About

Chenhui Shao is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan. His research interests encompass smart manufacturing, machine learning, statistics, big data analytics, in-situ process monitoring and real-time control, materials joining, manufacturing systems control and automation, quality control, robotic additive manufacturing, 3D metrology, cyber-physical infrastructure, and human-robot collaboration. Shao's work focuses on advancing manufacturing technologies through innovative control and automation strategies, integrating data-driven approaches to improve process efficiency, quality, and reliability. He holds a Ph.D. in Mechanical Engineering from the University of Michigan, obtained in 2016, along with a Master’s degree in Statistics and a Master’s in Industrial and Operations Engineering from the same institution. His educational background is complemented by a Bachelor of Engineering in Automation from the University of Science and Technology of China. Shao has received numerous awards recognizing his contributions, including the NSF CAREER Award, the SME Barbara M. Fossum Outstanding Young Manufacturing Engineer Award, and the RAMP Competition 2nd Prize. He has been actively involved in advancing manufacturing research and education, contributing to the integration of artificial intelligence and data science into engineering curricula, and fostering collaborations that have led to significant industry impacts, such as weld monitoring technology that helped avoid substantial repair costs.

Research topics

• Computer science
• Materials science
• Artificial intelligence
• Composite material
• Engineering

Selected publications

• Physics-guided data-driven machine health monitoring for two-photon lithography via part geometry modeling

  Journal of Manufacturing Processes · 2026-03-28

  articleOpen accessSenior authorCorresponding

  Two-photon lithography (TPL) is a sophisticated additive manufacturing technology for creating three-dimensional (3D) micro- and nano-structures. Maintaining the health of TPL systems is critical for ensuring consistent fabrication quality. Current maintenance practices often rely on experience rather than informed monitoring of machine health, resulting in either untimely maintenance that causes machine downtime and poor-quality fabrication, or unnecessary maintenance that leads to inefficiencies and avoidable downtime. To address this gap, this paper presents three methods for TPL machine health monitoring through analyzing the geometric changes in manufactured parts. Through integrating physics-guided data-driven predictive models for structure geometry with statistical approaches, the proposed methods are able to handle increasingly complex scenarios featuring different levels of generalizability. A comprehensive experimental dataset that encompasses six process parameter combinations and six structure dimensions under two machine health conditions was collected to evaluate the effectiveness of the proposed approaches. Across all test scenarios, the approaches are shown to achieve high accuracies, demonstrating excellent effectiveness, robustness, and generalizability. These results represent a significant step toward condition-based maintenance for TPL systems.

  PublisherDOI

• A Unified Hierarchical Multi-Task Multi-Fidelity Framework for Data-Efficient Surrogate Modeling in Manufacturing

  arXiv (Cornell University) · 2026-03-10

  preprintOpen accessSenior author

  Surrogate modeling is an essential data-driven technique for quantifying relationships between input variables and system responses in manufacturing and engineering systems. Two major challenges limit its effectiveness: (1) large data requirements for learning complex nonlinear relationships, and (2) heterogeneous data collected from sources with varying fidelity levels. Multi-task learning (MTL) addresses the first challenge by enabling information sharing across related processes, while multi-fidelity modeling addresses the second by accounting for fidelity-dependent uncertainty. However, existing approaches typically address these challenges separately, and no unified framework simultaneously leverages inter-task similarity and fidelity-dependent data characteristics. This paper develops a novel hierarchical multi-task multi-fidelity (H-MT-MF) framework for Gaussian process-based surrogate modeling. The proposed framework decomposes each task's response into a task-specific global trend and a residual local variability component that is jointly learned across tasks using a hierarchical Bayesian formulation. The framework accommodates an arbitrary number of tasks, design points, and fidelity levels while providing predictive uncertainty quantification. We demonstrate the effectiveness of the proposed method using a 1D synthetic example and a real-world engine surface shape prediction case study. Compared to (1) a state-of-the-art MTL model that does not account for fidelity information and (2) a stochastic kriging model that learns tasks independently, the proposed approach improves prediction accuracy by up to 19% and 23%, respectively. The H-MT-MF framework provides a general and extensible solution for surrogate modeling in manufacturing systems characterized by heterogeneous data sources.

  PublisherDOI

• Adaptive few-shot learning for robust part quality classification in two-photon lithography

  ArXiv.org · 2026-01-13

  articleOpen accessSenior author

  Two-photon lithography (TPL) is an advanced additive manufacturing (AM) technique for fabricating high-precision micro-structures. While computer vision (CV) is proofed for automated quality control, existing models are often static, rendering them ineffective in dynamic manufacturing environments. These models typically cannot detect new, unseen defect classes, be efficiently updated from scarce data, or adapt to new part geometries. To address this gap, this paper presents an adaptive CV framework for the entire life-cycle of quality model maintenance. The proposed framework is built upon a same, scale-robust backbone model and integrates three key methodologies: (1) a statistical hypothesis testing framework based on Linear Discriminant Analysis (LDA) for novelty detection, (2) a two-stage, rehearsal-based strategy for few-shot incremental learning, and (3) a few-shot Domain-Adversarial Neural Network (DANN) for few-shot domain adaptation. The framework was evaluated on a TPL dataset featuring hemisphere as source domain and cube as target domain structures, with each domain categorized into good, minor damaged, and damaged quality classes. The hypothesis testing method successfully identified new class batches with 99-100% accuracy. The incremental learning method integrated a new class to 92% accuracy using only K=20 samples. The domain adaptation model bridged the severe domain gap, achieving 96.19% accuracy on the target domain using only K=5 shots. These results demonstrate a robust and data-efficient solution for deploying and maintaining CV models in evolving production scenarios.

  PublisherOA PDF

• Feedrate Optimization via Pass-to-Pass Learning–Applied to 2.5D Contour Machining under Servo Error and Spindle Torque Constraints

  Preprints.org · 2026-03-30

  preprintOpen access

  Repeated machining passes (i.e., continuous toolpaths) are common in CNC manufacturing, including multi-level machining of prismatic parts and iso-contour passes in contour machining. They present an opportunity to exploit pass-to-pass learning to improve productivity without sacrificing quality through feedrate optimization. Traditional iterative learning methods provide a means to exploit pass-to-pass learning for quality improvements, but they are not well-suited to feedrate optimization because the reference trajectories change as the feedrate increases. In the authors’ prior work, learning-based feedrate optimization was demonstrated for repeated machining along identical toolpaths. This paper extends that concept to the more challenging case of similar but non-identical cutting paths, as encountered in contour machining. A pass-to-pass learning strategy is proposed in which corresponding sections of non-identical iso-contour passes are identified using a contour-matching method based on geometric similarity. Bayesian linear regression models are then used to learn and predict contour error and spindle torque across passes, with uncertainty explicitly quantified through credible intervals. These predictions are embedded in a window-based feedrate optimization framework solved via sequential linear programming, enabling feedrate maximization subject to kinematic, contour-error, and spindle-torque constraints. The proposed approach is experimentally validated on a 3-axis desktop CNC milling machine through multiple 2.5D contour machining case studies. Results show that the method can rapidly approach near-optimal feedrates after only a few passes, culminating in up to 16.4% increase in productivity compared to an equivalent learning-based feedrate optimization approach for identical toolpaths.

  PublisherDOI

• Physics-guided data-driven machine health monitoring for two-photon lithography via part geometry modeling

  Journal of Manufacturing Processes · 2026-03-28

  articleOpen accessSenior authorCorresponding

  Two-photon lithography (TPL) is a sophisticated additive manufacturing technology for creating three-dimensional (3D) micro- and nano-structures. Maintaining the health of TPL systems is critical for ensuring consistent fabrication quality. Current maintenance practices often rely on experience rather than informed monitoring of machine health, resulting in either untimely maintenance that causes machine downtime and poor-quality fabrication, or unnecessary maintenance that leads to inefficiencies and avoidable downtime. To address this gap, this paper presents three methods for TPL machine health monitoring through analyzing the geometric changes in manufactured parts. Through integrating physics-guided data-driven predictive models for structure geometry with statistical approaches, the proposed methods are able to handle increasingly complex scenarios featuring different levels of generalizability. A comprehensive experimental dataset that encompasses six process parameter combinations and six structure dimensions under two machine health conditions was collected to evaluate the effectiveness of the proposed approaches. Across all test scenarios, the approaches are shown to achieve high accuracies, demonstrating excellent effectiveness, robustness, and generalizability. These results represent a significant step toward condition-based maintenance for TPL systems.

  PublisherDOI

• Hybrid synthetic data generation with domain randomization enables zero-shot vision-based part inspection under extreme class imbalance

  Journal of Manufacturing Processes · 2026-04-15

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Adaptive few-shot learning for robust part quality classification in two-photon lithography

  arXiv (Cornell University) · 2026-01-13

  preprintOpen accessSenior author

  Two-photon lithography (TPL) is an advanced additive manufacturing (AM) technique for fabricating high-precision micro-structures. While computer vision (CV) is proofed for automated quality control, existing models are often static, rendering them ineffective in dynamic manufacturing environments. These models typically cannot detect new, unseen defect classes, be efficiently updated from scarce data, or adapt to new part geometries. To address this gap, this paper presents an adaptive CV framework for the entire life-cycle of quality model maintenance. The proposed framework is built upon a same, scale-robust backbone model and integrates three key methodologies: (1) a statistical hypothesis testing framework based on Linear Discriminant Analysis (LDA) for novelty detection, (2) a two-stage, rehearsal-based strategy for few-shot incremental learning, and (3) a few-shot Domain-Adversarial Neural Network (DANN) for few-shot domain adaptation. The framework was evaluated on a TPL dataset featuring hemisphere as source domain and cube as target domain structures, with each domain categorized into good, minor damaged, and damaged quality classes. The hypothesis testing method successfully identified new class batches with 99-100% accuracy. The incremental learning method integrated a new class to 92% accuracy using only K=20 samples. The domain adaptation model bridged the severe domain gap, achieving 96.19% accuracy on the target domain using only K=5 shots. These results demonstrate a robust and data-efficient solution for deploying and maintaining CV models in evolving production scenarios.

  PublisherDOI

• Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding

  ArXiv.org · 2026-04-15

  articleOpen accessSenior author

  Ultrasonic metal welding (UMW) is widely used in industrial applications but is sensitive to tool wear, surface contamination, and material variability, which can lead to unexpected process faults and unsatisfactory weld quality. Conventional monitoring systems typically rely on supervised learning models that assume all fault types are known in advance, limiting their ability to handle previously unseen process faults. To address this challenge, this paper proposes an adaptive condition monitoring approach that enables unknown fault detection and few-shot continual learning for UMW. Unknown faults are detected by analyzing hidden-layer representations of a multilayer perceptron and leveraging a statistical thresholding strategy. Once detected, the samples from unknown fault types are incorporated into the existing model through a continual learning procedure that selectively updates only the final layers of the network, which enables the model to recognize new fault types while preserving knowledge of existing classes. To accelerate the labeling process, cosine similarity transformation combined with a clustering algorithm groups similar unknown samples, thereby reducing manual labeling effort. Experimental results using a multi-sensor UMW dataset demonstrate that the proposed method achieves 96% accuracy in detecting unseen fault conditions while maintaining reliable classification of known classes. After incorporating a new fault type using only five labeled samples, the updated model achieves 98% testing classification accuracy. These results demonstrate that the proposed approach enables adaptive monitoring with minimal retraining cost and time. The proposed approach provides a scalable solution for continual learning in condition monitoring where new process conditions may constantly emerge over time and is extensible to other manufacturing processes.

  PublisherOA PDF

• LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing

  arXiv (Cornell University) · 2026-05-05

  preprintOpen accessSenior author

  Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse industries. Fused filament fabrication (FFF) has extended AM to laboratories, classrooms, and small production environments, but this accessibility shifts process-planning responsibility to users who may lack manufacturing expertise. A syntactically valid slicer profile can still encode thermally or geometrically harmful settings, and subtle G-code edits can alter extrusion, cooling, or adhesion before a print begins. Pre-print G-code screening catches accidental or adversarial machine-program errors before material or machine time is wasted. This paper proposes LLM-ADAM as a generalizable LLM framework for pre-print anomaly detection in AM. The framework decomposes the task into three roles: Extractor-LLM maps a G-code file to a structured process-parameter schema; Reference-LLM converts printer and material documentation into aligned operating ranges; and Judge-LLM interprets a deterministic deviation table and G-code evidence to decide whether a part is non-defective or belongs to an anomaly class. Printers, materials, and LLM backbones are interchangeable test conditions, not fixed assumptions. We evaluate the framework on an N=200 FFF G-code corpus spanning two desktop printer families, two materials, and five classes including non-defective, under-extrusion, over-extrusion, warping, and stringing. The best framework configuration reaches 87.5% accuracy, compared with 59.5% for the strongest engineered single-LLM baseline. The results show that structured decomposition, rather than backbone strength alone, is the dominant source of improvement, with defect classes identified at or near ceiling for leading configurations while residual errors concentrate on conservative false alarms for non-defective samples.

  PublisherDOI

• Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding

  arXiv (Cornell University) · 2026-04-15

  preprintOpen accessSenior author

  Ultrasonic metal welding (UMW) is widely used in industrial applications but is sensitive to tool wear, surface contamination, and material variability, which can lead to unexpected process faults and unsatisfactory weld quality. Conventional monitoring systems typically rely on supervised learning models that assume all fault types are known in advance, limiting their ability to handle previously unseen process faults. To address this challenge, this paper proposes an adaptive condition monitoring approach that enables unknown fault detection and few-shot continual learning for UMW. Unknown faults are detected by analyzing hidden-layer representations of a multilayer perceptron and leveraging a statistical thresholding strategy. Once detected, the samples from unknown fault types are incorporated into the existing model through a continual learning procedure that selectively updates only the final layers of the network, which enables the model to recognize new fault types while preserving knowledge of existing classes. To accelerate the labeling process, cosine similarity transformation combined with a clustering algorithm groups similar unknown samples, thereby reducing manual labeling effort. Experimental results using a multi-sensor UMW dataset demonstrate that the proposed method achieves 96% accuracy in detecting unseen fault conditions while maintaining reliable classification of known classes. After incorporating a new fault type using only five labeled samples, the updated model achieves 98% testing classification accuracy. These results demonstrate that the proposed approach enables adaptive monitoring with minimal retraining cost and time. The proposed approach provides a scalable solution for continual learning in condition monitoring where new process conditions may constantly emerge over time and is extensible to other manufacturing processes.

  PublisherDOI

Recent grants

• Collaborative Research: A Digital Manufacturing Platform to Democratize Biological Tissue Access Using Smart Two-Photon Polymerization

  NSF · $96k · 2024–2025

• CAREER: Dynamic Process-Attribute-Data-Performance Modeling to Enable Smart Ultrasonic Metal Welding

  NSF · $500k · 2020–2024

• Collaborative Research: A Digital Manufacturing Platform to Democratize Biological Tissue Access Using Smart Two-Photon Polymerization

  NSF · $250k · 2021–2024

• CAREER: Dynamic Process-Attribute-Data-Performance Modeling to Enable Smart Ultrasonic Metal Welding

  NSF · $251k · 2024–2026

Frequent coauthors

• Yuquan Meng

  24 shared

• Nenad Miljkovic

  University of Illinois Urbana-Champaign

  17 shared

• Sanjiv Sinha

  13 shared

• Manjunath C. Rajagopal

  12 shared

• Gowtham Kuntumalla

  12 shared

• Yuhang Yang

  Shenyang University of Technology

  11 shared

• Sreenath Sundar

  University of Illinois Urbana-Champaign

  11 shared

• Hanyang Zhao

  11 shared

Labs

• Shao Manufacturing LabPI

Education

• Ph.D., Mechanical Engineering

  University of Michigan

  2016

• M.S.E., Industrial and Operations Engineering

  University of Michigan

  2013

• M.A., Statistics

  University of Michigan

  2013

Awards & honors

• S.M. Wu Research Implementation Award (2025)
• Selected Attendee, Grainger Foundation Frontiers of Engineer…
• Best Paper Award, IEEE 14th Annual Ubiquitous Computing, Ele…
• Dean’s Award for Excellence in Research, The Grainger Colleg…
• Editor's Choice Article, Machines (2022)