Perumalla Janaki Ramulu

Because of its exceptional mechanical strength, thermal stability, and precision, selective laser melting (SLM) is finding widespread adoption in additive manufacturing (AM). In particular, the SLM processing of Ti6Al4V alloy is well-suited for applications that require high-performance with weight savings, high structural integrity, and durability for long-term applications. However, the wide range of process parameters and complex thermo-physical phenomenon involved significantly affect its torsional and torsional-fatigue properties. This study investigates the torsional performance and fracture behavior of SLM-printed Ti6Al4V alloy components under static torsional loading and high-cycle fatigue conditions. The tests were performed through 3D-printing of samples to investigate the mechanical response properties and fracture behavior of SLM-Ti6Al4V samples under torque-controlled conditions, followed by fractography analysis. Unique mechanical response characteristics were observed in the torsional stress–strain loading curves. The torsional performance parameters at the neck point were determined to be: torsional yield shear stress (τ _y ) of 588 MPa, ultimate torsional shear stress (τ _u ) of 1108 MPa at a twist angle of 24.58°, a shear modulus (G) of 42.85 GPa, and torsional shear stiffness of 1.7 kNm/rad. The ultimate shear stress (τ _u ) is higher than what is reported so far, while the torsional yield shear stress (τ _y ) obtained here is comparable to previous SLM components and higher than the wrought alloy values. Post-peak region behavior and fractography surface morphology features showed strain softening behavior as the crucial cause of the fractured surface of the Ti6Al4V alloy samples. Similarly, the torsional-fatigue strength of the SLM-Ti6Al4V alloy sample was found to be in the range of 97 MPa and 145 MPa under 100,000 cycles. These findings confirm that the torsional properties of the SLM-Ti6Al4V samples meet the performance requirements for demanding high-performance applications, offering reliable strength and stability under torsional loading conditions.

Selective Laser Melting (SLM) is a highly adaptable additive manufacturing method notable for its feature accuracy and flexibility. Processing Ti-6Al-4V alloy using SLM is particularly advantageous because of the alloy's unique structural features and thermal characteristics, making it well-suited for demanding applications where lightweight design, strength, and durability are essential. However, prohibitively large number of process parameters and complex thermo-physical phenomenon significantly influence the microstructural features, which in turn affect the torsional performance of printed Ti-6Al-4V components. In this study, the crystallographic structural features behavior of SLM-processed Ti-6Al-4V alloy components was investigated for high-performance loading applications. X-ray diffraction (XRD) analysis of the 3D-printed SLM Ti-6Al-4V alloy specimens revealed crystallite sizes ranging from 687 to 2376 nm along the printed surface in the horizontal position (XY plane) and from 500 to 3747 nm along the part-building direction (Z-axis). Due to the associated microstrain effect, the printed surfaces in the horizontal position exhibited a region of tensile strain (2.440 × 10 ⁻⁴ ), whereas those parallel to the part-building direction exhibited region of compressive strain (−0.3057 × 10 ⁻⁴ ) during layer deposition. The printed surfaces demonstrated a higher degree of crystallinity in the vertical-position (96.9%) while the horizontal position exhibited a more amorphous structure (67.5%), suggesting that greater crystalline region develops along the part-building direction of the SLM process. Furthermore, the tensile and torsional yield strengths in the horizontal orientation (550 MPa and 308 MPa, respectively) were slightly higher than those in the vertical directions (540 MPa and 306 MPa, respectively), indicating a marginally better mechanical resistance in the horizontally oriented features compared to the vertically oriented ones.

Simultaneous localization and mapping (SLAM) plays a vital role in the operation of autonomous robots. In a dynamic environment, the results of some commonly used SLAM algorithms may not be optimal. Therefore, Visual SLAM is used. This study presents a comparative study of SLAM algorithms for autonomous mobile robots in structured indoor environments, especially those found in warehouses. Nevertheless, GMapping and Cartographer SLAMs are preferred due to their ease of use, 2D LiDAR data, and low computational complexity. However, they are often confronted with problems of dynamic obstacles, loop closures, and localization drifts in large areas. To overcome these problems, this research aims to investigate the combination of Visual SLAM with an Intel RealSense D435i depth camera, YD LiDAR X4, and a custom $ wheeled mobile robot platform built with a Raspberry Pi 4B and four 100 RPM DC motors. This configuration is intended to facilitate the implementation and comparison of GMapping, Cartographer, and Visual SLAM algorithms under the same circumstances. The results of this experiment show that Visual SLAM greatly improves the localization accuracy, map quality, and loop closure detection in warehouse environments. These results support the application of Visual SLAM to improve autonomous robots’ navigation in real‐world indoor environments.

This study investigates the performance, combustion, and emission characteristics of ternary fuel blends composed of diesel, waste cooking oil biodiesel, and pyrolysis oil in a direct injection compression ignition (CI) engine. Experimental tests were conducted using various fuel blends under different injection pressures (180, 210, and 240 bar) and engine loads. Performance metrics such as brake thermal efficiency (BTE), volumetric efficiency, and exhaust gas temperature (EGT) were analyzed, alongside combustion parameters like heat release rate (HRR) and peak in-cylinder pressure. Emissions of NOx, carbon monoxide (CO), smoke opacity, and unburned hydrocarbons (UHC) were also evaluated. The results reveal that an injection pressure of 240 bar optimizes engine performance, improving BTE and volumetric efficiency while reducing fuel consumption for the blends with 70% diesel, 10% pyrolysis oil and 20% sample 2 biodiesel (P10B220D70), 70% diesel, 15% pyrolysis oil and 15% sample 2 biodiesel (P15B215D70), and 70% diesel, 5% pyrolysis oil and 25% sample 2 biodiesel (P5B225D70). Combustion analysis shows that biodiesel-pyrolysis oil blends achieve faster pre-mixed combustion compared to diesel, with peak in-cylinder pressures highest at 240 bar. However, extremely high or low injection pressures negatively affect combustion efficiency. Emission analysis indicates that NOx emissions vary by blend type, with certain blends 70% diesel, 5% pyrolysis oil and 25% sample 1 biodiesel (P5B125D70 and 70% diesel, 5% pyrolysis oil and 25% sample 2 biodiesel (P5B225D70) producing lower values due to reduced premixed combustion. CO emissions decrease with higher injection pressure, while lighter-viscosity blends exhibit reduced smoke opacity. UHC levels remain relatively stable across different loads. Correlation analysis identifies injection pressure as the most influential parameter affecting engine performance and emissions, with blend type and engine load having minimal impact. These findings underscore the potential of biodiesel-pyrolysis oil blends as sustainable alternatives for compression ignition engines when optimized with appropriate injection pressures.

Predictive modeling has emerged as a key focus for cryptocurrency market asset valuation due to its complex nature and high market volatility. The research looks into Ethereum price forecasting with the methods of autoregressive integrated moving average (ARIMA) and Facebook Prophet model and long short‐term memory (LSTM) networks. These models operate on historical Ethereum prices and show their efficiency regarding temporal pattern recognition and prediction accuracy. The ARIMA model helps reveal trends as well as seasonal patterns and irregularities within Ethereum price fluctuations. The Facebook Prophet model serves as a forecasting tool because it automatically handles peculiarities present within cryptocurrency price data. Time series forecasting with LSTMs becomes an advanced technique used to detect intricate patterns along with sustained dependency relationships between data points. The systematic process of preparing data and constructing models and assessing results enables proper utilization of LSTMs for predicting time series data with accuracy. Ethereum price datasets are applied to train the models which undergo performance evaluation using MPE alongside MAPE and RMSE along with MAE to reveal strengths and weaknesses during Ethereum price predictions. The evaluation shows that ARIMA and Facebook Prophet together with LSTM demonstrate success in modeling Ethereum price fluctuations. This research explores the effectiveness of time series forecasting methods for cryptocurrency price prediction yielding vital knowledge about reliable tools for financial market trend modeling. Current research findings will provide knowledge to investors and risk management professionals making decisions within the volatile digital asset space.