2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023): Education
Program Organizers: Joseph Beaman, University of Texas at Austin

Tuesday 2:40 PM
August 15, 2023
Room: Salon F
Location: Hilton Austin

Session Chair: Doug Sassaman, University of Texas Austin

2:40 PM
A Case Study of Cognitive Workload and Design Knowledge Gaps in Hybrid Manufacturing Teams: Kenton Fillingim¹; Thomas Feldhausen¹; ¹Oak Ridge National Laboratory
    Hybrid manufacturing systems are an emerging technology hoping to emphasize the benefits, while mitigating the drawbacks, of both additive and subtractive processes in their typical, separate states. However, this also holds system operators accountable for smooth production in both processes. This is additionally challenging due to the knowledge gaps and lack of readily transferrable hybrid knowledge available. This research presents a case study identifying the cognitive load and knowledge gap impacts on operators across three hybrid manufacturing systems. Operators completed surveys consisting of a cognitive workload scale, factors contributing to cognitive load, and a part quality assessment. Operators self-reported skill, rule, and knowledge-based (SRK) errors contributing to process setbacks. Preliminary results show statistically significant changes in part quality and cognitive workload factors when reporting instances of SRK mistakes. This work provides a foundation towards improving hybrid systems workspace design by better understanding operator interaction with the system and the designer.

3:00 PM
Design For(e!) Additive Manufacturing: In Search of a Comprehensive Design Challenge Suitable Across AM Education: Nicholas Meisel¹; ¹Pennsylvania State University
    Design for additive manufacturing (DfAM) education tends to emphasize the use of problem-based learning (PBL) to encourage student learning. Unfortunately, dedicated DfAM education is still nascent, which results in a wide range of educators leveraging an equally wide, and often unproven, range of design challenges to support PBL. Because of this, it is possible that a chosen design challenge will not represent AM as an end-use manufacturing process nor promote a design space that benefits from full consideration of all opportunistic and restrictive DfAM concepts. In this paper, the author draws on lessons learned from years of DfAM-centric coursework at both the undergraduate and graduate levels to enumerate the need for three key aspects for a successful DfAM challenge in education: clarity, applicability, and demonstrability. In doing so, the author discusses and defends a comprehensive design challenge that is suitable across the range of AM education: a golf putter.

3:20 PM
Multi-material Design in Metal Additive Manufacturing: Dennis Lehnert¹; Thomas Tröster¹; Christian Boedger¹; Stefan Gnaase¹; ¹Paderborn University
    Additive manufacturing has the economic potential to complement conventional manufacturing processes, particularly in producing complex, multi-material components. However, to fully realize the benefits of optimized lightweight structures, it's necessary to use dissimilar materials with different physical properties. These multi-material combinations from conventional processes aren't compatible with AM due to their differing material properties. In addition, geometric tolerances, recycling strategies for waste and the component’s topology restrictions aren't yet defined. The european funded project “MADE3D” addresses these challenges by the concurrent engineering of designing materials, developing design concepts for multi-material structures, and adapting the process for L-PBF and DED. Advanced computational material engineering will aid alloy and process development, and recycling concepts for powder and will promote sustainability. These adaptations will increase process reliability and speed, facilitating the dissemination of multi-material additive manufacturing throughout the industry.

3:40 PM
Development of a Testbench for Additive Manufacturing Data Integration, Management, and Analytics: Chen-Wei Yang¹; Alexander Kuan¹; Yan Lu¹; Sheng Yen Li¹; Jaehyuk Kim¹; Fan Tien Cheng²; Haw Ching Yang³; ¹Engineering Lab, National Institute of Standards and Technology (NIST); ²Institute of Manufacturing Information and Systems, National Cheng Kung University (NCKU); ³Department of Electrical Engineering, National Kaohsiung University of Science and Technology (NKUST)
     This presentation describes the NIST additive manufacturing (AM) data integration testbench that is setup to test AM data integration functions, including high speed in-process data acquisition, real-time feature extraction, process monitoring, process control, predictive modeling based on machine learning, and findable, accessible, interoperable and reusable data management. Through investigations based on the testbench, data integration requirements are collected, and solutions are developed for various AM data exchange and data analytics scenarios. A reference architecture, common information models and function interfaces are also developed for AM system integration. In addition, AM data streaming and integration with MES and ERP systems are also explored using a high-performance data warehouse for long-term data archiving and metadata management. The NIST AM Testbench provides a platform to conduct data and software integration tests and functionality evaluation from data collecting, data analysis to manufacturing operational intelligence. AM data integration capabilities can be optimized for AM industrialization

4:00 PM
Development of 3D Printable Part Library for Easy to Manufacture Components for Educational and Competetive Robotics: Indira Dwivedi¹; Bharat Dwivedi²; Arun Rebbapragada³; Rajeev Dwivedi⁴; Arka Rebbapragada³; ¹Eastlake High School; ²Lake Washington School District; ³Farmers Branch ISD; ⁴STEM and Robotics Academy
    Educational and competetive Robotics enable hands on learning and experimentation. Despite cost effective and ease of access of controllers, drives and sensors, the structural components continue to be very expensive. Motivated by the Robotics for Everyone initiative, we are developing many easy to manufacture parts that will allow learners across the world to easily 3D print parts for (1) structural assembly of Robot Chassis (2) Sensor mounting (3) Electronic control mounting (4) power supply (5) various power drives. The ecosystem of the robotic compoenents is developed around extrusion structures and 3D printing is used for building the parts for testing and qualifying. Fixtures for moutning cameras for advanced machine learning and computer vision experiments are provided.