About
Sara McMains is a Professor of Mechanical Engineering at the University of California, Berkeley. Her research interests encompass a broad range of topics within computational and mechanical engineering, including geometric and solid modeling, computational geometry, and General Purpose computation on the GPU (GPGPU). She also focuses on CAD/CAM and computer aided process planning, additive manufacturing, computer vision, visualization, virtual prototyping, and virtual reality. Through her work, Professor McMains contributes to advancing the integration of computational techniques with mechanical design and manufacturing processes, leveraging modern computational tools to enhance engineering applications.
Research topics
- Computer Science
- Engineering
- Materials science
- Mechanical engineering
- Software engineering
- Algorithm
- Nanotechnology
- Biology
- Simulation
- Metallurgy
- Engineering drawing
- Chemical engineering
- Manufacturing engineering
Selected publications
Computer-Aided Design and Applications · 2025-12-03
articleOpen accessSenior authorComputer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.
Manufacturing Letters · 2025-08-01
articleOpen accessSenior authorCorrespondingDeep learning (DL) models have revolutionized automation in fields such as image classification and segmentation. In traditional computer science fields, necessary training dataset size and quality, input resolution, and input shape representation/DL architecture pairings have been carefully selected for specific tasks. Predicting additive manufacturing (AM) part quality is increasingly important as more AM parts are made as end-use parts, but these predictions are often time and resource intensive. This research compares four DL pipelines’ performance, across different dataset sizes and input resolutions, at predicting AM print quality. We train our DL pipelines on varied, real world data and systematically evaluate each model’s predictive performance, training time, and sensitivity to hyperparameter tuning across different dataset sizes and input resolutions. We build and train voxel, depth image, and distance field 3D CNN and point cloud transformer pipelines that get far superior results to a baseline model. The distance field 3D CNN model achieves the best performance, 9.62% error, predicting AM print quality compared to 24.96% error for our baseline model. We find that dataset size and input resolution both impact model performance and hyperparameter sensitivity, but that dataset size has a greater impact on model performance than input resolution for the DL pipelines we test. We gain initial insight into what shape representation/DL pipelines are promising for improving AM part quality and performance predictions. Finally, this research demonstrates a systematic way to fairly compare multiple DL pipelines to a baseline model and evaluate the impacts of changing individual variables in the DL pipeline.
Computer-Aided Design and Applications · 2025-12-03
articleOpen accessSenior authorComputer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.
2025-05-09
articleSenior author2025-05-09
articleSenior authorDesign Science · 2024-01-01 · 10 citations
articleOpen accessAbstract Improving designers’ ability to identify manufacturing constraints during design can help reduce the time and cost involved in the development of new products. Different design for additive manufacturing (DfAM) tools exist, but the design outcomes produced using such tools are often evaluated without comparison to existing tools. This study addresses the research gap by directly comparing design performance using two design support tools: a worksheet listing DfAM principles and a manufacturability analysis software tool that analyzes compliance with the same principles. In a randomized-controlled study, 49 nonexpert designers completed a design task to improve the manufacturability of a 3D-printed part using either the software tool or the worksheet tool. In this study, design outcome data (creativity and manufacturability) and design process data (task load and time taken) were measured. We identified statistically significant differences in the number of manufacturability violations in the software and worksheet groups and the creativity of the designs with novel build orientations. Results demonstrated limitations associated with lists of principles and highlighted the potential of software in promoting creativity by encouraging the exploration of alternative build orientations. This study provides support for using software to help designers, particularly nonexpert designers who rely on trial and error during design, evaluate the manufacturability of their designs more effectively, thereby promoting concurrent engineering design practices.
Journal of Iron and Steel Research International · 2023-02-18 · 8 citations
articleOpen accessSenior authorAbstract The microstructural characteristics of spherical metal powders play an important role in determining the quality of mechanical parts manufactured by powder metallurgy processes. Identifying the individual powder particles from their microscopic images is one of the most convenient and cost-efficient methods for the analysis of powder characteristics. Although numerous image processing algorithms have been developed for automating the powder particle identification process, they perform less accurately in identifying adjacent particles that are heavily overlapped in their image regions. We propose an automatic algorithm to robustly and accurately identify spherical powder particles, especially heavily overlapped particles, from their microscope images. A parallel computing framework is designed to further enhance the computational efficiency of the proposed algorithm. Powder characteristics such as particle size distribution and the location of potential satellite particles have been derived from our identification results. The accuracy and efficiency of our algorithm are validated by real-world scanning electron microscope images, outperforming other existing methods and achieving both precision and recall above 99%.
Deep reinforcement learning for stacking sequence optimization of composite laminates
Manufacturing Letters · 2023-08-01 · 6 citations
articleSenior authorCorrespondingComputational Visual Media · 2022-10-18 · 12 citations
articleOpen accessSenior authorFiber-reinforced polymer (FRP) composites are increasingly popular due to their superior strength to weight ratio. In contrast to significant recent advances in automating the FRP manufacturing process via 3D printing, quality inspection and defect detection remain largely manual and inefficient. In this paper, we propose a new approach to automatically detect, from microscope images, one of the major defects in 3D printed FRP parts: fiber-deficient areas (or equivalently, resin-rich areas). From cross-sectional microscope images, we detect the locations and sizes of fibers, construct their Voronoi diagram, and employ α-shape theory to determine fiber-deficient areas. Our Voronoi diagram and α-shape construction algorithms are specialized to exploit typical characteristics of 3D printed FRP parts, giving significant efficiency gains. Our algorithms robustly handle real-world inputs containing hundreds of thousands of fiber cross-sections, whether in general or non-general position.
Automation of intercept method for grain size measurement: A topological skeleton approach
Materials & Design · 2022 · 61 citations
Senior authorCorresponding- Computer Science
- Materials science
- Algorithm
In the microstructure characterization of metallic materials, the intercept method is one of the most widely accepted approaches to determine average grain size due to its simplicity, accuracy, and the ability to handle both equiaxed and non-equiaxed grain structures. However, its manual implementation is relatively time-consuming and error-prone, and the design of automated implementations is challenging due to the requirement of recognizing, classifying, and scoring different types of intersections (between test patterns and grain boundaries) by international standards such as ASTM E112 and EN ISO 643. In this research, a novel algorithm is proposed to automate the intercept method for grain size measurement from microscopic images. Building on topological skeletons, the algorithm is able to extract continuous and closed grain boundaries from the raw image, and determine the average grain size by recognizing and classifying different types of intersections in accordance with international standards. The effectiveness and efficiency of the proposed algorithm is validated on metallographic microscope images from both high-purity iron and stainless steel. Additionally, our algorithm has been extended to automate other standard grain size measurement methods such as the planimetric method and the whole grain area method.
Recent grants
BSF:2012362:Parallel GPU Algorithms for Proximity Analysis of Freeforms
NSF · $41k · 2013–2019
CAREER: Parallel GPU Analysis for Real-Time Manufacturability
NSF · $400k · 2006–2012
Frequent coauthors
- 16 shared
Kazuhiro Saitou
University of Massachusetts Amherst
- 16 shared
Shuming Gao
University of Massachusetts Amherst
- 16 shared
Ian R. Grosse
University of Massachusetts Amherst
- 16 shared
Adarsh Krishnamurthy
Iowa State University
- 14 shared
Hannah Budinoff
University of Arizona
- 9 shared
Rahul Khardekar
- 8 shared
Youngwook Paul Kwon
- 8 shared
Bodi Yuan
Awards & honors
- Best Paper Awards from Usenix (1995)
- Best Poster and a Best Paper Award from the ACM Solid and Ph…
- NSF CAREER Award (2005)
- Best Paper Award from ASME DETC (2000)
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