Dr. N. Rajesh is working as Associate Professor in Department of Mechanical Engineering, Sri Venkateswara College of Engineering, Tirupati. He obtained Doctor of Philosophy in Mechanical Engineering (Ph.D. 2020)] from the JNTUniveristy, Anatapur focusing in Fabrication and Machining of Nanocomposites His research areas currently span: (a) Development of metal matrix Nanocomposites; (b) Advanced materials processing techniques; (c) Electron microscopy; and (e) Shape Memory Alloys. He obtained 3 patents & 2 Deign patents for the benefit of the society.
RESEARCH INTERESTS
Nano composites, Metal Cutting, Optimization, 3D printing
Multi-response optimization of PETG FDM parameters using taguchi–grey relational analysis and perdition by regression modeling P. Thejasree, N. Manikandan, N. Rajesh, R. Lokanadham, P. C. Krishnamachary, Kumar Shubham, Regasa Yadeta Sembeta Scientific Reports, 2025 Additive Manufacturing (AM) techniques, especially Fused Deposition Modelling (FDM), have generated much interest recently for their capabilities for manufacturing complex geometries using a variety of materials. In this work, a regression model has been developed for the FDM process performance enhancement and to control PETG processing. This study systematically analysed the effect of critical FDM parameters on key performance criteria such as printing time, dimensional deviation, and surface finish, including nozzle temperature, printing speed, and infill density. Experiments were carried out following a defined design of experiments to gather data which were then used to develop regression models for the prediction of printing results. A statistical treatment was done on the relationships among process variables with its impact on performance metrics. The predictive model developed showed a high level of accuracy, thus allowing for the identification of optimal levels for parameter settings conducive to PETG component efficiency, surface quality, and dimensional accuracy. Thus, the study acts as a practical guide for manufacturers willing to upgrade their additive manufacturing processes relating to process optimization, quality control, and production planning. By tying experimental inquiry with predictive modeling, this work delves deep into the dynamics of the FDM process and provides valuable insights for the mass use of PETG-based FDM in automotive, aerospace, biomedical, and other industry sectors.
Application of Taguchi Grey-Based ANFIS model for prediction of process parameters in fused deposition modelling of PETG P. Thejasree, N. Manikandan, N. Rajesh, R. Lokanadham, P.C. Krishnamachary, Kumar Shubham, Bamidele Charles Olaiya Scientific Reports, 2025 Fused Deposition Modelling (FDM) is one of the most used Additive Manufacturing technologies in the world due to its application in the fabrication of complex structures with different materials. The present study deals with improving the process control and efficiency of FDM for Polyethylene Terephthalate Glycol (PETG) material through the development of a predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Systematic analysis of the effect of the most important FDM process factor-infill density, nozzle temperature, and printing speed performance metrics while printing time, dimensional deviation, and surface roughness is performed. The required data set is generated for the conduct of experimental trials based on a Taguchi L27 orthogonal array. An ANFIS Model is, therefore, developed, which integrates neural learning with fuzzy logic to predict printing characteristics by analyzing the interaction between inputs and outputs. The model has been developed such that it is highly capable of predicting results, thereby inducing the best or optimum process variables to enhance FDM performance. Infill density, nozzle temperature, and printing speed follow last in their effects on the performance of FDM. A GRG value of 0.8547 and a R 2 of 0.9999 confirm quite strong prediction capabilities of the Grey-ANFIS model between those predicted and experimental results, endorsing it as a reliable optimization tool in FDM of PETG material.
Challenges and Solutions of Industry 4.0 to Industry 5.0 N. Rajesh, Kamineni Jagath Narayana, G. Guru Mahesh, R. Lokanadham Manufacturing in the Digital Age, 2025 Industry 4.0 represents a significant shift in manufacturing, leveraging digital technologies like Internet of Things, Artificial Intelligence (AI), and automation to revolutionize manufacturing processes. This evolution enables the creation of smart factories and facilitates real-time data analytics, resulting in enhanced efficiency and connectivity across various industries. Building upon these advancements, Industry 5.0 places emphasis on human–robot collaboration and personalized manufacturing experiences. By incorporating AI, robotics, and advanced analytics, it aims to establish adaptable and sustainable manufacturing ecosystems tailored to individual requirements. However, the adoption of these technologies presents both opportunities and challenges for organizations. Challenges include navigating initial investments, ensuring compatibility with legacy systems, addressing data security concerns, and enhancing workforce skills for Industry 4.0. Moreover, Industry 5.0 introduces new considerations such as ethical dilemmas, customization requirements, and sustainability concerns. Successfully addressing these challenges requires careful strategic planning, investment, and the cultivation of a culture of innovation within organizations. This chapter seeks to explore these challenges in depth and propose potential solutions to facilitate the seamless adoption of Industry 4.0 and Industry 5.0 technologies, thereby enabling businesses to thrive in the era of advanced manufacturing and smart industries.
Response surface methodology based evaluation of S-glass fibre composite reinforced with aluminium oxide/pearlite hybrid nano particles Guru Mahesh G, Rajesh N, Jayakrishna K Advances in Materials and Processing Technologies, 2024 ABSTRACTThe affinity towards utilisation of Glass Fiber Reinforced Polymers (GFRPs) is its exceptional and distinctive properties. These are substitutional to conventional materials with added benefits upon incorporation of filler materials into the glass/epoxy polymer composites. The present work focused in order to prepare hybrid S-Glass nanocomposite samples with a mix of nano Al2O3 (1.0 Wt% to 3.0 Wt%) and nano perlite powder (3.0 Wt% to 9.0 Wt%) with different weight percentages. According to the Response Surface Methodology (RSM) and its experimental design, the hybrid S-glass/Al2O3/Nano perlite composite was prepared using hand layup process. Further, the fabricated samples were processed as per ASTM standards and the mechanical characteristics were investigated. Thus, the responses Tensile strength and Flexural strength were recorded, optimised using RSM and anlysed using Analysis of Variance (ANOVA). Also, statistical techniques were applied to verify the experimental results. The optimal weight percentages of nano Al2O3 powder and nano Perallite powders are 2% for obtaining maximum tensile strength and 3% to obtain maximum flexural strength, respectively. The water absorption test was carried for obtained optimal sample and water uptake pattern has been recorded as per ASTM D570.KEYWORDS: S-glass fibernano Al2O3perlite nano-powderswater absorptionresponse surface methodology AcknowledgementsThe authors would like to thank Omega Labs, Chennai, for conducting the mechanical tests for the specimens.Disclosure statementNo potential conflict of interest was reported by the authors.
Study of Machining Parameters on Tensile strength and Surface roughness of ABS samples printed by FDM Rajesh N, Guru Mahesh G, P. Venkataramaiah Advances in Materials and Processing Technologies, 2022 Additive Manufacturing or 3D printing is referred as digital fabrication technology which is rapidly increasing globally. It creates physical objects from geometrical representation by successive addition of materials. 3D printing is economical in manufacturing. Due to its better properties like toughness, formability, hardness, non-toxic, retention of colour and simple to form, these properties are appropriate for the FDM technology when Acrylonitrile Butadiene Styrene (ABS) is used for fabrication. In this paper, ABS samples are manufactured using Fusion Deposition Method (FDM) with an aim of studying tensile behaviour and surface roughness of the samples at different process parameters utilised in fabrication. According to Taguchi design of experiments, the experimental runs are designed. The input parameters considered for this investigation are layer height and melting temperature. For this corresponding process parameters, the output responses tensile strength and surface roughness are measured. Investigations were made to determine the optimal parameter combinations for fabrication and explored for various applications when fabrication is done using this ABS. In this work, better strength is obtained at layer height of 0.25 mm and nozzle temperature of 235°C, and better surface finish is obtained at layer height of 0.2 mm and nozzle temperature of 235°C.
