DHANUNJAY KUMAR AMMISETTI

@lbrce.ac.in

Sr.Asst.Professor and Mechanical Engineering
Lakireddy Bali Reddy College of Engineering

DHANUNJAY KUMAR AMMISETTI


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https://researchid.co/pardhu015

RESEARCH, TEACHING, or OTHER INTERESTS

Mechanical Engineering, Modeling and Simulation, Ceramics and Composites, Artificial Intelligence
16

Scopus Publications

205

Scholar Citations

8

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Multi‐objective optimization of 3D printing process parameters using hybrid TOPSIS technique for short carbon fiber reinforced PETG composites
    V. V. D. Sahithi, Shishir Kasturi, Dhanunjay Kumar Ammisetti
    International Journal on Interactive Design and Manufacturing, 2026
  • Experimental and machine learning studies on flexural properties of 3D-printed PLA/wood composites
    Vaka Venkata Durga Sahithi, Dhanunjay Kumar Ammisetti
    Journal of Reinforced Plastics and Composites, 2026
    This study investigated the impact of critical input parameters in fused filament fabrication (FFF) on the flexural strength of PLA/wood bio composite specimens. This study considers the layer height (LH), printing temperature (PT), and printing speed (PS) as input parameters at five levels. Taguchi design of experiments (L25 orthogonal array) was employed to minimize experimental runs, followed by analysis of variance (ANOVA) to identify significant factors. The results demonstrated that layer height (78.91% contribution) is the most critical parameter, with 0.1 mm identified as the optimal amount for achieving the maximum flexural response, while printing temperature (9.39%) had a moderate effect and printing speed (2.69%) showed negligible influence. Machine learning methodologies such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting Regression (GBR), and hybrid methods including SVM + DT, RF + DT, and GBR + DT were utilized to predict the flexural strength of the composite filament. In contrast to previous studies relying only on single models, this work shows that utilizing hybrid frameworks (specifically GBR + DT) achieves even better generalization performance, with a test accuracy of 94.30% and considerably lower prediction error (MSE = 10.8109 and RMSE = 3.288) than other models. The usage of Taguchi-ANOVA experimental design with advanced hybrid ML methods achieves a new contribution to using the approaches in additive manufacturing materials property modeling for suggesting comprehensive experimental investigation while improving predictive accuracy and robustness.
  • Experimental Investigation on the Effect of Graphene/Al2O3 Reinforcements on the Microstructural, Mechanical, and Tribological Characteristics of AZ91 Alloy
    Dhanunjay Kumar Ammisetti, S. S. Harish Kruthiventi
    Journal of Tribology, 2026
    In the present work, the effect of graphene/Al2O3 on the microstructure, mechanical, and wear characteristics of AZ91 alloy was studied. The composites with various proportions of graphene/ Al2O3 are prepared using inert gas-assisted stir-casting technique. The scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) mapping investigation exhibited that the reinforcing elements were uniformly distributed inside the matrix structure. The composite's hardness increased from 62HB to 86HB with the incorporation of reinforcements. The hybrid composite (AZ91 + 1 wt% graphene + 2 wt%Al2O3) demonstrated a significant enhancement in ultimate tensile strength (UTS), yield strength (YS), and compressive strength (CS), with enhancements of 39.01%, 59.26%, and 52.17%, respectively, as compared to the AZ91 alloy. From wear test results, it is observed that the hybrid composites demonstrate greater wear resistance compared to the AZ91 alloy. At a load of 30 N, the wear rate (WR) of the hybrid composite (HC3) was 79.10% lower than that of pure AZ91, indicating a greater improvement in wear resistance. Further, the SEM analysis identified delamination, abrasion, oxidation, and adhesion mechanisms on the worn surface.
  • Experimental Investigation and Machine Learning Modeling on the Tribological Characteristics of Heat Treated AA7075/B4C/BN/SiC Hybrid Composites
    Seelam Pichi Reddy, Dhanunjay Kumar Ammisetti, Simhadri Raju Juvvala, Annapareddy V. N. Reddy
    Journal of Materials Engineering and Performance, 2026
  • Experimental Investigation and Machine Learning Modeling of Electrical Discharge Machining Characteristics of AZ31/B4C/GNPs Hybrid Composites
    Dhanunjay Kumar Ammisetti, Satya Sai Harish Kruthiventi, Krishna Prakash Arunachalam, Victor Poblete Pulgar, Ravi Kumar Kottala, Seepana Praveenkumar, Pasupureddy Srinivasa Rao
    Crystals, 2025
    Magnesium alloys, like AZ31, possess a desirable low weight and high specific strength, which make them favorable for aerospace and auto applications, yet their difficulty to machine limits their broader implementation for the industry. Electrical discharge machining (EDM) is an effective technology for machining difficult-to-machine materials, particularly when the materials are reinforced with ceramic and graphene-based fillers. This study examines the impact of reinforcement percentage (R) and different electrical discharge machining (EDM) parameters such as current (I), pulse on time (Ton) and pulse off time (Toff) on the material removal rate (MRR) and surface roughness (SR) of AZ31/B4C/GNPs composites. The combined reinforcement range varies from 2 wt.% to 4 wt.%. The Taguchi design (L27) is utilized to conduct the experiments in this study. ANOVA of the experimental data indicated that current (I) significantly affects MRR and SR, exhibiting the greatest contribution of 44.93% and 51.39% on MRR and SR, respectively, among the variables analyzed. The surface integrity properties of EDMed surfaces are examined using SEM under both higher and lower material removal rate settings. Diverse machine learning techniques, including linear regression (LR), polynomial regression (PR), Random Forest (RF), and Gradient Boost Regression (GBR), are employed to construct an efficient predictive model for outcome estimation. The built models are trained and evaluated using 80% and 20% of the total data points, respectively. Statistical measures (MSE, RMSE, and R2) are utilized to evaluate the performance of the models. Among all the developed models, GBR exhibited superior performance in predicting MRR and SR, achieving high accuracy (exceeding 92%) and lower error rates compared to the other models evaluated in this work. This work demonstrated the synergy between techniques in optimizing EDM performance for hybrid composites using a statistical design and machine learning strategies that will facilitate greater use of hybrid composites in high-precision engineering applications and advanced manufacturing sectors.
  • A Review on Mechanical and Wear Characteristics of Magnesium Metal Matrix Composites
    Dhanunjay Kumar Ammisetti, K Sai Sarath, S. S. Harish Kruthiventi
    Journal of Tribology, 2025
    Magnesium (Mg) and its alloys provide a desirable mixture of characteristics, including minimal density and an excellent strength/weight ratio. Nevertheless, these materials have limitations in relation to their thermal conductivity, wear and corrosion resistance, among various other attributes. The limits described above place restrictions on the use of these alloys in various applications. Currently, various methods are being employed to efficiently address and alleviate those limitations through the utilization of composite materials. The incorporation of micro/nanosized elements has been utilized to elevate the properties of Mg. Various methods are utilized to provide a homogeneous dispersal of reinforcement throughout the matrix, resulting in the production of magnesium metal matrix composites (MgMMCs). The use of MgMMCs has experienced a notable rise across many sectors such as aerospace, defense, automotive, and biomedical. This may be attributed to their exceptional attributes, which consist of enhanced specific strength, reduced weight, and congruence with biological systems. The current study objective is to perform an exhaustive examination of the different reinforcements employed in the fabrication of MgMMCs and their impact on mechanical and tribological characteristics. Furthermore, the study presented in this paper showcases the development of prediction models for the wear properties of MgMMCs through the utilization of diverse machine learning approaches.
  • A review on reinforcements, fabrication methods, and mechanical and wear properties of titanium metal matrix composites
    Dhanunjay Kumar Ammisetti, S. S. Harish Kruthiventi, Sankararao Vinjavarapu, Nelakuditi Naresh Babu, Jaya Raju Gandepudi, Sudheer Kumar Battula
    Journal of Engineering and Applied Science, 2024
    Titanium and its alloys exhibit a favorable integration of characteristics, including notable strength and high resistance to corrosion. However, they are deficient in terms of wear resistance and thermal conductivity, among other properties. The aforementioned limitations impose constraints on the utilization of these alloys across diverse applications. Currently, various strategies involving the utilization of composite materials are being implemented in order to address and mitigate these previously mentioned limitations. The utilization of micro- or nano-sized reinforcements has been employed to improve the characteristics of the metal matrix. Diverse techniques are employed to uniformly distribute the reinforcement within the matrix, thereby generating titanium metal matrix composites (TMCs). The use of TMCs has become increasingly prevalent in diverse sectors, including defense, automotive, aerospace, and biomedical, owing to their remarkable characteristics, which encompass lower weight, higher specific strength, and compatibility with biological systems. The present study discusses various manufacturing techniques, including spark plasma sintering (SPS), additive manufacturing, and vacuum melting. This study further examines different reinforcements that are considered in the production of TMCs. The current study also investigates the effects of reinforcements on properties such as mechanical and tribological characteristics. The study demonstrated that the incorporation of reinforcements resulted in enhanced properties.
  • Experimental Investigation and Machine Learning Modeling of Tribological Characteristics of AZ31/B4C/GNPs Hybrid Composites
    Dhanunjay Kumar Ammisetti, Bharat Kumar Chigilipalli, Baburao Gaddala, Ravi Kumar Kottala, Radhamanohar Aepuru, T. Srinivasa Rao, Seepana Praveenkumar, Ravinder Kumar
    Crystals, 2024
    In this study, the AZ31 hybrid composites reinforced with boron carbide (B4C) and graphene nano-platelets (GNPs) are prepared by the stir casting method. The main aim of the study is to study the effect of various wear parameters (reinforcement percentage (R), applied load (L), sliding distance (D), and velocity (V)) on the wear characteristics (wear rate (WR)) of the AZ91/B4C/GNP composites. Experiments are designed using the Taguchi technique, and it was determined that load (L) is the most significant parameter affecting WR, followed by D, R, and V. The wear mechanisms under conditions of maximum and minimum wear rates are examined using SEM analysis of the worn-out surfaces of the specimens. From the result analysis on the WR, the ideal conditions for achieving the lowest WR are R = 4 wt.%, L = 15 N, V = 3 m/s, and D = 500 m. Machine learning (ML) models, including linear regression (LR), polynomial regression (PR), random forest (RF), and Gaussian process regression (GPR), are implemented to develop a reliable prediction model that forecasts output responses in accordance with input variables. A total of 90% of the experimental data points were used to train and 10% to evaluate the models. The PR model exceeded the accuracy of other models in predicting WR, with R2 = 0.953, MSE = 0.011, RMSE = 0.103, and COF with R2 = 0.937, MSE = 0.013, and RMSE = 0.114, respectively.
  • Experimental Analysis and Artificial Neural Network Teaching–Learning-Based Optimization Modeling on Electrical Discharge Machining Characteristics of AZ91 Composites
    Dhanunjay Kumar Ammisetti, S. S. Harish Kruthiventi
    Journal of Materials Engineering and Performance, 2024
  • Experimental Investigation of the Influence of Various Wear Parameters on the Tribological Characteristics of AZ91 Hybrid Composites and Their Machine Learning Modeling
    Bhagwan Singh Lovevanshi, P. K. Soni, Savita Dixit
    Journal of Tribology, 2024
    This research work aims to synthesize a hybrid Al7075 metal matrix composite reinforced with sustainable and synthetic reinforcement. With the employment of an ultrasonic transducer, two-stage stir casting is used to synthesize composite materials. The prepared samples were machined and polished for mechanical, tribological, and microstructural characterization. Optical microscopy and field emission scanning electron microscopy with elemental mapping were used to analyze the microstructure of the composite material. The microstructural examination revealed the homogeneous dispersion of reinforcement particles throughout the matrix. With the incorporation of reinforcement, the synthesized composite's compressive strength and micro-hardness were both increased, and the highest values were found to be 569.172 MPa and 178.86 HV, respectively, in one of the samples (B3 sample) as compared to as-cast Al7075 alloy. Tribological examination of composite samples shows that wear-rate enhances with an increase in the content of reinforcement. The wear resistance of sample B3 is highest among all prepared composite samples. Wear debris, grooves, micro-cracks, and small pits were observed on the worn-out surfaces of the samples by field emission scanning electron microscope analysis.
  • Metal-Based Additive-Manufactured Wear-Resistant Bioimplants
    Bharat Kumar Chigilipalli, Shirisha Bhadrakali Ainapurapu, Borra N Dhanunjayarao, Arunakumari Mavuri, Dhanunjay Kumar Ammisetti
    Tribo Behaviors of Biomaterials and their Applications Fundamentals Recent Advancements and Future Trends, 2024
  • Experimental Investigation and Machine Learning Modeling of Wear Characteristics of AZ91 Composites
    S. S. Harish Kruthiventi, Dhanunjay Kumar Ammisetti
    Journal of Tribology, 2023
  • Experimental Investigation on Water Cooler Test Rig With And Without Diffusers
    Mallikarjuna Rao Dandu, K. Sai Babu, Nelakuditi Naresh Babu, A. Dhanunjay Kumar, K. Venkateswara Reddy, B. Sai Rama Krishna, V. Dhana Raju
    E3s Web of Conferences, 2023
  • Experimental Analysis of Azolla Biodiesel Blends in CI Engine
    N. Sunil Naik, Dhanunjay Kumar Ammisetti, B. Kishan, Suresh Chitturi
    Lecture Notes in Mechanical Engineering, 2022
  • Improvement of Weld Joint Strength by Applying Random Vibrations along with External Magnetic Field
    Suresh Chitturi, Chowdary K Mohana Krishna, Munmun Bhaumik, Dhanunjay Kumar Ammisetti
    Iop Conference Series Materials Science and Engineering, 2020
  • Recent trends on titanium metal matrix composites: A review
    Dhanunjay Kumar Ammisetti, S S Harish Kruthiventi
    Materials Today Proceedings, 2019

RECENT SCHOLAR PUBLICATIONS

  • Experimental Investigation and Machine Learning Modeling on the Tribological Characteristics of Heat Treated AA7075/B4C/BN/SiC Hybrid Composites
    SP Reddy, DK Ammisetti, SR Juvvala, AVN Reddy
    Journal of Materials Engineering and Performance, 1-21 , 2026
    2026
  • Effect of molding sands on microstructure, mechanical, and wear properties of AA2024 aluminum alloy via CO 2 casting: an experimental study
    Dhanunjay Kumar Ammisetti,Thamatapu Eswara Rao & Kottala Ravi Kumar, Lamilla ...
    Canadian Metallurgical Quarterly 66 (1), 1-20 , 2026
    2026
  • Experimental and machine learning studies on flexural properties of 3D-printed PLA/wood composites
    VVD Sahithi, DK Ammisetti
    Journal of Reinforced Plastics and Composites, 07316844261435056 , 2026
    2026
  • Biomedical applications: magnesium hybrid composites reinforced with nanographene nanoparticles (GNPs) and Al2O3
    DK Ammisetti
    Metal and Polymer Micro and Nano Composites Nanoparticles, Nanofibers … , 2026
    2026
  • Detailed review of micro- and nanocomposites
    Dhanunjay Kumar Ammisetti, Suresh Goka and K. Aruna Prabha,V. V. D. Sahithi ...
    Metal and Polymer Micro and Nano Composites Nanoparticles, Nanofibers … , 2026
    2026
  • Multi‐objective optimization of 3D printing process parameters using hybrid TOPSIS technique for short carbon fiber reinforced PETG composites
    VVD Sahithi, S Kasturi, DK Ammisetti
    International Journal on Interactive Design and Manufacturing (IJIDeM) 20 (2 … , 2026
    2026
    Citations: 2
  • Experimental Investigation on the Effect of Graphene/Al 2 O 3 Reinforcements on the Microstructural, Mechanical, and Tribological Characteristics of AZ91 Alloy
    DK Ammisetti, SSH Kruthiventi
    Journal of Tribology 148 (1), 011401 , 2026
    2026
    Citations: 6
  • Experimental Investigation and Machine Learning Modeling of Electrical Discharge Machining Characteristics of AZ31/B 4 C/GNP s Hybrid Composites
    DK Ammisetti, SSH Kruthiventi, KP Arunachalam, VP Pulgar, RK Kottala, ...
    Crystals 15 (10), 844 , 2025
    2025
  • A review on mechanical and wear characteristics of magnesium metal matrix composites
    DK Ammisetti, K Sai Sarath, SSH Kruthiventi
    Journal of Tribology 147 (2), 020801 , 2025
    2025
    Citations: 18
  • Experimental Investigation and Machine Learning Modeling of Tribological Characteristics of AZ31/B 4 C/GNPs Hybrid Composites
    DK Ammisetti, BK Chigilipalli, B Gaddala, RK Kottala, R Aepuru, TS Rao, ...
    Crystals 14 (12), 1007 , 2024
    2024
    Citations: 9
  • Experimental analysis and artificial neural network teaching–learning-based optimization modeling on electrical discharge machining characteristics of AZ91 composites
    DK Ammisetti, SSH Kruthiventi
    Journal of Materials Engineering and Performance 33 (21), 11718-11735 , 2024
    2024
    Citations: 10
  • Metal-Based Additive-Manufactured Wear-Resistant Bioimplants
    BK Chigilipalli, SB Ainapurapu, BN Dhanunjayarao, A Mavuri, ...
    Tribo-Behaviors of Biomaterials and their Applications, 118-136 , 2024
    2024
    Citations: 1
  • Experimental investigation of the influence of various wear parameters on the tribological characteristics of AZ91 hybrid composites and their machine learning modeling
    DK Ammisetti, SSH Kruthiventi
    Journal of Tribology 146 (5), 051704 , 2024
    2024
    Citations: 31
  • A review on reinforcements, fabrication methods, and mechanical and wear properties of titanium metal matrix composites
    DK Ammisetti, SSH Kruthiventi, S Vinjavarapu, NN Babu, JR Gandepudi, ...
    Journal of Engineering and Applied Science 71 (1), 60 , 2024
    2024
    Citations: 33
  • Experimental investigation and machine learning modeling of wear characteristics of AZ91 composites
    SSH Kruthiventi, DK Ammisetti
    Journal of Tribology 145 (10), 101704 , 2023
    2023
    Citations: 20
  • Experimental Investigation on Water Cooler Test Rig With And Without Diffusers
    Mallikarjuna Dandu ,K. Sai Babu, Nelakuditi Naresh Babu, Dhanunjay Kumar ...
    E3S Web of Conferences 391 (1), 10.1051/e3sconf/202339101101 , 2023
    2023
  • Experimental Analysis of Azolla Biodiesel Blends in CI Engine
    NS Naik, DK Ammisetti, B Kishan, S Chitturi
    Innovations in Mechanical Engineering: Select Proceedings of ICIME 2021, 379-390 , 2022
    2022
  • Optimization of EDM process parameters on machining characteristics of sic and graphene reinforced Al 6061-T6nano-Composites
    S Ammisetty, D Ammisetti, K Satyanarayana, S Chitturi, NS Naik
    IOP Conference Series: Materials Science and Engineering 1112 (1), 012017 , 2021
    2021
    Citations: 9
  • Recent trends on titanium metal matrix composites: A review
    DK Ammisetti, SSH Kruthiventi
    Materials Today: Proceedings 46, 9730-9735 , 2021
    2021
    Citations: 64
  • Improvement of weld joint strength by applying random vibrations along with external magnetic field
    S Chitturi, CK Mohana Krishna, M Bhaumik, DK Ammisetti
    IOP Conference Series: Materials Science and Engineering 998 (1), 012035 , 2020
    2020
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Recent trends on titanium metal matrix composites: A review
    DK Ammisetti, SSH Kruthiventi
    Materials Today: Proceedings 46, 9730-9735 , 2021
    2021
    Citations: 64
  • A review on reinforcements, fabrication methods, and mechanical and wear properties of titanium metal matrix composites
    DK Ammisetti, SSH Kruthiventi, S Vinjavarapu, NN Babu, JR Gandepudi, ...
    Journal of Engineering and Applied Science 71 (1), 60 , 2024
    2024
    Citations: 33
  • Experimental investigation of the influence of various wear parameters on the tribological characteristics of AZ91 hybrid composites and their machine learning modeling
    DK Ammisetti, SSH Kruthiventi
    Journal of Tribology 146 (5), 051704 , 2024
    2024
    Citations: 31
  • Experimental investigation and machine learning modeling of wear characteristics of AZ91 composites
    SSH Kruthiventi, DK Ammisetti
    Journal of Tribology 145 (10), 101704 , 2023
    2023
    Citations: 20
  • A review on mechanical and wear characteristics of magnesium metal matrix composites
    DK Ammisetti, K Sai Sarath, SSH Kruthiventi
    Journal of Tribology 147 (2), 020801 , 2025
    2025
    Citations: 18
  • Experimental analysis and artificial neural network teaching–learning-based optimization modeling on electrical discharge machining characteristics of AZ91 composites
    DK Ammisetti, SSH Kruthiventi
    Journal of Materials Engineering and Performance 33 (21), 11718-11735 , 2024
    2024
    Citations: 10
  • Experimental Investigation and Machine Learning Modeling of Tribological Characteristics of AZ31/B 4 C/GNPs Hybrid Composites
    DK Ammisetti, BK Chigilipalli, B Gaddala, RK Kottala, R Aepuru, TS Rao, ...
    Crystals 14 (12), 1007 , 2024
    2024
    Citations: 9
  • Optimization of EDM process parameters on machining characteristics of sic and graphene reinforced Al 6061-T6nano-Composites
    S Ammisetty, D Ammisetti, K Satyanarayana, S Chitturi, NS Naik
    IOP Conference Series: Materials Science and Engineering 1112 (1), 012017 , 2021
    2021
    Citations: 9
  • Experimental Investigation on the Effect of Graphene/Al 2 O 3 Reinforcements on the Microstructural, Mechanical, and Tribological Characteristics of AZ91 Alloy
    DK Ammisetti, SSH Kruthiventi
    Journal of Tribology 148 (1), 011401 , 2026
    2026
    Citations: 6
  • Multi‐objective optimization of 3D printing process parameters using hybrid TOPSIS technique for short carbon fiber reinforced PETG composites
    VVD Sahithi, S Kasturi, DK Ammisetti
    International Journal on Interactive Design and Manufacturing (IJIDeM) 20 (2 … , 2026
    2026
    Citations: 2
  • Improvement of weld joint strength by applying random vibrations along with external magnetic field
    S Chitturi, CK Mohana Krishna, M Bhaumik, DK Ammisetti
    IOP Conference Series: Materials Science and Engineering 998 (1), 012035 , 2020
    2020
    Citations: 2
  • Metal-Based Additive-Manufactured Wear-Resistant Bioimplants
    BK Chigilipalli, SB Ainapurapu, BN Dhanunjayarao, A Mavuri, ...
    Tribo-Behaviors of Biomaterials and their Applications, 118-136 , 2024
    2024
    Citations: 1
  • Experimental Investigation and Machine Learning Modeling on the Tribological Characteristics of Heat Treated AA7075/B4C/BN/SiC Hybrid Composites
    SP Reddy, DK Ammisetti, SR Juvvala, AVN Reddy
    Journal of Materials Engineering and Performance, 1-21 , 2026
    2026
  • Effect of molding sands on microstructure, mechanical, and wear properties of AA2024 aluminum alloy via CO 2 casting: an experimental study
    Dhanunjay Kumar Ammisetti,Thamatapu Eswara Rao & Kottala Ravi Kumar, Lamilla ...
    Canadian Metallurgical Quarterly 66 (1), 1-20 , 2026
    2026
  • Experimental and machine learning studies on flexural properties of 3D-printed PLA/wood composites
    VVD Sahithi, DK Ammisetti
    Journal of Reinforced Plastics and Composites, 07316844261435056 , 2026
    2026
  • Biomedical applications: magnesium hybrid composites reinforced with nanographene nanoparticles (GNPs) and Al2O3
    DK Ammisetti
    Metal and Polymer Micro and Nano Composites Nanoparticles, Nanofibers … , 2026
    2026
  • Detailed review of micro- and nanocomposites
    Dhanunjay Kumar Ammisetti, Suresh Goka and K. Aruna Prabha,V. V. D. Sahithi ...
    Metal and Polymer Micro and Nano Composites Nanoparticles, Nanofibers … , 2026
    2026
  • Experimental Investigation and Machine Learning Modeling of Electrical Discharge Machining Characteristics of AZ31/B 4 C/GNP s Hybrid Composites
    DK Ammisetti, SSH Kruthiventi, KP Arunachalam, VP Pulgar, RK Kottala, ...
    Crystals 15 (10), 844 , 2025
    2025
  • Experimental Investigation on Water Cooler Test Rig With And Without Diffusers
    Mallikarjuna Dandu ,K. Sai Babu, Nelakuditi Naresh Babu, Dhanunjay Kumar ...
    E3S Web of Conferences 391 (1), 10.1051/e3sconf/202339101101 , 2023
    2023
  • Experimental Analysis of Azolla Biodiesel Blends in CI Engine
    NS Naik, DK Ammisetti, B Kishan, S Chitturi
    Innovations in Mechanical Engineering: Select Proceedings of ICIME 2021, 379-390 , 2022
    2022