Xiangyi Cheng
Assistant Professor of Mechanical Engineering Loyola Marymount University
Biography
Office: East Hall 122
Email: xiangyi.cheng@lmu.edu
Phone: 310.568.6612
Education
Texas A&M University
Ph.D.
Mechanical Engineering
2022
China University of Mining and Technology, Beijing
B.S.E.
Mechanical Engineering
2015
Social
Areas of Expertise
Affiliations
- Institute of Electrical and Electronics Engineers
- American Society of Mechanical Engineers
- American Society for Engineering Education
Courses
ME 532 Robotics
An introduction to topics in robotics. The course will introduce two major types of robots, robot manipulators and mobile robots. Topics include kinematics, transformation, industrial robot operation and programming, motion planning, and computer vision. We will also provide an overview of microprocessor (Raspberry Pi) to illustrate how to control a robot. Students will earn valuable hands-on experience through assembling and programming an intelligent vision robot car Hiwonder GoGoPi and interact with a Fanuc robot manipulator.
ME 401/402 Capstone Project
In ME 401 and ME 402, students will complete their team-based, yearlong capstone design project. Various project options will be offered, such as student design competitions, industry-sponsored projects, and service-learning projects. Throughout the semester, student teams will undergo three major milestones. The first milestone is the System Requirements Review (SRR) in which teams will present detailed design requirements and an initial conceptual design. The second milestone is a Preliminary Design Review (PDR) where teams will present a refined and detailed design and the results of relevant design and risk analyses. The third milestone of this course is the Critical Design Review (CDR) in which teams will present their final detailed design, updated analyses, initial prototype testing results, and specifications for manufacturing the final prototype. By the end of the semester, teams will integrate advisor feedback and recommended additional work, which will feed into a Delta CDR (dCDR) that will be required in the second semester. Teams will meet at least weekly with their faculty advisor.
ENGR 1300 Engineering Visualization
Introduction to engineering drawing and sketching as a tool for design communication. Development of three-dimensional (3D) visualization skills for engineering analysis and design. Use of computer-aided design (CAD) software packages for the creation of 3D parametric solid models. Presentation of 3D geometry using two-dimensional (2D) engineering drawings. Creating orthographic planar projections from 3D isometric views including sections, dimensioning, tolerances, and abbreviations. Reading and interpreting professional grade drawings (blueprints) used in industry. Industry examples from Mechanical, Civil and Architectural Engineering will be presented. Teamwork and effective communication are emphasized.
ENGR 190 Engineering Seminar
Understanding what skills are needed in the engineering field can help you establish good habits now during your time as an engineering student. Learning about the LMU engineering community and our available resources is also helpful for your success and overall wellbeing.
ENGR 100 Introduction to Engineering Analysis, Problem Solving and Design
This course is designed to introduce basic concepts relevant to engineering and to promote interest in the profession. The course seeks to establish a solid foundation of technical, creative, teamwork, and communication skills for engineers through effective problem solving, analysis, and design techniques.
Articles
Finite Element Analysis-Assisted Surgical Planning and Evaluation of Flap Design in Hand Surgery
Frontiers in Bioengineering and Biotechnology 2025Guang Yang, Hui Shen, Yewon Jang, and Xiangyi Cheng
Given the anatomical variability among patients and the intricate geometry of the hand, the shape and size of the skin flap have traditionally relied heavily on the surgeon’s experience and subjective judgment. This dependence can lead to inconsistent and sometimes suboptimal results, particularly in complex cases such as web reconstruction in syndactyly surgery. Finite element analysis (FEA) provides a quantitative method to simulate and optimize skin flap design during surgery. However, existing FEA studies in this field are scattered across a wide range of seemingly unrelated topics. To address this, we present a comprehensive review focused on the application of FEA in skin flap design since 2000, with attention to all aspects of preprocessing and postprocessing. The primary objective is to evaluate the potential of FEA to generate patient-specific models by integrating individualized anatomical and biomechanical data while identifying key advancements, analyzing methodological challenges, exploring emerging technologies, and outlining future research directions. A critical finding is that the mechanical modeling of skin remains a major limitation in current FEA applications. To address this, future studies should focus on the development and refinement of non-invasive techniques for acquiring patient-specific skin properties. We also recommend several additional research directions based on our findings. These include exploring techniques to unfold 3D wound surfaces into 2D representations, which can improve mesh quality and computational efficiency; validating FEA simulations through large-scale, multicenter clinical studies to ensure robustness and generalizability; developing real-time AR/MR systems that integrate simulation or optimization results into surgical workflows; and creating AI-powered platforms that learn from clinical data to provide adaptive and personalized flap design recommendations. These findings offer a pathway to bridge the gap between simulation and clinical practice, ultimately aiming to improve surgical outcomes.
A Photogrammetry-Based Approach for Patient-Specific Modeling in Syndactyly Surgery: Prototyping, 3D Reconstruction, and Finite Element Analysis
ASME International Mechanical Engineering Congress and Exposition (IMECE), 2025Xiangyi Cheng, Hunter G. Geibel, Samuel J. Clawson, Yewon Jang, Tomas C. Hastings, Flint S. Guerra, Patrick D. Bak, Guang Yang, Vladimir T. Herdman, Carter M. Betts, Hui Shen
Syndactyly, a congenital condition in which fingers or toes are fused, often requires surgical intervention to restore both function and aesthetics. The procedure involves separating the fused digits, reconstructing the web space, and covering the exposed areas with a dorsal flap harvested from the patient. A major challenge in this process is determining the optimal size and shape of the dorsal flap for individuals. Our research aims to provide an objective, quantitative framework for improving surgical outcomes by generating patient-specific 3D models and using finite element analysis (FEA) for dorsal flap optimization.
As a preliminary study, this work has two main objectives: 1) developing a photogrammetry system capable of 3D reconstructing accurate hand models and 2) performing FEA to analyze stress and strain distribution in the web space from the reconstructed model to refine the dorsal flap design. A prototype with four rotating arms, each holding a camera, was built to create 3D models of the hand. A real hand was scanned and reconstructed using the prototype, and key flap parameters were extracted from the model to perform FEA evaluating a hexagonal flap design. The FEA results reveal high stress concentrations at the four corners of the flap, especially along the top edge where the flap is stretched and sutured to the palmar commissure. This pattern is consistent with surgical observations.
This consistency demonstrates the potential of integrating computational modeling into preoperative planning through FEA to improve flap design. Future work will focus on expanding the system’s analytical scope, enhancing automation, exploring effective ways to present FEA results, and improving clinical integration to advance personalized surgical planning.
The Role of AI in Boosting Cybersecurity and Trusted Embedded Systems Performance: A Systematic Review on Current and Future Trends
IEEE Access 2025Ahmed Oun, Kaden Wince, and Xiangyi Cheng
As technology becomes increasingly interconnected, ensuring the security of cyber and embedded systems is critical due to escalating vulnerabilities and sophisticated cyber threats. Researchers are exploring artificial intelligence (AI) to improve security mechanisms, yet there is a lack of a comprehensive technical, AI-focused analysis detailing the integration of AI into existing security hardware and frameworks. To address this gap, this article systematically reviews 63 articles on AI in cybersecurity and trusted embedded systems. The reviewed articles are categorized into four application domains: 1) Intrusion Detection and Prevention (IDPS), 2) Malware Detection, 3) Industrial Control and Cyber-Physical Systems (CPS) and 4) Distributed Denial-of-Service (DDoS) Detection and Prevention. We investigated current trends in integrating AI into security domains by summarizing the hardware used, the AI methodologies adopted, and the statistical distribution by publication year and region. The key findings of our review indicate that AI significantly enhances security measures by enabling capabilities such as detection, classification, feature selection, data privacy preservation, model combination, data generation, output interpretation, optimization, and adaptation. In addition, the benefits and challenges identified in these studies provide insight into the future potential of AI integration in security. Suggested directions for future work include improving generalization and scalability, exploring continuous or real-time monitoring, and improving AI model performance. This analysis serves as a foundation for advancing AI applications in the effective securing of cyber and embedded systems effectively.
Facilitating Robot Kinematics Learning through Mixed Reality: Development of an Interactive Application
IEEE Integrated STEM Education Conference 2025Kaden Wince and Xiangyi Cheng
With the growing interest in robotics within STEM, robot kinematics has become a pivotal area of study. Denavit–Hartenberg parameters (D-H parameters), which is an approach to perform robot kinematics, have thus been widely explored by students. This approach involves assigning coordinate frames to robot joints based on a set of rules and determining four parameters between every two neighboring frames, which demands strong 3D spatial reasoning. However, due to the lack of a direct 3D visualization approach, it poses great challenges for learners. To address this, we proposed a mixed reality application for HoloLens 2 that incorporates both training and testing sessions to enhance 3D visualization of the D-H parameters. The training session offers virtual coordinate frames overlaid on a physical robot, synchronizing their movements with the joints by tracking the motion of the robot links via a set of fiducial markers. During the testing session, users interact with diagrams displaying various robots to evaluate their understanding of the D-H parameters. A quantitative evaluation was conducted to assess the technology of the application, focusing on how well the coordinate frames are placed on the Fanuc LR Mate 200iD robot. While there are deviations between the generated frames and the ground truth, these are either within acceptable limits or barely noticeable. This demonstrated the feasibility of the technology and highlighted the significant potential of applied AR technology in enhancing robotics education within STEM.
Mobile Devices or Head-Mounted Displays: A Comparative Review and Analysis of Augmented Reality in Healthcare
IEEE AccessAhmed Oun, Nathan Hagerdorn, Caleb Scheideger, Xiangyi Cheng
2024-02-02
Augmented reality (AR), which combines digital rendering with the real world, has significantly shaped the healthcare system. AR technology can be utilized through a range of devices, broadly grouped into two types: mobile devices and head-mounted displays (HMDs). Mobile devices for AR usually include smartphones, tablets, and smartwatches; on the other hand, HMDs include different models of smart glasses and AR headsets. Each device type offers a unique way to experience AR, making the technology accessible and adaptable for various healthcare applications. However, the differences between using mobile devices and HMDs, and which is preferred under specific conditions, have yet to be determined. To address this, the survey provides a comparative review and analysis of the use of mobile- and HMD-based AR in the context of healthcare.
Computerized Block Games for Automated Cognitive Assessment: Development and Evaluation Study
JMIR Serious GamesXiangyi Cheng, Grover C Gilmore, Alan J Lerner, Kiju Lee
2023-05-16
Cognitive assessment using tangible objects can measure fine motor and hand-eye coordination skills along with other cognitive domains. Administering such tests is often expensive, labor-intensive, and error prone owing to manual recording and potential subjectivity. Automating the administration and scoring processes can address these difficulties while reducing time and cost. e-Cube is a new vision-based, computerized cognitive assessment tool that integrates computational measures of play complexity and item generators to enable automated and adaptive testing. The e-Cube games use a set of cubes, and the system tracks the movements and locations of these cubes as manipulated by the player.
GA-SVM-based facial emotion recognition using facial geometric features
IEEE Sensors JournalX Liu, X Cheng, K Lee
2020-10-01
This paper presents a facial emotion recognition technique using two newly defined geometric features, landmark curvature and vectorized landmark. These features are extracted from facial landmarks associated with individual components of facial muscle movements. The presented method combines support vector machine (SVM) based classification with a genetic algorithm (GA) for a multi-attribute optimization problem of feature and parameter selection.
Intubot: Design and prototyping of a robotic intubation device
2018 IEEE International Conference on Robotics and Automation (ICRA)Xiangyi Cheng, Gaojun Jiang, Kiju Lee, Yehoshua N Laker
2018-05-21
Endotracheal intubation is one of the most common procedures performed worldwide in emergency departments and operating rooms. It is a highly complicated procedure susceptible to failure. This paper presents a robotic prototype, called IntuBot, designed to automate this procedure. The hardware system consists of a stepper motor to steer the stylet in forward and backward motions and two servo motors to generate bending at the stylet tip to navigate through a patient's airway. A real-time vision-based navigation algorithm is also presented to guide the stylet to localize the vocal cords, which is the tubes ultimate target. For pre-clinical testing, we 3D printed and then molded a silicone model of the airway from the mouth to the vocal cords based on a series of actual CT scan images. The prototype was tested for its steering capabilities.
