Nowrin Akter Surovi

I am deeply interested in applying artificial intelligence to advance manufacturing, with a particular focus on additive manufacturing. My research aims to automate and optimize key stages of the manufacturing workflow — including design, process planning, monitoring, defect detection, and post-processing — to enhance precision, efficiency, and productivity in next-generation manufacturing systems.

I am currently a Postdoctoral Researcher at the National Institute of Standards and Technology (NIST), where I have been working since January 2024. I earned my Ph.D. in Engineering from the Singapore University of Technology and Design (SUTD) in September 2023, specializing in intelligent manufacturing technologies.

Prior to my Ph.D., I served as a Research Assistant at the Bangladesh Council of Scientific and Industrial Research (BCSIR), contributing to applied research in industrial innovation and process development.

Curriculum Vitae

news

Oct 2, 2025	Presented at NIST AI workshop.
Aug 21, 2025	Elected as Secretary for the Systems Engineering, Information, and Knowledge Management (SEIKM) track at ASME IDETC-CIE for the 2026 leadership team.
Aug 20, 2025	Joined the International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC-CIE), 2025.
Jul 17, 2025	Awarded ASME Micro-grant
May 1, 2025	Joined the 32st Annual NIST Sigma Xi Early-Career Poster Presentation (ECPP).

selected publications

Acoustic feature based geometric defect identification in wire arc additive manufacturing

Surovi, Nowrin Akter, and Soh, Gim Song

Virtual and Physical Prototyping 2023

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Current State and Benchmarking of Generative Artificial Intelligence for Additive Manufacturing

Surovi, Nowrin Akter, Witherell, Paul, Mathew, Vinay Saji, and Kumara, Soundar

2024

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Additive Manufacturing (AM) is gaining popularity in the industry for its cost-effectiveness and time-saving benefits. However, AM encounters challenges that need to be addressed to enhance its efficiency. While Machine Learning (ML) can tackle various AM challenges, it is often limited to specific issues, necessitating multiple models. In contrast, Generative Artificial Intelligence (GenAI) has the potential to mitigate instance-specific bias due to its broader training. This paper presents a comprehensive methodology for evaluating the capabilities of various existing GenAI tools in addressing diverse AM-related tasks. We propose three categories of metrics, totaling 35 metrics, namely agnostic, domain task, and problem task metrics. Additionally, we introduce a scoring matrix, a practical tool that can be used to assess the responses of different GenAI tools. The study involves data collection from diverse published papers, which are used to create inquiries for GenAI tools. The results demonstrate that transformer-based models, such as multi-modal GPT-4 and Gemini (prev. BARD), can handle both AM image and text data. In contrast, uni-modals such as GPT-3 and Llama 2 are proficient in processing AM text data. Furthermore, image-based models such as DALL·E 3 and Stable Diffusion can accept AM text data and generate images. It is also observed that the performance of these models varies across different AM-related tasks. The variation in their performance may be due to their underlying architecture and the training dataset.
Process map generation of geometrically uniform beads using support vector machine

Surovi, Nowrin Akter, and Soh, Gim Song

Materials Today: Proceedings 2022

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