Mohammad Asif Equbal

@citranchi.ac.in

Associate Professor, Mechanical Engineering Department
Cambridge institute of Technology, Ranchi

Mohammad Asif Equbal


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https://researchid.co/asif_equbal
22

Scopus Publications

661

Scholar Citations

11

Scholar h-index

13

Scholar i10-index

Scopus Publications

  • Optimizing diesel engine performance with nanoparticle-biodiesel blends: a hybrid priority machine learning and multi-criteria decision-making approach
    Rashique Arif, Mushtaque Ahmad, Md. Asif Equbal, Azhar Equbal, Praveen Pachauri, Taufique Ahamad, Mohd Parvez, Shadab Ahmad, Haidar Howari, Brahmdeo Yadav, Osama Khan
    Results in Engineering, 2025
    • Study evaluated thirty nanoparticle–biodiesel blends for diesel engine performance. • Employed k-means clustering with MEREC-TOPSIS for optimal nanoparticle selection. • Diesel engine showed improved BTE, reduced BSFC, CO, NOx, UBHC, vibration, and noise. • Aluminium oxide ranked highest with TOPSIS CCi score of 1.000. Nanoparticles blended biodiesel fuels hold significant promise in improving performance of the diesel engine. However, selection of the most suitable nanoparticle to be blended with the biodiesel to achieve enhanced performance of the engine remains a subject of investigation. Consequently, this study has been conducted with an aim to identify the optimum nanoparticle that results in the optimal performance of the diesel engine. Different nanoparticle-biodiesel blends have been prepared and used in a diesel engine for experimental investigations. Output parameters of the diesel engine including brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), carbon mono oxide (CO), nitrogen oxide (NOx), unburnt hydrocarbon (UBHC), vibration and noise have been measured. A novel hybrid approach comprising of k-means machine learning (ML) clustering and multi criteria decision making (MCDM) methods have been used to analyse the experimental results so as to select the optimum nanoparticles. k-means clustering method has been used to group the nanoparticles in the best, the worst and the average clusters while MCDM aids in ranking the top five nanoparticles. Results reveal that CO and BSFC, with weights of 24.7% and 22%, are the highest priority output parameters, respectively. Conversely, BTE and Noise, with weights of 6.3% and 1.3% are the lowest priority output parameters, respectively. Further, aluminium oxide nanoparticle is found to be the optimum one as its rank is 1. Findings of the present study may help the decision makers to deduce well-informed decisions pertaining to the use of more efficient and environmentally friendly nanoparticle-biodiesel blends to achieve sustainable transportation solutions.
  • An experimental analysis of photovoltaic panel performance using rice husk-derived cellulose nanocrystals: a response surface methodology based k-means clustering approach
    Md Shaquib Equbal, Ibrahim Alsaduni, Aiyeshah Alhodaib, Osama Khan, Mohd Parvez
    Journal of Thermal Analysis and Calorimetry, 2025
  • A review on the rapid liquid printing (RLP): future 3D printing technology
    Azhar Equbal, Md. Asif Equbal, Zahid A. Khan, Irfan Anjum Badruddin
    Progress in Additive Manufacturing, 2025
  • Machine learning in additive manufacturing: A comprehensive insight
    Md Asif Equbal, Azhar Equbal, Zahid A. Khan, Irfan Anjum Badruddin
    International Journal of Lightweight Materials and Manufacture, 2025
    Additive manufacturing (AM) is a technological advancement gaining colossal popularity due to its advantages and simplified fabrication. AM facilitates the manufacturing of complex, light, and strong products from digitized designs. With recent advancements, AM can bring digital flexibility and improved efficiency to industrial operations. Despite the various advantages, there is continuous variation in the qualities of AM products, which remains the main challenge in the wide application of AM. The performance of printed parts is directly influenced by processing parameters, and adjusting the parameters in the AM process can be quite challenging. The barrier can be minimized by proper monitoring of the AM process and precise measurement of AM materials and components, which is difficult to achieve through analytical and numerical models. Current research demonstrates machine learning (ML) and its techniques as a novel way to reduce costs. It also helps achieve optimal process design and part quality using the fundamentals of the AM process. ML is a subcategory of artificial intelligence (AI) that enables systems to learn and improve from measured data and past experiences. The present paper is focused on presenting a broad understanding of the current applications of ML in AM and thus provides a solid background for practitioners and researchers to apply ML in AM. Very few earlier reviews were presented before, but their studies mostly focus on artificial neural network technology and other irrelevant papers. In addition, most papers were published in 2021 and 2022 and were not discussed in earlier reviews. This state-of-the-art review is based on the latest database collected from Web of Science (WoS), Publons, Scopus, and Google Scholar using machine learning and additive manufacturing as the keywords. Extensive information collected on the possible applications of ML in AM shows that ML can be effectively applied to improve AM part quality and process reliability.
  • Machine Learning–Based Optimization for FDM-Printed Poly Lactic Acid Parts
    Pranav Ravindrannair, Azhar Equbal, VMS Hussain, Md. Israr Equbal, Md. Asif Equbal
    Process Modeling and Optimization in Modern Manufacturing, 2025
    This chapter analyzes the experimental-based investigation to determine the optimal process parameter settings for 3D printing a spur gear in a fused deposition modeling (FDM) machine using poly lactic acid (PLA) as the printing material. Crucial FDM process parameters like printing speed ( https://www.w3.org/1998/Math/MathML" display="inline"> v https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0204.tif"/> ), layer thickness ( https://www.w3.org/1998/Math/MathML" display="inline"> t https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0205.tif"/> ), and fill density ( https://www.w3.org/1998/Math/MathML" display="inline"> ρ https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0206.tif"/> ) were undertaken to proceed with the investigation. The effect of process parameters on two vital quality characteristics namely surface roughness and component hardness were evaluated. A design methodology based on response surface methodology (RSM) is used to setup the experimental runs and experiments were conducted following the face-centered central composite design. ANOVA techniques were employed to ascertain the significance of the parameters. In addition, 3D surface plots are used to show the effect of input process parameters on studied quality characteristics. The optimal process parameters are determined using desirability function approach which yields the desirable quality characteristics and additionally the particle swarm optimization (PSO)-based optimization was also attempted. It was observed that the optimum values of SR and Hardness have been observed as: 3.53 durometer scale and 33.21 https://www.w3.org/1998/Math/MathML" display="inline"> μ m https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0207.tif"/> under the parameters level of https://www.w3.org/1998/Math/MathML" display="inline"> 30 m m 3 /sec https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0208.tif"/> printing speed, layer thickness is https://www.w3.org/1998/Math/MathML" display="inline"> 0 .1867 mm https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0209.tif"/> , and infill density at 20% using PSO method.
  • Enhancing hydrogen production: A study on steam reforming using advanced nanocomposites
    Ramsha Aalam, Zarqa Shamshad, Ayush Kumar Gautam, Anurag Yadav, Md Shaquib Equbal, Azhar Equbal
    Next Generation Renewable Thermal Energy Harvesting Conversion and Storage Technologies, 2025
  • Sustainable coatings for green solar photovoltaic cells: performance and environmental impact of recyclable biomass digestate polymers
    Aiyeshah Alhodaib, Zeinebou Yahya, Osama Khan, Azhar Equbal, Md Shaquib Equbal, Mohd Parvez, Ashok Kumar Yadav, M. Javed Idrisi
    Scientific Reports, 2024
    The underutilization of digestate-derived polymers presents a pressing environmental concern as these valuable materials, derived from anaerobic digestion processes, remain largely unused, contributing to pollution and environmental degradation when left unutilized. This study explores the recovery and utilization of biodegradable polymers from biomass anaerobic digestate to enhance the performance of solar photovoltaic (PV) cells while promoting environmental sustainability. The anaerobic digestion process generates organic residues rich in biodegradable materials, often considered waste. However, this research investigates the potential of repurposing these materials by recovering and transforming them into high-quality coatings or encapsulants for PV cells. The recovered biodegradable polymers not only improve the efficiency and lifespan of PV cells but also align with sustainability objectives by reducing the carbon footprint associated with PV cell production and mitigating environmental harm. The study involves a comprehensive experimental design, varying coating thickness, direct normal irradiance (DNI) (A), dry bulb temperature (DBT) (B), and relative humidity (C) levels to analyze how different types of recovered biodegradable polymers interact with diverse environmental conditions. Optimization showed that better result was achieved at A = 8 W/m2, B = 40 °C and C = 70% for both the coated material studied. Comparative study showed that for enhanced cell efficiency and cost effectiveness, EcoPolyBlend coated material is more suited however for improving durability and reducing environmental impact NanoBioCelluSynth coated material is preferable choice. Results show that these materials offer promising improvements in PV cell performance and significantly lower environmental impact, providing a sustainable solution for renewable energy production. This research contributes to advancing both the utilization of biomass waste and the development of eco-friendly PV cell technologies, with implications for a more sustainable and greener energy future. This study underscores the pivotal role of exploring anaerobic digestate-derived polymers in advancing the sustainability and performance of solar photovoltaic cells, addressing critical environmental and energy challenges of our time.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 7 Given name: [Ashok] Last name [Kumar Yadav]. Also, kindly confirm the details in the metadata are correct.correct
  • Comparative analysis of machine learning and artificial intelligence models for optimizing mixed refrigerant characteristics in a hydrogen pre-cooling storage system
    Md Shaquib Equbal, Osama Khan, Azhar Equbal, Mohd Parvez, Shadab Ahmad, Zeinebou Yahya, Aiyeshah Alhodaib, Ashok Kumar Yadav, Ümit Ağbulut
    Journal of Energy Storage, 2024
  • Grey based Taguchi method for multi-response optimization of FSW of aluminium AA 6061 alloy
    Pranav Ravindrannair, Azhar Equbal, Md. Asif Equbal, Kuldeep K. Saxena, Md. Israr Equbal
    International Journal on Interactive Design and Manufacturing, 2024
  • A recent review on advancements in dimensional accuracy in fused deposition modeling (FDM) 3D printing
    Azhar Equbal, Ramesh Murmu, Veenit Kumar, Md. Asif Equbal
    Aims Materials Science, 2024
    <p>Fused deposition modeling (FDM) fabricated components have gained significant attention and widespread adoption across modern industries due to their versatility, serving as both prototypes and final products. FDM offers rapid and cost-effective prototyping and production capabilities; however, utilizing directly manufactured FDM parts is not practical. Secondary operations like post-processing, testing, and validation are typically required to ensure that the fabricated parts meet the necessary standards for their intended applications. Desired repeatability, reproducibility, reliability, and preciseness should be the main prerequisites of the part fabricated. It is desirable that additive manufacturing (AM) products should be produced with advanced control processes which should possess acceptable quality characteristics. Ensuring the dimensional accuracy of FDM parts is very crucial, and hence it is important to emphasize the key factors that influence the dimensional precision during their fabrication. Sharing insights into these critical factors is essential to steer scholars, researchers, and the AM industry towards informed decisions and future advancements in AM. We aimed to outline the significant factors influencing the dimensional accuracy of the FDM part. These research papers are collected from Scopus and web of science data using "FDM" and "dimensional accuracy" as the keywords. We include the latest papers published especially during 2020 to 2024, which were lacking in earlier research.</p>
  • Evaluating CNC Milling Performance for Machining AISI 316 Stainless Steel with Carbide Cutting Tool Insert
    Azhar Equbal, Md. Asif Equbal, Md. Israr Equbal, Pranav Ravindrannair, Zahid A. Khan, Irfan Anjum Badruddin, Sarfaraz Kamangar, Vineet Tirth, Syed Javed, M. I. Kittur
    Materials, 2022
  • Characteristics of Conventional and Microwave Sintered Iron Ore Preform
    Azhar Equbal, Mohammad Ali, Md. Asif Equbal, S. C. Srivastava, Zahid A. Khan, Md. Israr Equbal, Irfan Anjum Badruddin, Khalid Mohamed El-Hady, Sarfaraz Kamangar
    Materials, 2022
  • Investigating the dimensional accuracy of the cavity produced by abs p400 polymer-based novel edm electrode
    Azhar Equbal, Asif Equbal, Zahid A. Khan, Irfan Anjum Badruddin, Mohamed Bashir Ali Bashir, Hussein Alrobei
    Polymers, 2021
  • A Review and Reflection on Part Quality Improvement of Fused Deposition Modelled Parts
    A Equbal, Md. A. Equbal, A K Sood, R Pranav, Md. I. Equbal
    Iop Conference Series Materials Science and Engineering, 2018
  • Optimisation of forging parameters of 35C8 steel using grey relational analysis
    Md Israr Equbal, Azhar Equbal, Md Asif Equbal, R. Pranav
    International Journal of Microstructure and Materials Properties, 2018
  • A comparative study on electroplating of FDM parts
    Azhar Equbal, Md. Israr Equbal, Anoop Kumar Sood, Md. Asif Equbal
    International Journal of Technology, 2017
  • A comprehensive model for supply chain performance measurement: Application in the coal beneficiation plant of steel manufacturing company
    Md. Asif Equbal, Azhar Equbal, Archana Kumari, Rajkumar Ohdar
    IEEE International Conference on Industrial Engineering and Engineering Management, 2017
  • A comprehensive supply chain performance measurement and evaluation (Cspme) methodology
    International Journal of Mechanical and Production Engineering Research and Development, 2017
  • Optimization of process parameters of FDM part for minimiizing its dimensional inaccuracy
    International Journal of Mechanical and Production Engineering Research and Development, 2017
  • An investigation on sliding wear of FDM built parts
    Anoop Kumar Sood, Asif Equbal, Vijay Toppo, R.K. Ohdar, S.S. Mahapatra
    CIRP Journal of Manufacturing Science and Technology, 2012
  • Prediction of dimensional accuracy in fused deposition modelling: A fuzzy logic approach
    Asif Equbal, Anoop Kumar Sood, S.S. Mahapatra
    International Journal of Productivity and Quality Management, 2011
  • Prediction and analysis of sliding wear performance of fused deposition modelling-processed ABS plastic parts
    A Equbal, A K Sood, V Toppo, R K Ohdar, S S Mahapatra
    Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology, 2010

RECENT SCHOLAR PUBLICATIONS

  • RSM-Based Statistical Approach to Enhance the Compressive Performance of ABS P400 Parts Fabricated via FDM Technology
    A Equbal, MA Equbal, R Ahmad, M Kumar, HRISHIKESH, O Khan, ...
    Surface Review and Letters 33 (04), 2550021 , 2026
    2026
    Citations: 11
  • Experimental Investigation and Machine Learning-Based Optimization of Abrasive Water Jet Drilling in GFRP Laminates
    UA Kumar, R Arif, MI Equbal, A Equbal
    International Journal of Lightweight Materials and Manufacture , 2026
    2026
  • Machine Learning–Based Optimization for FDM-Printed Poly Lactic Acid Parts
    P Ravindrannair, A Equbal, VMS Hussain, MI Equbal, MA Equbal
    Process Modeling and Optimization in Modern Manufacturing, 149-161 , 2025
    2025
  • Multi-Objective Optimization Approaches for Enhancing Building Energy Efficiency through Material and Equipment Selection
    R Arif, MQ Tabrej, A Equbal, MA Equbal, IA Badruddin, ERI Mahmoud, ...
    Case Studies in Thermal Engineering, 107209 , 2025
    2025
    Citations: 1
  • Optimizing Diesel Engine Performance with Nanoparticle-Biodiesel Blends: A Hybrid Priority Machine Learning and Multi-Criteria Decision-Making Approach
    R Arif, M Ahmad, MA Equbal, A Equbal, P Pachauri, T Ahamad, M Parvez, ...
    Results in Engineering, 107491 , 2025
    2025
    Citations: 3
  • A review on the rapid liquid printing (RLP): future 3D printing technology
    A Equbal, MA Equbal, ZA Khan, IA Badruddin
    Progress in Additive Manufacturing 10 (5), 3157-3173 , 2025
    2025
    Citations: 5
  • Machine learning in additive manufacturing: A comprehensive insight
    MA Equbal, A Equbal, ZA Khan, IA Badruddin
    International Journal of Lightweight Materials and Manufacture 8 (2), 264-284 , 2025
    2025
    Citations: 44
  • A critical review on electro-discharge machining of non-conductive materials
    A Equbal, A Equbal, H VMS, I Equbal, IA Badruddin, S Kamangar, ...
    Machining Science and Technology 28 (6), 1092-1128 , 2024
    2024
    Citations: 8
  • Grey based Taguchi method for multi-response optimization of FSW of aluminium AA 6061 alloy
    P Ravindrannair, A Equbal, MA Equbal, KK Saxena, MI Equbal
    International Journal on Interactive Design and Manufacturing (IJIDeM) 18 (3 … , 2024
    2024
    Citations: 9
  • A recent review on advancements in dimensional accuracy in fused deposition modeling (FDM) 3D printing
    A Equbal, R Murmu, V Kumar, MA Equbal
    AIMS Materials Science 11 (5), 950-990 , 2024
    2024
    Citations: 32
  • Optimizing the machining parameters during turning of EN 31 steel using TiN+ Al2O3+ TiCN coated tungsten carbide tool
    J Prakash, A Equbal, P Ravindrannair, MA Equbal, MI Equbal
    Materials Today: Proceedings , 2023
    2023
    Citations: 3
  • Analysis of The Crack in The Thick Pressure Vessel
    A Kumar, MDA Equbal, B Prasad, MK Sahu
    Materials and Structures 3 (01), 85-94 , 2023
    2023
  • Study and Analysis of Failure Analysis of Wire Rope
    RK Prasad, AR Ansari, MDA Equbal, MK Sahu
    2023
  • Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert
    A Equbal, MA Equbal, MI Equbal, P Ravindrannair, ZA Khan, ...
    Materials 15 (22), 8051 , 2022
    2022
    Citations: 38
  • 5 Wearing Behaviour
    A Equbal, MA Equbal
    Advanced Manufacturing Methods: Smart Processes and Modeling for … , 2022
    2022
  • Wearing Behaviour of Electrodes during EDM of AISI 1035 Steel
    A Equbal, MA Equbal
    Advanced Manufacturing Methods, 101-112 , 2022
    2022
  • Characteristics of conventional and microwave sintered iron ore preform
    A Equbal, M Ali, MA Equbal, SC Srivastava, ZA Khan, MI Equbal, ...
    Materials 15 (7), 2655 , 2022
    2022
    Citations: 3
  • Investigating the dimensional accuracy of the cavity produced by ABS P400 polymer-based novel EDM electrode
    A Equbal, A Equbal, ZA Khan, IA Badruddin, MBA Bashir, H Alrobei
    Polymers 13 (23), 4109 , 2021
    2021
    Citations: 18
  • Application of machine learning in fused deposition modeling: a review
    A Equbal, S Akhter, MA Equbal, AK Sood
    Fused Deposition Modeling Based 3D Printing, 445-463 , 2021
    2021
    Citations: 14
  • A Reflection on the Use of Additive Manufacturing in Nephrology for Education and Surgical Planning
    A Equbal, S Akhtar, MA Equbal
    Apollo Medicine 17 (4), 264-266 , 2020
    2020
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • An investigation on sliding wear of FDM built parts
    AK Sood, A Equbal, V Toppo, RK Ohdar, SS Mahapatra
    CIRP Journal of Manufacturing Science and Technology 5 (1), 48-54 , 2012
    2012.0
    Citations: 203
  • Prediction of dimensional accuracy in fused deposition modelling: a fuzzy logic approach
    A Equbal, AK Sood, SS Mahapatra
    International Journal of Productivity and Quality Management 7 (1), 22-43 , 2011
    2011.0
    Citations: 74
  • Optimization of process parameters of FDM part for minimiizing its dimensional inaccuracy
    A Equbal, AK Sood, AR Ansari, MA Equbal
    International Journal of Mechanical and Production Engineering Research and … , 2017
    2017.0
    Citations: 60
  • Prediction and analysis of sliding wear performance of fused deposition modelling-processed ABS plastic parts
    A Equbal, AK Sood, V Toppo, RK Ohdar, SS Mahapatra
    Proceedings of the Institution of Mechanical Engineers, Part J: Journal of … , 2010
    2010.0
    Citations: 47
  • Machine learning in additive manufacturing: A comprehensive insight
    MA Equbal, A Equbal, ZA Khan, IA Badruddin
    International Journal of Lightweight Materials and Manufacture 8 (2), 264-284 , 2025
    2025.0
    Citations: 44
  • Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert
    A Equbal, MA Equbal, MI Equbal, P Ravindrannair, ZA Khan, ...
    Materials 15 (22), 8051 , 2022
    2022.0
    Citations: 38
  • A recent review on advancements in dimensional accuracy in fused deposition modeling (FDM) 3D printing
    A Equbal, R Murmu, V Kumar, MA Equbal
    AIMS Materials Science 11 (5), 950-990 , 2024
    2024.0
    Citations: 32
  • Metallization on FDM processed parts using electroless procedure
    A Equbal, A Equbal, AK Sood
    Procedia materials science 6, 1197-1206 , 2014
    2014.0
    Citations: 32
  • A review and reflection on part quality improvement of fused deposition modelled parts
    A Equbal, MA Equbal, AK Sood, R Pranav, MI Equbal
    IOP Conference Series: Materials Science and Engineering 455 (1), 012072 , 2018
    2018.0
    Citations: 19
  • Investigating the dimensional accuracy of the cavity produced by ABS P400 polymer-based novel EDM electrode
    A Equbal, A Equbal, ZA Khan, IA Badruddin, MBA Bashir, H Alrobei
    Polymers 13 (23), 4109 , 2021
    2021.0
    Citations: 18
  • Application of machine learning in fused deposition modeling: a review
    A Equbal, S Akhter, MA Equbal, AK Sood
    Fused Deposition Modeling Based 3D Printing, 445-463 , 2021
    2021.0
    Citations: 14
  • RSM-Based Statistical Approach to Enhance the Compressive Performance of ABS P400 Parts Fabricated via FDM Technology
    A Equbal, MA Equbal, R Ahmad, M Kumar, HRISHIKESH, O Khan, ...
    Surface Review and Letters 33 (04), 2550021 , 2026
    2026.0
    Citations: 11
  • A Comparative Study on Electroplating of FDM Parts
    A Equbal, MI Equbal, A Sood, MA Equbal
    Citations: 10
  • Grey based Taguchi method for multi-response optimization of FSW of aluminium AA 6061 alloy
    P Ravindrannair, A Equbal, MA Equbal, KK Saxena, MI Equbal
    International Journal on Interactive Design and Manufacturing (IJIDeM) 18 (3 … , 2024
    2024.0
    Citations: 9
  • A critical review on electro-discharge machining of non-conductive materials
    A Equbal, A Equbal, H VMS, I Equbal, IA Badruddin, S Kamangar, ...
    Machining Science and Technology 28 (6), 1092-1128 , 2024
    2024.0
    Citations: 8
  • A Comprehensive Supply Chain Performance Measurement and Evaluation (CSPME) Methodology
    A Equbal, R Ohdar
    International Journal of Mechanical and Production Engineering Research and … , 2017
    2017.0
    Citations: 6
  • An investigation on material removal rate of EDM process: a response surface methodology approach
    A Equbal, AK Sood, MA Equbal, MI Equbal
    World Acad. Sci. Eng. Technol. Int. J. Mech. Aerospace, Ind. Mechatron … , 2017
    2017.0
    Citations: 6
  • A review on the rapid liquid printing (RLP): future 3D printing technology
    A Equbal, MA Equbal, ZA Khan, IA Badruddin
    Progress in Additive Manufacturing 10 (5), 3157-3173 , 2025
    2025.0
    Citations: 5
  • Multi-criterion decision method for roughness optimization of fused deposition modelled parts
    A Equbal, MA Equbal, MI Equbal, AK Sood
    Additive Manufacturing Technologies from an Optimization Perspective, 235-262 , 2019
    2019.0
    Citations: 4
  • Optimizing Diesel Engine Performance with Nanoparticle-Biodiesel Blends: A Hybrid Priority Machine Learning and Multi-Criteria Decision-Making Approach
    R Arif, M Ahmad, MA Equbal, A Equbal, P Pachauri, T Ahamad, M Parvez, ...
    Results in Engineering, 107491 , 2025
    2025.0
    Citations: 3