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1. Using machine learning to predict engineering technology students’ success with computer‐aided design

   https://doi.org/10.1002/cae.22489  

   Singh, Jasmine; Perera, Viranga; Magana, Alejandra J.; Newell, Brittany; Wei‐Kocsis, Jin; Seah, Ying Ying; Strimel, Greg J.; Xie, Charles (January 2022, Computer Applications in Engineering Education)

   Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open-source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task. We challenged students to design a house that consumed zero net energy as part of an introductory engineering technology undergraduate course. Using data from 128 students, along with the scikit-learn Python machine learning library, we tested our models using both total counts of design actions and sequences of design actions as inputs. We found that our models using early design sequence actions are particularly valuable for prediction. Our logistic regression model achieved a >60% chance of predicting if a student would succeed in designing a zero net energy house. Our results suggest that it would be feasible for Aladdin to provide useful feedback to students when they are approximately halfway through their design. Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning. 

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2. Establishing a peer review system for open-source educational CAD models

   Bull, G; Novitski, R; Trumble, J (June 2025, Contemporary issues in technology and teacher education)

   Mouza, C (Ed.)

   Since the advent of affordable fabrication technologies such as 3D printers, many schools have established maker spaces. The educational effectiveness of fabrication tools in K-12 maker spaces is facilitated by access to useful Computer-Assisted Design (CAD) models and associated instructional supports. The launch of Educational Fabrication & Design provides a site for peer review and publication of CAD models designed for K-12 education. Upon acceptance, articles will be published with a link to a corresponding model in an educational CAD Model Repository. 

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3. Automated physics-based modeling of construction equipment through data fusion

   https://doi.org/10.1016/j.autcon.2024.105880  

   Xu, Liqun; Veeramani, Dharmaraj; Zhu, Zhenhua (December 2024, Automation in Construction)

   Physics-based simulations are essential for designing autonomous construction equipment, but preparing models is time-consuming, requiring the integration of mechanical and geometric data. Current automatic modeling methods for modular robots are inadequate for construction equipment. This paper explores automating the modeling process by integrating mechanical data into 3D computer-aided design (CAD) models. A template library is developed with hierarchy and joint templates specific for equipment. During model generation, appropriate templates are selected based on the equipment type. Unspecified joint template data is extracted from technical specifications using a large language model (LLM). The 3D CAD model is then converted into a Universal Scene Description (USD) model. Users can adjust the part names and hierarchy within the USD model to align with the hierarchy template, and joint data is automatically integrated, resulting in a simulation-ready model. This method reduces modeling time by over 87 % compared to manual methods, while maintaining accuracy. 

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4. Computational metadata generation methods for biological specimen image collections

   https://doi.org/10.1007/s00799-022-00342-1  

   Karnani, Kevin; Pepper, Joel; Bakiş, Yasin; Wang, Xiaojun; Bart, Henry; Breen, David E.; Greenberg, Jane (November 2022, International Journal on Digital Libraries)

   Adam, N.; Neuhold, E.; Furuta, R. (Ed.)

   Metadata is a key data source for researchers seeking to apply machine learning (ML) to the vast collections of digitized biological specimens that can be found online. Unfortunately, the associated metadata is often sparse and, at times, erroneous. This paper extends previous research conducted with the Illinois Natural History Survey (INHS) collection (7244 specimen images) that uses computational approaches to analyze image quality, and then automatically generates 22 metadata properties representing the image quality and morphological features of the specimens. In the research reported here, we demonstrate the extension of our initial work to University of the Wisconsin Zoological Museum (UWZM) collection (4155 specimen images). Further, we enhance our computational methods in four ways: (1) augmenting the training set, (2) applying contrast enhancement, (3) upscaling small objects, and (4) refining our processing logic. Together these new methods improved our overall error rates from 4.6 to 1.1%. These enhancements also allowed us to compute an additional set of 17 image-based metadata properties. The new metadata properties provide supplemental features and information that may also be used to analyze and classify the fish specimens. Examples of these new features include convex area, eccentricity, perimeter, skew, etc. The newly refined process further outperforms humans in terms of time and labor cost, as well as accuracy, providing a novel solution for leveraging digitized specimens with ML. This research demonstrates the ability of computational methods to enhance the digital library services associated with the tens of thousands of digitized specimens stored in open-access repositories world-wide by generating accurate and valuable metadata for those repositories. 

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5. Organic-flow: An open-source organic standard cell library and process development kit

   Chang, T.-J.; Yao, Z.; Rand, B.P.; Wentzlaff, D. (April 2020, Design, Automation and Test in Europe (DATE) 2020)

   Organic thin-film transistors (OTFTs) are drawing increasing attention due to their unique advantages of mechanical flexibility, low-cost fabrication, and biodegradability, enabling diverse applications that were not achievable using traditional inorganic transistors. With a growing number of complex applications being proposed, the need for expediting the design process and ensuring the yield of large-scale designs with organic technology increases. A complete digital standard cell library plays a crucial role in integrating the emerging organic technology into existing computer-aided-design (CAD) flows. In this paper, we present the design, fabrication, and characterization of a standard cell library based on bottom gate, top contact pentacene OTFTs. We also propose a commercial tool compatible, RTL-to-GDS flow along with a new organic process design kit (PDK) developed based on our process. To the best of our knowledge, this is the first open-source organic standard cell library, enabling the community to explore this emerging technology. 

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