About

Satish Bukkapatnam is a Professor and the Department Head of Industrial & Systems Engineering at Texas A&M University. His research interests include smart manufacturing, harnessing high-resolution nonlinear dynamic information from wireless micro-electromechanical systems sensors, and improving monitoring and prognostics of ultraprecision and nanomanufacturing processes and machines. He also focuses on wearable sensors for cardiorespiratory process prognostics. His work has led to five pending patents. Bukkapatnam has received numerous awards and honors, including being a Fellow of the Society of Manufacturing Engineers (SME) and the Institute of Industrial and Systems Engineering (IISE), as well as the Fulbright-Tocqueville Distinguished Chair Award for 2019-20. He holds a Ph.D. in Industrial & Manufacturing Engineering from Pennsylvania State University, obtained in 1997, and has contributed significantly to the field through research, publications, and leadership roles.

Research topics

• Computer Science
• Risk analysis (engineering)
• Business
• Engineering
• Manufacturing engineering
• Computer Security
• Materials science
• Composite material
• Political Science
• Mechanical engineering
• Philosophy
• Epistemology
• Nanotechnology
• Geology
• Law
• Marketing

Selected publications

• Disentangled multi-loss autoencoders for interpretable surface morphology in directed energy deposition (DED)

  Journal of Manufacturing Processes · 2026-04-19

  articleSenior authorCorresponding
  PublisherDOI

• Physical domain knowledge based image processing algorithm for in-situ surface porosity inspection and digitalization for 3D concrete printing structures

  Cement and Concrete Research · 2026-05-23

  articleSenior author
  PublisherDOI

• PORECAST: A Self-Calibrating Image Processing Algorithm Using Concrete Domain Knowledge for In-Process Porosity Monitoring and Digitalization of the Process

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Quality in Production—Process Control II

  2025-12-01

  book-chapter
  PublisherDOI

• Never Trust the Manufacturer, Never Trust the Client: A Novel Method for Streaming STL Files for Secure Additive manufacturing

  ArXiv.org · 2025-07-08

  preprintOpen accessSenior author

  While additive manufacturing has opened interesting avenues to reimagine manufacturing as a service (MaaS) platform, transmission of design files from client to manufacturer over networks opens up many cybersecurity challenges. Securing client's intellectual property (IP) especially from cyber-attacks emerges as a major challenge. Earlier works introduced streaming, instead of sharing process plan (G-code) files, as a possible solution. However, executing client's G-codes on manufacturer's machines exposes them to potential malicious G-codes. This paper proposes a viable approach when the client and manufacturer do not trust each other and both the client and manufacturer want to preserve their IP of designs and manufacturing process respectively. The proposed approach is based on segmenting and streaming design (STL) files and employing a novel machine-specific STL to G-code translator at the manufacturer's site in real-time for printing. This approach secures design and manufacturing process IPs as demonstrated in a real-world implementation.

  PublisherOA PDFDOI

• Investigation of the effective length of filler wire on heat transfer during laser-directed energy deposition

  Optics & Laser Technology · 2025-10-28

  article
  PublisherDOI

• DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers

  ArXiv.org · 2025-08-29

  preprintOpen access

  Industry 4.0's highly networked Machine Tool Controllers (MTCs) are prime targets for replay attacks that use outdated sensor data to manipulate actuators. Dynamic watermarking can reveal such tampering, but current schemes assume linear-Gaussian dynamics and use constant watermark statistics, making them vulnerable to the time-varying, partly proprietary behavior of MTCs. We close this gap with DynaMark, a reinforcement learning framework that models dynamic watermarking as a Markov decision process (MDP). It learns an adaptive policy online that dynamically adapts the covariance of a zero-mean Gaussian watermark using available measurements and detector feedback, without needing system knowledge. DynaMark maximizes a unique reward function balancing control performance, energy consumption, and detection confidence dynamically. We develop a Bayesian belief updating mechanism for real-time detection confidence in linear systems. This approach, independent of specific system assumptions, underpins the MDP for systems with linear dynamics. On a Siemens Sinumerik 828D controller digital twin, DynaMark achieves a reduction in watermark energy by 70% while preserving the nominal trajectory, compared to constant variance baselines. It also maintains an average detection delay equivalent to one sampling interval. A physical stepper-motor testbed validates these findings, rapidly triggering alarms with less control performance decline and exceeding existing benchmarks.

  PublisherOA PDFDOI

• Efficient screening of rare large pit anomalies on polished surfaces using a minimalist sampling scheme

  Journal of Manufacturing Processes · 2025-05-14

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• In-situ surface porosity prediction in hybrid-directed energy deposition (DED) process using explainable multimodal sensor fusion

  Journal of Manufacturing Processes · 2025-11-20 · 2 citations

  articleOpen accessSenior author

  Porosity remains one of the most critical defects in Directed Energy Deposition (DED), due to the complex thermo-mechanical interactions within the melt pool, directly limiting the reliability of additively manufactured parts. Conventional inspection methods such as X-ray computed tomography scans are expensive, time-consuming, and unsuitable for real-time intervention, while in-situ thermal imaging often lacks the resolution and sensitivity to capture limited pore formation pathways. These limitations highlight the industrial need for real-time, mechanism-aware porosity detection to enable closed-loop quality control, especially in hybrid-DED systems where sequential printing and machining cycles introduce additional complexity. In this study, synchronized multimodal sensing and explainable deep learning are employed to investigate pore formation at sub-millimeter resolution surface elements, referred to as surfels . Acoustic emission (AE), accelerometer, and thermal imaging data were synchronized to 0.1 ms temporal and 0.5 mm spatial resolution during hybrid-DED of 316L stainless steel using an Optomec–LENS® MTS 500 system. Time–frequency features extracted from these signals were used to train a convolutional neural network (CNN), which achieved a classification accuracy of 87 % in distinguishing porous from nonporous surfels. Our results indicate that sensor data from the printing cycle are more effective for pore detection than those from machining. Furthermore, explainable AI analysis with local interpretable model-agnostic explanations (LIME) revealed that high-frequency AE signatures differentiate lack-of-fusion voids, caused by insufficient inter-track overlap, from spatter-induced porosity. Porous surfels exhibited ∼37 % lower spectral energy in high-frequency AE bands compared to sound regions, while spatter-induced porosity showed ∼27 % higher energy relative to lack-of-fusion voids. These findings demonstrate that the integration of multimodal sensor fusion with explainable AI enhances porosity detection accuracy while uncovering key underlying pore formation mechanisms. The proposed framework offers a practical route towards physics-informed, real-time monitoring and control of hybrid-DED processes, with significant implications for improving build quality and reducing dependence on expensive post-process inspections. • Acoustic Emission (AE) time-frequency patterns reveal two pore modes (spatter-induced and lack-of-fusion) in Hybrid-DED. • AE mapping classifies each 1 mm surface as porous or non-porous with pore-cause identification. • AE signals show strongest pore signatures during Hybrid-DED printing, not machining.

  PublisherDOI

• When Textures Deceive: Weakly Supervised Industrial Anomaly Detection with Adapted-Loss CycleGAN

  2025-06-11

  articleSenior author

  Complex background textures challenge industrial anomaly detection (IAD) due to their intricate spatial variations, yet they are underrepresented in datasets like MVTecAD. Pixel-wise labeling is impractical, while existing weakly supervised and unsupervised methods largely overlook such textures due to the lack of suitable benchmarks. Moreover, subtle domain discrepancies in textures and occluded anomalies can degrade the performance of unsupervised approaches. GAN-based approaches, though widely used, have inherent loss function flaws reducing the effectiveness of IAD in complex environments. We introduce the Manufacturing Complex Background Texture (MCBT) dataset with 1,027 real-world images of diverse machining-induced textures, and propose Adapted-Loss Cycle-Consistent GAN (AL-CycleGAN) that leverages domain transfer while mitigating GAN limitations via a power-switch algorithm. Our analysis demonstrates that (1) MCBT introduces greater texture complexity while complementing standard datasets, (2) AL-CycleGAN achieves state-of-the-art performance on both MCBT and existing benchmarks, and (3) the proposed approach significantly improves GAN-based IAD by addressing critical loss function limitations.

  PublisherDOI

Recent grants

• I-Corps: SleepEez: Point-of-care sensor for prediction and prevention of sleep apnea

  NSF · $50k · 2014–2014

• Atomistic Dynamics of Acoustic Emission (AE) Generation in Ultra-Precision Machining (UPM) for Incipient Anomaly Detection

  NSF · $230k · 2014–2018

• Localized Finishing of Freeform Geometries using Dynamic Magnetic Field-Manipulated Magneto-Viscoelastic Fluids

  NSF · $120k · 2016–2018

• I-Corps: SleepEez: Point-of-care sensor for prediction and prevention of sleep apnea

  NSF · $50k · 2013–2014

• SGER: Sequential Bayesian Decision Making for End-Point Detection and Control in Chemical Mechanical Planarization (CMP) Processes

  NSF · $80k · 2008–2009

Frequent coauthors

• R. Komanduri

  Oklahoma State University

  59 shared

• Ashif Sikandar Iquebal

  Arizona State University

  37 shared

• Mohamed El Mansori

  École nationale supérieure d'arts et métiers

  35 shared

• Faissal Chegdani

  HESAM Université

  33 shared

• Lionel M. Raff

  Oklahoma State University

  30 shared

• Yu Ding

  Binghamton University

  30 shared

• Shilan Jin

  Texas A&M University

  29 shared

• Akash Tiwari

  29 shared

Labs

• Industrial & Systems Engineering, Texas A&M UniversityPI

Awards & honors

• Fulbright-Tocqueville Distinguished Chair Award (2019-20)
• IISE Transactions best paper award (honorable mention, 2019)
• Global Grand Challenges best undergraduate project poster aw…
• Best AggiE-challenge project award (2016, 2017)
• IISE Transactions best application paper award honorable men…