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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 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 purpose of the study is to establish a predictive model for sustainable wire electrical discharge machining (WEDM) by using adaptive neuro fuzzy interface system (ANFIS). Machining was done on Titanium grade 2 alloy, which is also nicknamed as workhorse of commercially pure titanium industry. ANFIS, being a state-of-the-art technology, is a highly sophisticated and reliable technique used for the prediction and decision-making.

Keeping in the mind the complex nature of WEDM along with the goal of sustainable manufacturing process, ANFIS was chosen to construct predictive models for the material removal rate (MRR) and power consumption (Pc), which reflect environmental and economic aspects. The machining parameters chosen for the machining process are pulse on-time, wire feed, wire tension, servo voltage, servo feed and peak current.

The ANFIS predicted values were verified experimentally, which gave a root mean squared error (RMSE) of 0.329 for MRR and 0.805 for Pc. The significantly low RMSE verifies the accuracy of the process.

ANFIS has been there for quite a time, but it has not been used yet for its possible application in the field of sustainable WEDM of titanium grade-2 alloy with emphasis on MRR and Pc. The novelty of the work is that a predictive model for sustainable machining of titanium grade-2 alloy has been successfully developed using ANFIS, thereby showing the reliability of this technique for the development of predictive models and decision-making for sustainable manufacturing.

This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated.

The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing.

The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors.

The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status.

This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results.

Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness.

This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

An experimental investigation for developing structure-property correlations of hot-rolled E410 steels with different carbon contents, i.e. 0.04wt.%C and 0.17wt.%C metal active gas (MAG) and cold metal transfer (CMT)-MAG weldments was undertaken.

Mechanical properties and microstructure of MAG and CMT-MAG weldments of two E410 steels with varying content of carbon were compared using standardized mechanical testing procedures, and conventional microscopy.

0.04wt.%C steel had strained ferritic and cementite sub-structures in blocky shape and large dislocation density, while 0.17wt.%C steel consisted of pearlite and polygonal ductile ferrite. This effected yield strength (YS), and microhardness being larger in 0.04wt.%C steel, %elongation being larger in 0.17wt.%C steel. Weldments of both E410 steels obtained with CMT-MAG performed better than MAG in terms of YS, ultimate tensile strength (UTS), %elongation, and toughness. It was due to low heat input of CMT-MAG that resulted in refinement of weld metal, and subzones of heat affected zone (HAZ).

A substantial improvement in YS (∼9%), %elongation (∼38%), and room temperature impact toughness (∼29%) of 0.04wt.%C E410 steel is achieved with CMT-MAG over MAG welding. Almost ∼10, ∼12.5, and ∼16% increment in YS, %elongation, and toughness of 0.17wt.%C E410 steel is observed with CMT-MAG. Relatively low heat input of CMT-MAG leads to development of fine Widmanstätten and acicular ferrite in weld metal and microstructural refinement in HAZ subzones with nearly similar characteristics of base metal.

Community centers play a socio-economic and urban role of combining different communal necessities, that serve inhabitants, at different neighborhoods in cities. Their role emerged in importance as being a hub for improving and customizing quality of life experiences of the public. This research presents a code-based risk assessment tool for evaluating fire safety measures that can be adapted in the context of community centers. It also provides an exemplary case study to demonstrate its application.

The study identified the factors that render community centers as a high-risk type of facilities in fire events. Various fire codes and standards were reviewed to describe the relevant fire safety measures. A code-based fire risk assessment tool was developed and implemented, through a case study. A set of recommendations were developed to improve the fire safety conditions of the case study facility.

Several violations to fire safety were identified in the case study building. The findings led to identifying a set of recommendations to improve its fire safety conditions.

This research introduced a systematic approach to raise awareness about fire incidences and consequences in community centers, and provides facilities managers with a tool, to assess compliance based on international fire code requirements.

In fire events, community centers are considered as high-risk facilities that may lead to significant losses of human lives and damages to assets. It is significant to study the causes of fire, for ensuring effective prevention and safe operations.

To develop electrode materials for supercapacitor with superior electrochemical performance and simple preparation process, the purpose of this study is to prepare flexible CC/NiS/a-NiS electrodes with self-supporting structure by loading hydrothermally synthesized a-NiS particles along with nano-NiS on carbon cloth by electroplating method.

The effects of current densities, temperatures and pH values on the loading amount and uniformity of the active substances during the plating process were investigated on the basis of optimization of surface morphology, crystalline structure and electrochemical evaluation as the cyclic voltammetry curves, constant current charge–discharge curves and AC impedance.

The a-NiS particles on CC/NiS/a-NiS were mostly covered by the plated nano-NiS, which behaved as a bulge and provided a larger specific surface area. The CC/NiS/a-NiS electrode prepared with the optimized parameter exhibited a specific capacitance of 115.13 F/g at a current density of 1 A/g and a Coulomb efficiency of 84% at 5 A/g, which is superior to that of CC/NiS electrode prepared by electroplating at a current density of 10 mA/cm², a temperature of 55°C and a pH of 4, demonstrating its fast charge response of the electrode and potential application in wearable electronics.

This study provides an integrated solution for the development of specifically structured NiS-based electrode for supercapacitor with simple process, low cost and high electrochemical charge/discharge performance, and the simple and easy-to-use method is also applicable to other electrochemically active composites.

There is a growing urgent concern in involving higher education institutions (HEIs) towards an international effort in implementing a more suitable role as conductors of sustainable development. This paper/study aims to present the application of light emiting diode (LED) technology in an HEI assuming technology innovation as part of a larger institutional innovation management strategy addressing multiple dimensions in sustainability.

Investments in LED technology are analyzed for their impact on consumption results and quantitative comparisons between 2008 and 2022 are impaired with detailed information on the types of luminaires and the amount of installed energy.

The collected data shows a clear economic advantage of using LED technology, and the results subsidize institutional planning, considering not only ongoing technological innovation, but also educational components and community involvement in the organization sustainability strategy.

The study is limited to a specific HEI and further comparative research should be carried out.

A holistic approach on sustainability objectives encourages further investment in environmental-friendly technology, example to other HEIs.

The strategic vision of innovation is confirmed with the involvement of the community, at various levels, such as the academic community, local community, scientific international community.

This study addresses the lack of examples in the literature of structural planning and management systems that see sustainability as a strategy built in HEIs. The elaboration of an environmental sustainability plan places environmental sustainability at the confluence of themes such as education, investigation, use of natural resources, waste separation. For each topic are listed measures, actions, environment improvements, institution improvements and their results.

Electric motor heating during biomass recovery and its handling on conveyor is a serious concern for the motor performance. Thus, the purpose of this paper is to design and develop a hardware prototype of master–slave electric motors based biomass conveyor system to use the motors under normal operating conditions without overheating.

The hardware prototype of the system used master–slave electric motors for embedded controller operated robotic arm to automatically replace conveyor motors by one another. A mixed signal based embedded controller (C8051F226DK), fully compliant with IEEE 1149.1 specifications, was used to operate the entire system. A precise temperature measurement of motor with the help of negative temperature coefficient sensor was possible due to the utilization of industry standard temperature controller (N76E003AT20). Also, a pulse width modulation based speed control was achieved for master–slave motors of biomass conveyor.

As compared to conventional energy based mains supply, the system is self-sufficient to extract more energy from solar supply with an energy increase of 11.38%. With respect to conventional energy based \ of 47.31%, solar energy based higher energy saving of 52.69% was reported. Also, the work achieved higher temperature reduction of 34.26% of the motor as compared to previous cooling options.

The proposed technique is free from air, liquid and phase-changing material based cooling materials. As a consequence, the work prevents the wastage of these materials and does not cause the risk of health hazards. Also, the motors are used with their original dimensions without facing any leakage problems.