Optimization and Modeling of Material Removal Rate in Wire-EDM of Silicon Particle Reinforced Al6061 Composite

The mechanical, physical and interfacial properties of aluminum alloys are improved by reinforcing the silicon carbide particles (SiCp). Machinability of such alloys by traditional methods is challenging due to higher tool wear and surface roughness. The objective of research is to investigate the machinability of SiCp reinforced Al6061 composite by Wire-Electrical Discharge Machining (wire-EDM). The effect of wire-EDM parameters namely current (I), pulse-on time (Ton), wire-speed (Ws), voltage (Iv) and pulse-off time (Toff) on material removal rate (MRR) is investigated and their settings are optimized for achieving the high MRR. The experiments are designed by using Taguchi L16 orthogonal arrays. The MRR obtained at different experiments are analyzed using statistical tools. It is observed that all the chosen process parameters showed significant influence of on the MRR with contribution of 27.39%, 22.08%, 21.32%, 15.76% and 12.94% by I, Iv, Toff, Ton and Ws, respectively. At optimum settings, the Wire-EDM resulted in MRR of 65.21 mg/min and 62.41 mg/min for samples with 4% and 8% SiCp. The results also indicated reinforcing SiCp upto 8% showed marginally low influence on MRR. Microstructural investigation of the cut surface revealed the presence of craters with wave pattern on its surface. The top surface of the crater is featured by the recast layers connecting adjacent craters. Further, the statistical model is developed using linear regression to predict the MRR (?2—73.65%) and its predicting accuracy is verified by the confirmation trials. The statistical model is useful for predicting the MRR for different settings of the process parameters. The optimized settings can be used to improve the machining productivity by increasing the MRR while machining of Al6061-SiCp (upto 8 wt. %) alloy by wire-EDM industries

Characterization of Physical and Mechanical Properties of Rice Straw Particles and Furcraea foetida Fiber Reinforced Hybrid Composite

The biodegradable characteristics and abundant availability of the fiber sources have gained the attention of various industries to produce natural fiber-based composites. As a sustainable alternative to the non-biodegradable fiber-based products, the natural composites provide a viable solution to reduce the environmental pollution caused by synthetic materials. This study developed rice straw particle (RSp) and Furcraea foetida (FF) fiber reinforced hybrid composite and investigated its physical and mechanical properties. The addition of 15 wt.% of RSp reduced the density of the test samples by 41.87% and its water absorption (WA) increased with the increase in fiber concentration. The composite with 5 wt.% and 15 wt.% of RSp showed maximum tensile strength (σt: 29.45 MPa) and modulus (σtm: 3.67 GPa), respectively. At 15 wt.% of RSp, the maximum flexural strength (σf: 43.12 MPa) and modulus (σfm: 2.09 GPa) was achieved and at 10 wt.% of RSp showed the highest impact strength (σi: 101.01 J/m). The σt (40.21%) and σf (7.76%) of the RSp reinforced composite were improved by the hybridization of FF (20 wt.%) fiber reinforcement

Influence of Chemical Treatments on the Physical and Mechanical Properties of Furcraea Foetida Fiber for Polymer Reinforcement Applications

The usage of natural fibers is regarded as the most viable solution for controlling the consumption of synthetic materials. However, the low moisture resistance, stiffness and poor adhesion capabilities restrict their use in several advanced composite applications. Hence, different surface treatments are employed to improve the physical characteristics of natural materials. In this study, the effect of chemical treatments (sodium hydroxide, acetic acid, potassium permanganate and bromodecane) on the surface characteristics and the adhesion capabilities of Furcraea foetida (FF) fiber with epoxy material is evaluated. From the study, it is seen that the chemical treatment (CT) eliminates the O-H functional groups and enhances the hydrophobic characteristics in FF fiber. Furthermore, the CT removes the amorphous organic attachments and improves the crystallinity in the fiber. However, no substantial increase in the thermal stability of FF fiber was observed post chemical treatment. The microscopic analysis of chemically treated FF (CTFF) fiber shows the elimination of organic attachments and developed neat uniform surface structure. The NaOH-treated FF fiber exhibited maximum tensile strength (σt: 241.75 MPa). Whereas the acetic acid-treated FF fiber showed maximum tensile modulus (σm: 6.9 GPa) and interfacial shear strength (IFSS: 0.06 MPa) compared to CTFF and untreated FF fiber (UTFF)

An Investigation of Abrasive Water Jet Machining on Graphite/Glass/Epoxy Composite

In the present research work, the effect of abrasive water jet (AWJ) machining parameters such as jet operating pressure, feed rate, standoff distance (SOD), and concentration of abrasive on kerf width produced on graphite filled glass fiber reinforced epoxy composite is investigated. Experiments were conducted based on Taguchi’s L27 orthogonal arrays and the process parameters were optimized to obtain small kerf. The main as well as interaction effects of the process parameters were analyzed using the analysis of variance (ANOVA) and regression models were developed to predict kerf width. The results show that the operating pressure, the SOD, and the feed rate are found to be significantly affecting the top kerf width and their contribution to kerf width is 24.72%, 12.38%, and 52.16%, respectively. Further, morphological study is made using scanning electron microscope (SEM) on the samples that were machined at optimized process parameters. It was observed that AWJ machined surfaces were free from delamination at optimized process parameters

Processing, Characterization of Furcraea foetida (FF) Fiber and Investigation of Physical/Mechanical Properties of FF/Epoxy Composite

In recent days the rising concern over environmental pollution with excessive use of synthetic materials has led to various eco-friendly innovations. Due to the organic nature, abundance and higher strength, natural fibers are gaining a lot of interest among researchers and are also extensively used by various industries to produce ecological products. Natural fibers are widely used in the composite industry as an alternative to synthetic fibers for numerous applications and new sources of fiber are continuously being explored. In this study, a fiber extracted from the Furcraea foetida (FF) plant is characterized for its feasibility as a reinforcement to fabricate polymer composite. The results show that the fiber has a density of 0.903 ± 0.07 g/cm3, tensile strength (σt) of 170.47 ± 24.71 MPa and the fiber is thermally stable up to 250 °C. The chemical functional groups and elements present in the FF fiber are evaluated by conducting Fourier transform infrared spectroscopy (FT-IR) and energy dispersive spectroscopy (EDS). The addition of FF fibers in epoxy reduced the density (13.44%) and hardness (10.9%) of the FF/Epoxy (FF/E) composite. However, the void content (Vc < 8%) and water absorption (WA: < 6%) rate increased in the composite. The FF/E composite with 30% volume of FF fibers showed maximum σt (32.14 ± 5.54 MPa) and flexural strength (σf: 80.23 ± 11.3 MPa)

Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications

The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models

Optimization of material removal rate and surface characterization of wire electric discharge machined Ti-6Al-4V alloy by response surface method

Wire electric discharge machining (WEDM) is one of the foremost methods which has been utilized for machining hard-to-cut materials like Titanium alloys. However, there is a need to optimize their important operating parameters to achieve maximum material removal rate (MRR). The present paper investigates the effect of control factors like current, pulse on time (Ton), pulse off time (Toff) on MRR of machining of Ti-6Al-4V alloy. The study showed that, increase in current from 2 A to 6 A results in a significant increase in MRR by 93.27% and increase in Ton from 20 μs to 35 μs improved the MRR by 7.98%, beyond which there was no improvement of MRR. The increase in Toff showed a counterproductive effect. Increase in Toff from 10 μs to 30 μs showed an almost linear decrease in MRR by 52.77%. Morphological study of the machined surface showed that cut surface consists of recast layer on which microcracks were present, and revealed the presence of globules, ridge-structured formations of recast layers and voids. In addition, a regression model was developed to predict the MRR with respect to the control factors, which showed a good prediction with an R2 value of 99.67%

Optimization of preheating temperature for TiB

This work emphasizes the optimization of preheating temperature of TiB2 reinforcement powder with LM4 composites, and statistical analysis for predicting hardness improvement during aging treatment using ANOVA, are illustrated in this article. A two-stage stir casting procedure was used to fabricate LM4 + TiB2 (1, 2 and 3 wt.%) composites. The impact of preheating TiB2 reinforcement powder at various temperatures such as 600, 500, 450, 350 and 250 °C, to attain uniform distribution of reinforcements in the matrix was studied. Optical microstructure analysis clearly shows that the optimum preheating temperature of TiB2 powder for effective preparation of composites is 350 °C for 30 min without agglomeration of reinforcement particles. After successful preparation of composites, the as-cast samples were subjected to single-stage and multistage solutionizing treatments and then artificially aged at 100 and 200 °C to obtain peak hardness. Micro Vickers Hardness test was done to calculate the hardness of both age hardened LM4 alloy and its composites and results were analyzed. An increase in wt.% of TiB2 (1–3%), the hardness of composites increased, and multistage solutionizing treatment followed by artificial aging at 100 °C was proven to achieve the highest peak hardness value for LM4 + 3 wt.% TiB2 composites. Compared to as-cast LM4 alloy, 80–150% increase in hardness was observed when aged at 100 °C and 65–120% increase in hardness was observed at 200 °C during SSHT and MSHT, respectively. ANOVA was performed with wt.%, solutionizing type, aging temperatures as factors, and peak hardness as the outcome. From the results, it can confirm that all three factors contributed effectively for achieving the peak hardness. R2 value validates that the factors account for 100% of the variance in the hardness results

Investigation of the Mechanical and Liquid Absorption Properties of a Rice Straw-Based Composite for Ayurvedic Treatment Tables

Managing rice crop stubble is one of the major challenges witnessed in the agricultural sector. This work attempts to investigate the physical, mechanical, and liquid absorption properties of rice straw (RS)-reinforced polymer composite for assessing its suitability to use as an ayurvedic treatment table. This material is expected to be an alternative for wooden-based ayurvedic treatment tables. The results showed that the addition of rice straw particles (RSp) up to 60% volume in epoxy reduced the density of the composite material by 46.20% and the hardness by 15.69%. The maximum tensile and flexural strength of the RSp composite was 17.53 MPa and 43.23 MPa, respectively. The scanning electron microscopy (SEM) analysis showed deposits of silica in the form of phytoliths in various size and shapes on the outer surface of RS. The study also revealed that the water absorption rate (WA) was less than 7.8% for the test samples with 45% volume of RSp. Interestingly the test samples showed greater resistance to the absorption of Kottakal Dhanvantaram Thailam (<2%). In addition, the developed samples showed resistance towards bacterial and fungal growth under the exposure of treatment oils and water