in Ti6Al4V.

The purpose of this paper is to examine the quality of the turned surface. The quality of the surface produced depends on the nature of the chips, which are produced while turning metal matrix composites. This quality is a function of the machining parameters, tool material, tool configuration and elements of the composites.

In this study, the turning of AA7075/15 wt.% SiC (particle size 20–40 µm) composites is investigated. Thirty experiments were conducted, and the chip-formation mechanism in turning AA7075/SiCp composites at various combinations of cutting speeds, feed and depth of cuts was studied.

It is observed from the response surface methodology-based experimentation that in turning of coarser reinforcement (particle size 20–40 µm) composites, total gross fracture occurs. This causes small slices of chips and a higher shear plane angle. The nature of chips produced at various combinations of cutting speeds, feed and depth of cuts is different. The chips generated were segmented, spiral in cylindrical form, connected C type, chips with saw tooth, curled chips, washer C type chips, half-curved segmented chips and small-radii segmented chips.

The novelty of this research is that, so far, very little work has been published on the detailed analysis of chips produced during turning of AA7075/15 wt.% SiC (particle size 20–40 µm) composites.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

The purpose of this study is to explore the machining characteristics of electrical discharge machining (EDM) when a tool is fabricated using powder metallurgy. Because pure Cu tools obtained using conventional machining pose problems of high tool wear rate, tool oxidation causes loss of characteristics in tool shape.

The research investigation carried out experiments planned through Taguchi’s robust design of experiments and used analysis of variance (ANOVA) to carry out statistical analysis.

It has been found that copper and chromium electrodes give less metal removal rate as compared to the pure Cu tool. Analytical outcomes of ANOVA demonstrated that MRR is notably affected by the variable’s polarity, peak current, pulse on time and electrode type in the machining of EN9 steel with EDM, whereas the variables pulse on time, gap voltage and electrode type have a significant influence on EWR. Furthermore, the process also showed that the use of powder metallurgy tool effectively reduces the value of SR of the machined surface as well as the tool wear rate. The investigation exhibited the possibility of the use of powder metallurgy electrodes to upgrade the machining efficiency of EDM process.

There is no major limitation or implication of this study. However, the composition of the powders used in powder metallurgy for the fabrication of tools needs to be precisely controlled with careful control of process variables during subsequent fabrication of electrodes.

To the best of the authors’ knowledge, this is the first study that investigates the effectiveness of copper and chromium electrodes/tools fabricated by means of powder metallurgy in EDM of EN9 steel. The effectiveness of the tool is assessed in terms of productivity, as well as accuracy measures of MRR and surface roughness of the components in EDM machining.

The world of work is constantly changing. The COVID-19 pandemic has reinforced working from home, and there is an increasing demand for flexibility regarding the workplace. There is little empirical evidence on the mechanisms and factors that influence employee outcomes, such as productivity and turnover intention, at the workplace and at home. In addition, it is unclear whether the workplace characteristics that influence employee outcomes vary between different nations due to country-specific circumstances. The paper aims to address these two issues.

The research model applied in this study is based on the job demands-resources (JD-R) and environmental demands-resources models using German (n = 429) and USA (n = 507) survey samples. Partial least squares structural equation modelling is used to analyse the influence of workplace characteristics (isolation, family–work interference, equipment/facilities and skill variety) on employee outcomes (satisfaction, burnout, productivity and turnover intention). Additionally, a multi-group analysis is used to explore group differences in the factors influencing satisfaction, burnout, productivity and turnover intention between employees in Germany and the USA.

The results reveal that significant determinants of productivity and turnover intention include isolation, family–work interference, equipment/facilities and skill variety. Isolation and equipment/facilities are identified as the most important demands and resources of the home workplace. Some significant differences are found between Germany and the USA. The positive effect of isolation on burnout is significantly stronger in the USA than in Germany, whereas the positive effect of family–work inference on burnout is stronger in Germany than in the USA. The negative effects visible for the relations between burnout and satisfaction, and satisfaction and turnover intention are stronger in Germany than in the USA. The positive effect of burnout on turnover intention is stronger in the USA compared to Germany.

The study adds empirical evidence to the JD-R theory by analysing the influence of the home workplace characteristics on employee outcomes in different countries for the first time using a multi-group analysis. In addition, the study reveals new insights into the differences between the knowledge workforces in Germany and the USA by uncovering how key factors influence employee outcomes such as productivity and turnover intention, partially carried by varying length of experience in work from home between both of these countries. Insights from this study can support corporate real estate managers to make better decisions on the design of employees’ home workplaces and the integration of work from home into the company’s workspace concept.