SHAILENDRA CHAUHAN

Scopus Publications

Intelligent surface roughness prediction in inconel 625 milling using sensor fusion and explainable AI
Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh, Vishal S Sharma, BS Pabla
Engineering Failure Analysis, 2026
Intelligent Tool Wear Prediction for Enhanced Sustainability in Milling of Ni-Based Superalloy
S. Chauhan, R. Trehan, R.P. Singh, V.S. Sharma
IEEE Sensors Journal, 2026
This research presents an integrated and systematically validated framework for predicting tool wear in milling Inconel X750 using multi-sensor fusion. In this study, an accelerometer and a dynamometer are integrated to achieve sensor fusion, along with cryogenically treated cutting tool inserts with different edge radii. Experiments were designed to analyse tool wear, with results evaluated using ANOVA tests. The study employs S-Golay filtered Stationary Wavelet Transform and the Largest Lyapunov Exponent to extract features from vibration and cutting force signals, enhancing prediction accuracy. Explainable AI (XAI) ensures model transparency, while the Extreme Learning Machine (ELM) effectively manages complex data relationships, yielding robust predictions. By combining sensor fusion with XAI, the study enhances interpretability and trust in AI-based decisions, making predictive maintenance more actionable for industrial applications. Results show the depth of cut has the highest mean Shapley values, achieving accurate metrics for tool inserts T1 and T2. Furthermore the study achieves comparable accuracy metrics for cutting tool inserts T1 and T2, with an RMSE of 2.27%, MAE of 1.47%, and |<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R95%| of 4.61% for cutting tool T1 and an RMSE of 3.14%, MAE of 1.95%, and |<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R95%| of 5.1% for cutting tool T2. This research enhances machining practices, particularly in aerospace, improving tool life and efficiency.
Influence of cutting tool edge radius in milling of Inconel X750: experimental study and wear surface characterisation
Shailendra Chauhan, Ravi Pratap Singh, Rajeev Trehan
Canadian Metallurgical Quarterly, 2026
Investigation on Surface Integrity in Milling of Inconel X750: A Comprehensive analysis of Cutting Edges and Machining Parameters
Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh, Vishal S. Sharma
International Journal of Refractory Metals and Hard Materials, 2024
Experimental investigation into face milling of Inconel X750 super alloy: a study on cutting force and surface topography
Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh
World Journal of Engineering, 2024
Purpose This work aims to describe the face milling analysis on Inconel X-750 superalloy using coated carbides. The formed chips and tool wear were further analyzed at different cutting parameters. The various impact of cutting parameters on chip morphology was also analyzed. Superalloys, often referred to as heat-resistant alloys, have exceptional tensile, ductile and creep strength at high operating temperatures and good fatigue strength, and often better corrosion and oxidation resistance at extreme heat. Because of these qualities, these alloys account for more than half of the weight of sophisticated aviation, biomedical and thermal power plants today. Inconel X-750 is a high-temperature nickel-based superalloy that is hard to machine because of its extensive properties. At last, the discussion regarding the tool wear mechanism was analyzed and discussed in this article. Design/methodology/approach The machining parameters for the study are cutting speed, feed rate and depth of cut. One factor at a time approach was implemented to investigate the effect of cutting parameters on the cutting forces, surface roughness and material removal rate. The scatter plot was plotted between cutting parameters and target functions (cutting forces, surface roughness and material removal rate). The six levels of cutting speed, feed rate and depth of cut were taken as cutting parameters. Findings The cutting forces are primarily affected by the cutting parameters, tool geometry, work material etc. The maximum forces Fx were encountered at 10 mm/min cutting speed, 0.15 mm/rev feed rate and 0.4 mm depth of cut, further maximum forces Fy were attained at 10 mm/min cutting speed, 0.25 mm/rev feed rate and 0.4 mm depth of cut and maximum forces Fz were attained at 50 mm/min cutting speed, 0.05 mm/rev feed rate and 0.4 mm depth of cut. The maximum surface roughness value was observed at 40 mm/min cutting speed, 0.15 mm/rev feed rate and 0.5 mm depth of cut. Originality/value The effect of machining parameters on cutting forces, surface roughness, chip morphology and tool wear for milling of Inconel X-750 high-temperature superalloy is being less researched in the present literature. Therefore, this research paper will give a direction for researchers for further studies to be carried out in the domain of high-temperature superalloys. Furthermore, the different tool wear mechanisms at separate experimental trials have been explored to evaluate and validate the process performance by conducting scanning electron microscopy analysis. Chip morphology has also been evaluated and analyzed under the variation of selected process inputs at different levels.
Intelligent tool wear prediction in milling of nickel-based superalloy using advanced signal processing and bi-transformer
Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh, Vishal S. Sharma
International Journal of Mechatronics and Manufacturing Systems, 2024
This study presents an intelligent tool wear prediction approach for milling Nimonic 80, a superalloy used in high-temperature applications. The impact of different cutting-edge radii (0.8 mm and 0.4 mm) on tool wear is examined using sensor fusion techniques to integrate cutting force and vibration signals. Advanced signal processing methods like improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the Largest Lyapunov Exponent (LLE) for feature extraction are utilised. The experimental design employs a response surface methodology (RSM) with a central composite rotatable design and analysis of variance (ANOVA) to evaluate factor significance. A bi-Transformer model processes multi-sensor data, improving prediction accuracy. Results show that the proposed methodology significantly enhances tool wear prediction, offering insights into Nimonic 80 wear mechanisms. The bi-Transformer achieved an R2 of 97.61%, a root mean square error (RMSE) of 0.015, a mean absolute error (MAE) of 0.012, and ∣
Assessment of Machining Performance for Intelligent Tool Wear Prediction Using Hybrid Extreme Learning Machine
S. Chauhan, R. Trehan, R. P. Singh, V. S. Sharma
IEEE Sensors Journal, 2024
The increasing utilization of Ni-based superalloys in aerospace and power generation industries presents significant machining challenges due to their unique properties. This study introduces a hybrid extreme learning machine (ELM) to predict tool wear during face milling of Inconel X750 using multiple sensors, aiming to optimize machining processes and extend tool lifespan. Using two types of cutting tool inserts (Re =0.8 mm and Re =0.4 mm), the study employs a combination of stationary wavelet transform (SWT) and largest Lyapunov exponent (LLE) for signal processing and feature extraction. The primary performance characteristic, tool wear, is analyzed using ANOVA tests, and the predictive model’s accuracy is validated with real-world data. The multilayer perceptron ELM (MLELM) optimized with the slime mold algorithm (SMA) demonstrates low root-mean-square errors of 0.08 for T1 and 0.09 for T2, with minimum percentage errors of 0.82% for T1 and 0.68% for T2. These findings provide valuable insights for enhancing machining processes, contributing to more efficient, sustainable, and cost-effective manufacturing. This research underscores the importance of intelligent tool condition monitoring in advancing manufacturing technologies. The proposed model’s ability to optimize tool wear prediction can significantly improve manufacturing efficiency and reduce downtime in industrial applications. This research underscores the importance of intelligent tool condition monitoring in advancing manufacturing technologies and achieving sustainability goals.
State of the art in finite element approaches for milling process: a review
Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh
Advances in Manufacturing, 2023
Classification of surface roughness for CNC face milling of Inconel 625 superalloy utilizing cutting force signal features with SVM and ANN
Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh
Materials Today Proceedings, 2023
Study on surface integrity in turning of titanium using cryogenically treated CBN inserts
Shailendra Chauhan, B S Pabla, Ravi Pratap Singh, Ramesh Singh, Tarlochan Singh
Advances in Materials and Processing Technologies, 2022
In the engineering world, the machining of advanced materials like titanium alloys, which is having their superior applications in a variety of domains, that is, bio-implants, biomedical, aerospace, and so on, has been a challenge for many decades to the researchers. The optimised surface topography for such alloys has been revealed as a function of various input parameters. In this study, an attempt has been made by conducting the experimental investigation on the surface integrity and the quality machined work surface in turning of titanium alloy, that is, Ti6Al4V through employing cryogenically treated and non-cryogenically processed CBN inserts. The surface quality and the integrity of processed titanium alloy work-surface have been investigated under the influence of various considered input factors. The design of experimentation was constructed for planning the experiments. The analysis of variance test has been attempted for the studied response, that is, surface roughness and the mathematical models for the studied surface roughness (with both CBN inserts; cryogenically and non-cryogenically treated) have also been developed. The attained optimised parametric conditions for surface roughness are; feed rate – 0.067 mm/rev, cutting speed – 63.852 m/min, and depth of cut – 0.526 mm; and feed rate – 0.066 mm/rev, cutting speed – 59.450 m/min, and depth of cut – 0.309 mm, for non-cryogenically and cryogenically treated CBN inserts, respectively. Artificial neural network (ANN)-based modelling has also been attempted to predict and analyse the dataset of experiments. There is a variation of 1.43% between ANN predicted data of NC CBN and NC CBN experimental data. Further, there is an error of 0.2% between ANN predicted data of Cryo-CBN and Cryo-CBN experimental data. The microstructure analysis of the turned titanium surface reflected the presence of few tearing and groove lines with both the CBN inserts employed. The scratches and bamboo lines have been due to the built-up edges produced in the inserts.

SHAILENDRA CHAUHAN

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Scopus Publications