Experimental investigation of mechanical properties and multi-objective optimization of electronic, glass, and ceramic waste-mixed concrete.

The utilization of waste from various sources plays an important role in minimizing environmental pollution and civil construction costs. In this research, the mechanical properties of concrete were studied by mixing electronic waste (EW), glass powder (GW), and ceramic tile waste (CW). The effects of weight percentages of EW, GW, and CW are considered to investigate improvements in mechanical properties such as compressive strength (CS), split tensile strength (STS), and flexural strength (FS) of concrete. Taguchi analysis has been applied to predict the optimum composition of waste mixing percentages. The Multi-Objective Optimization Ratio Analysis (MOORA) techniques are applied to estimate the optimum composition of mixing wastes for maximizing the CS, STS, and FS of concrete. It was observed that 10 wt.% of EW, 15 wt.% of GW, and 30 wt.% of CW are predicted as the optimal mixing combinations to obtain a maximum compressive strength of 48.763 MPa, a split tensile strength of 4.178 MPa, and a flexural strength of 7.737 MPa, respectively. Finally, the predicted optimum waste-mixed weight percentages were used to examine the microstructure and various elements in the concrete using SEM and XRD analysis. When compared to conventional concrete, the optimum waste-mixed concrete has improved its compressive strength (38.453%), split tensile strength (41.149%), and flexural strength (36.215%).

Characteristics estimation of natural fibre reinforced plastic composites using deep multi-layer perceptron (MLP) technique.

Polymer Matrix Composite (PMC/Plastic Composite) often referred to as Plastic Composite with Natural fibre reinforcement has a huge interest in industries to manufacture components for various applications including medical, transportation, sports equipment etc. In the universe, different types of natural fibres are available which can be used for the reinforcement in PMC/Plastic Composite. So, the selection of appropriate fibre for the PMC/Plastic Composite/Plastic composite is a challenging task, but it can be done using an effective metaheuristic or optimization techniques. But in this type of optimal reinforcement fibre or matrix material selection, the optimization is formulated based on any one of the parameters of the composition. Hence to analyse the various parameter of any PMC/Plastic Composite/Plastic Composite without real manufacturing, a machine learning technique is recommended. The conventional simple or single-layer machine learning techniques were not sufficient to emulate the exact real-time performance of the PMC/Plastic Composite. Thus, a deep multi-layer perceptron (Deep MLP) algorithm is proposed to analyse the various parameter of PMC/Plastic Composite with natural fibre reinforcement. In the proposed technique the MLP is modified by including around 50 hidden layers to enhance its performance. In every hidden layer, the basis function is evaluated and subsequently, the sigmodal function-based activation is calculated. The proposed Deep MLP is utilized to evaluate the various parameters of PMC/Plastic Composite Tensile Strength, Tensile Modulus, Flexural Yield Strength, Flexural Yield Modulus, Young's Modulus, Elastic Modulus and Density. Then the obtained parameter is compared with the actual value and the performance of the proposed Deep MLP is evaluated based on the accuracy, precision, and recall. The proposed Deep MLP attained 87.2%, 87.18%, and 87.22% of accuracy, precision, and recall. Ultimately the proposed system proves that the proposed Deep MLP can perform better for the prediction of various parameters of PMC/Plastic Composite with natural fibre reinforcement.

Experimental investigation and multi-objective optimization of eco-friendly near-dry electrical discharge machining of shape memory alloy using Cu/SiC/Gr composite electrode.

The near-dry electrical discharge machining processes have been conducted using air-mist or gas mist as a dielectric fluid to minimize the environmental impacts. In this article, near-dry electrical discharge machining (NDEDM) experiments have been performed to improve machining performance using an oxygen-mist dielectric fluid, a copper composite electrode, and Cu-Al-Be polycrystalline shape memory alloy (SMA) work materials. The copper composite electrode is made up of 12 wt% silicon carbide and 9 wt% graphite particles. The oxygen-mist pressure (Op), pulse on time (Ton), spark current (Ip), gap voltage (Gv), and flow rate of mixed water (Fr) were used as process parameters, and the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) were used as performance characteristics. The global optimal alternative solution has been predicted by the PROMETHEE-II (Preference Ranking Organization METhod for Enrichment Evaluations-II) optimization technique. The best combinations of process parameters have been used to examine the microstructure of composite tools and SMA-machined surfaces by scanning electron microscopy (SEM) analysis. The best global optimum settings (oP: 9 bar, Ip: 60 µs, Ip: 12 A, Gv: 40 V, and Fr: 12 ml/min) are predicted to attain optimum machining performance (MRR: 39.049 g/min, TWR: 1.586 g/min, and SR: 1.78 µm). The tool wear rate of the NDEDM process has been significantly reduced by the copper composite electrode due to increasing microhardness, wear resistance, and melting point. When compared to the pure copper electrode tool, the MRR of NDEDM is improved to 21.91%, while the TWR and SR are reduced to 46.66% and 35.02%, respectively.

Sustainable developments in near-dry electrical discharge machining process using sunflower oil-mist dielectric fluid.

In this study, a near-dry electrical discharge machining (NDEDM) process has been conducted using compressed air mixed with a low quantity of biodegradable refined sunflower oil (called oil-mist) to investigate the machining characteristics. The Box-Behnken method looks at how oil flow rate (OR), air pressure (AR), spark current (SC), and pulse width (PW) affect gas emission concentration (GEC), material removal rate (MRR), and surface roughness (SR). The TOPSIS (The Technique for Order of Preference by Similarity to the Ideal Solution) technique estimates the parameter optimal set for the best machining characteristics. The optimal machining parameters have been used to examine the microstructure of the machined surfaces using a scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDS) analysis. The 0.981 mg/min of GEC, 55.145 mg/min of MRR, and 2.43 µm of surface roughness have been attained by the 14 ml/min flow rate, 7 bar of air pressure, 10 A spark current, and 48 µs pulse duration of the sun-flower oil-mist NDEDM process.

Cryogenically treated and untreated stainless steel grade 317 in sustainable wire electrical discharge machining process: a comparative study.

In this research, the influences of cryogenically treated stainless steel grade 317 on the eco-friendly near-dry wire-cut electrical discharge machining (NDWEDM) processes have been investigated using the minimum quantity of water mixed with oxygen gas (oxygen mist) dielectric fluid. The stainless steel grade 317 has been applied to make the various biomedical and industrial components due to its high creep strength. The wire wear ratio (WWR) and cutting rate (CR) of NDWEDM are compared using cryogenically treated and untreated work materials by Taguchi's analysis. The water flow rate, gas pressure, spark current, and pulse width had been considered as process parameters. The microstructure of wire electrode and machined surfaces of treated/untreated materials had been compared by scanning electron microscope (SEM) images. The WWR and CR of cryogenically treated materials in NDWEDM are 20.31% lower and 22.32% higher than untreated materials, respectively.

An investigation on gas emission concentration and relative emission rate of the near-dry wire-cut electrical discharge machining process.

Wire-cut electrical discharge machining (WEDM) is the highly essential unconventional electrothermal machining process to cut the contour profile in the hard materials in modern production industries. The various environmental impacting contaminants (by evaporating and reacting liquid dielectric fluid) have been produced during the conventional WEDM process and are harmful to the machine operators. These wastes have been minimized by the near-dry WEDM process in which the pressurized air mixed with a small amount of water is used as a dielectric medium. In this research, influences of machining parameters (air pressure, flow rate mixing water, spark current, and pulse width) on gas emission concentration (GEC), material removal rate (MRR), and relative emission rate (RER) of near-dry WEDM process have been optimized by the Taguchi analysis. RER has been determined to analyze the variations of gas emission concentration per unit quantity of material removal by changing the process parameters. It was revealed that the maximum air pressure and flow rate of mixing water have been predicted as significant parameters on GEC and RER. While comparing wet and near-dry WEDM processes, the material removal rate of near-dry process is comparable to that of wet WEDM with minimum GEC and RER.