About

Ashutosh Agrawal, PhD, is a Civil Engineer by training who began his research career in earthquake engineering. Guided by the universality of physical laws, he has since transitioned into the fields of biophysics and materials science. Dr. Agrawal leads the research group Life at the Interface, which explores the engineering principles that govern the integrity and functionality of two-dimensional structures at the intersection of mechanics, geometry, and electrostatics. His work spans a wide range of topics—from studying lipid–protein interactions in neurons to designing biomimetic, two-dimensional topological materials for various engineering applications. His team employs a broad toolkit, including mathematical modeling, atomistic and Monte Carlo simulations, and finite element analysis. As an educator, Dr. Agrawal is passionate about developing innovative teaching methodologies that promote hands-on learning and deepen students’ understanding of fundamental engineering principles across disciplines.

Research topics

• Computer Science
• Business
• Engineering
• Marketing
• Economics
• Materials science
• Manufacturing engineering
• Microeconomics
• Mechanical engineering
• Medicine
• Composite material
• Risk analysis (engineering)
• Process engineering
• Finance

Selected publications

• The Impact of a Buyer's Incentives and Organizational Structure on Suppliers' Quality

  Journal of Operations Management · 2025-09-24

  articleOpen access1st authorCorresponding

  ABSTRACT Changes in a buyer organization's incentives and structure can have a significant impact on supply chain performance, such as the quality of sourced components. We examine this relationship using data from a unique quasi‐experiment where one of the two plants of a buyer introduced long‐term quality improvement incentives and a more organic structure for supply chain engineers, while the other plant did not. Analyzing longitudinal component quality data from suppliers to both plants using difference‐in‐differences and triple‐differences methods, we find that (i) the changes in incentives and structure led to differential improvements in supplier quality across the two plants, and (ii) suppliers did not easily transfer quality knowledge gained from one buyer relationship to others. Our paper contributes by showing that organizational redesign by a buyer focused on implicit incentives along with a supportive organic organizational structure can create a learning environment at its suppliers that can help improve the quality of sourced components and the learning generated does not necessarily spill over to other buyers.

  PublisherDOI

• Diamond machining of additively manufactured Ti6Al4V: Novel chip formation and friction-induced welding mechanism

  Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2025-12-19 · 1 citations

  articleOpen accessSenior author

  The ultra-precision machining of additively manufactured (AM) Ti-6Al-4V presents complex interactions between the tool, material microstructure and surface integrity. This study reveals a previously unreported solid-state friction-induced welding phenomenon occurring during the machining of AM Ti-6Al-4V, which leads to redeposited debris and localised protrusions on the machined surface. Recent studies on AM of Ti6Al4V have explored deformation and failure mechanisms during laser beam powder bed fusion fabrication; however, these have primarily focused on quasi-static compression and mechanical property optimisation. The present work extends these mechanistic insights to the dynamic cutting environment, showing that interfacial friction and severe plastic deformation can trigger solid-state bonding at the tool–chip interface without any thermal phase transformation. The findings offer a new mechanistic explanation for debris accumulation and surface artefacts in precision machining of AM titanium alloys, providing pathways for improved process control and surface quality. This finding explicitly contrasts with earlier reports that attributed such deposits to carbides formed from diamond tool wear or to titanium precipitation. Our study also uncovered a novel continuous chip formation mechanism, dominated by a plastic mode of material removal interspersed with intermittent fracture sites, differing fundamentally from the adiabatic shear-induced saw-tooth chips reported for cast Ti6Al4V. These mechanism-level insights align with recent advances in machining difficult-to-cut and anisotropic materials but extend them by establishing direct evidence of debris welding in AM Ti6Al4V. This work reframes the challenges of precision surface generation in diamond machining and highlights the need for alternative strategies to suppress friction-induced welding for optical-grade finishes.

  PublisherDOI

• EEG VMAC Toolbox: A User-Friendly Open-Source Toolbox for EEG Signals Visualization Manipulation Analysis and Classification

  Procedia Computer Science · 2025-01-01 · 1 citations

  articleOpen accessSenior author

  Electroencephalography (EEG) is a well-known, non-invasive method for monitoring and recording electrical activities of the human brain. Problem: EEG signal visualization, manipulation, analysis, and classification are essential for clinical experts, doctors, and researchers to make certain decisions. So, there is a need for a toolbox that can provide such functionalities with a user-friendly graphical user interface (GUI); availability may be commercial or open-source. Aim: This paper describes the proposed and developed open-source EEG VMAC Toolbox, which provides an interface with features including a series of state-of-the-art methods for EEG signal analysis. Method: The main menu options of EEG VMAC Toolbox are - 1) File, 2) Signal Visualization, 3) Filtering, 4) Signal Decomposition, 5) Feature Reduction, 6) Feature Extraction, 7) Label, 8) Classification Models, and 9) Help. Each menu of the toolbox contains several functionalities. In addition to these nine menus, a file conversion option is available at the bottom of the toolbox. EEG VMAC Toolbox integrates all major state-of-the-art functionalities for EEG signal visualization, manipulation, analysis, and classification, which would be a valuable addition to the current literature. Results and Findings: The EEG VMAC Toolbox has been developed using Python programming language and tested over the CHB-MIT EEG Scalp EEG dataset, a benchmark dataset for seizure detection. So, this toolbox has a provision to bring psychologists, neuroscientists, clinical experts, and EEG researchers on the same platform to pursue extensive investigation and research for better reach.

  PublisherDOI

• Universal model for facial expression detection using convolutional neural network

  AIP conference proceedings · 2024-01-01 · 2 citations

  article
  PublisherDOI

• Diamond machining of additively manufactured Ti6Al4V ELI: Newer mode of material removal challenging the current simulation tools

  Journal of Manufacturing Processes · 2024-04-26 · 4 citations

  articleOpen access

  Single point diamond machining (SPDM) produces smooth machined surfaces that other production methods cannot match. While the mechanics of machining of cast alloys with SPDM is well-explored, the realm of SPDM for additively manufactured parts remains largely uncharted. This work reveals new insights into the surface generation process of an additively manufactured titanium alloy, specifically, a Ti6Al4V Extra Low Interstitials (ELI) alloy workpiece. Our examination of the chip morphology unveiled a distinct mode of chip removal, previously unrecorded in existing literature. During SPDM of additively made Ti6Al4V ELI workpiece, identification of numerous pores and discontinuities in the chips flowing on the tool rake face, indicating periodic intermittent cracking during the material's plastic flow was seen. To examine this phenomenon, a finite element analysis (FEA) model was developed. While the FEA model can well explain the machining mechanics and chip morphology of SPDM of cast Ti6Al4V ELI reported in the literature, it failed to describe the chip morphology that are obtained during machining of additively made workpiece in this work. This disparity underscores the need for innovative simulation approaches tailored for additively manufactured components. The experimental observations in this study highlight a unique form of chip formation in contrast to conventional Ti6Al4V alloy machining processes. At lower feeds, there was a presence of short, discontinuous chip formation with tearing at the outer periphery. Conversely, at higher feeds, a long, continuous ribbon-like chip formation was observed. In addition, some typical additive manufacturing defects appear on the machined surface and chips. Through optimisation of the SPDT parameters, a surface roughness (Ra) value of about 11.8 nm was achieved on additively manufactured Ti6Al4V ELI workpiece. This work provides a fresh perspective on the mechanics of SPDM for additively manufactured components, offering a stepping stone for subsequent studies.

  PublisherDOI

• Information sharing in a green supply chain: a bane or a boon?

  Journal of Business and Industrial Marketing · 2024-08-30 · 4 citations

  articleSenior author

  Purpose The purpose of this paper is to investigate the impact of demand information sharing on products’ greening levels with downstream competition. Specifically, this study examine two types of green products, “development-intensive” (DI) and “marginal-cost intensive” (MI), in a two-echelon supply chain where the manufacturer produces substitutable products, and competing retailers operate in a market with uncertain demand. Design/methodology/approach The authors adopt the manufacturer-led Stackelberg game-theoretic framework and consider a multistage game. This study consider how retailers receive private signals about uncertain demand and decide whether to share this information with the manufacturer, who then decides whether to acquire this information at a certain given cost. This paper considers backward induction and Bayesian Nash equilibrium to solve the model. Findings The authors find that in the absence of competition, information sharing is the only equilibrium and improves the greening level under DI, whereas no-information sharing is the only equilibrium and improves the greening level under MI, an increase in downstream competition drives higher investment in greening efforts by the manufacturer in both DI and MI and the manufacturer needs to offer a payment to the retailers to obtain demand information under both simultaneous and sequential contract schemes. Originality/value This paper contributes to the literature by examining how the nature of products (margin intensive green product or development intensive green product) influences green supply chain decisions under information asymmetry and downstream competition.

  PublisherDOI

• The over-ordering problem in trade credit: Role of return policies

  European Journal of Operational Research · 2023-01-11 · 13 citations

  articleSenior author
  PublisherDOI

• The Internet Measurement Network (AIORI-IMN)

  2023-03-16 · 4 citations

  article

  This paper establishes the need of an Internet measurement network for India which is capable of measuring, collecting, analysing, collaborating and sharing metrics. The purpose is to help in advanced Internet operations research towards a fast, responsive, stable, resilient and secured Internet infrastructure. The paper using use-case demonstrates the advantages of having an indigenous Internet measurement network for stakeholders and the benefits accruing to the digital ecosystem. The paper provides a brief of the existing Internet measurement networks already running globally and introduces Advanced Internet Operations Research in India-Internet Measurement Network (AIORI-IMN). AIORI is a grant in aid program of Ministry of Electronics & Information Technology (MeitY) with Software Technology Parks of India (STPI) as the executing agency and India Internet Foundation (IIFON) as the implementing agency. The AIORI research project has two components. The first component AIORI-IMN is introduced in this paper. The second component Anycast Test Bed helps in researching and building distributed edge services, currently research on DNS deployment models are being carried out. Both the components compliment each other to achieve the said objectives.

  PublisherDOI

• Machine learning model of acoustic signatures: Towards digitalised thermal spray manufacturing

  Mechanical Systems and Signal Processing · 2023-12-19 · 5 citations

  articleOpen access

  Thermal spraying, an important industrial surface manufacturing process in sectors such as aerospace, energy and biomedical, remains a skill intensive process often involving multiple trial runs impacting the yield. The core research challenge in digitalisation of thermal spraying process lies in instrumenting the manufacturing platform as the process includes harsh conditions, including UV Rays, high-plasma temperature, dusty chemical environment, and spray booth inaccessibility. This paper introduces a novel application of machine learning to the acoustic emission spectra of thermal spraying. By transitioning from the amplitude-time domain to a Fourier-transformed frequency-time domain, it is possible to predict anomalies in real-time, a crucial step towards sustainable material and manufacturing digitalization. Our experimental results also indicate that this method is suitable for industrial applications by generating useful data that can be used to develop Visual Geometry Group (VGG) transfer learning models to overcome the traditional limitations of convoluted neural networks (CNN).

  PublisherDOI

• Agile contracting: Managing incentives under uncertain needs

  Production and Operations Management · 2022-10-22 · 8 citations

  articleOpen access

  We consider a novel principal–agent model that captures some salient features of an agile software development project. Specifically, the project is technically complex, can be modularized via a set of independent stories which are developed in sprints, and has requirements that can change over time due to exogenous changes in business needs, technologies, or market conditions. In addition, given the iterative nature of agile development, our model also captures and analyzes the interaction between two types of learning effects, namely, viability learning and cost learning, which until our paper have been examined only individually in the literature. Our paper makes the following contributions to the literature: (i) We characterize an optimal contract for the principal in closed‐form and generate managerial insights on how the agent's incentive to work changes, and consequently how the optimal contracting terms offered by the principal change, depending upon the business environment. We show that the interaction between the two learning effects and need‐risk plays an important and yet unexplored role in influencing the dynamics in the optimal contract. (ii) Using the optimal contract as the benchmark, we examine the performance of time‐and‐material contracts that are popularly used in agile projects. (iii) We obtain an optimal contract for precedence‐dependent stories in which one story must be completed before starting another story. Overall, our results provide both prescriptive and qualitative guidance to firms outsourcing agile software development projects.

  PublisherOA PDFDOI

Frequent coauthors

• Saurav Goel

  London South Bank University

  23 shared

• Suresh Muthulingam

  Pennsylvania State University

  10 shared

• Chelliah Sriskandarajah

  6 shared

• H. Dharma Kwon

  University of Illinois Urbana-Champaign

  6 shared

• Luk N. Van Wassenhove

  5 shared

• Arnoud De Meyer

  5 shared

• Xichun Luo

  5 shared

• Arshpreet Singh

  Thapar Institute of Engineering & Technology

  5 shared

Education

• Ph.D., Not provided in the HTML snippet

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• M.S., Not provided in the HTML snippet

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• B.S., Civil Engineering

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Awards & honors

• Teaching Excellence Award University of Houston, 2015