Embedded Sensors and Feedback Loops for Iterative Improvement in Design Synthesis for Additive Manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Design problems are complex and not well-defined in the early stages of projects. To gain an insight into these problems, designers envision a space of various alternative solutions and explore various performance trade-offs, often manually. To assist designers with rapidly generating and exploring a design space, researchers introduced the concept of design synthesis methods. These methods promote innovative thinking and provide solutions that can augment a designer’s abilities to solve problems. Recent advances in technology push the boundaries of design synthesis methods in various ways: a vast number of novel solutions can be generated using high-performance computing in a timely manner, complex geometries can be fabricated using additive manufacturing, and integrated sensors can provide feedback for the next design generation using the Internet of things (IoT). Therefore, new synthesis methods should be able to provide designs that improve over time based on the feedback they receive from the use of the products. To this end, the objective of this study is to demonstrate a design synthesis approach that, based on high-level design requirements gathered from sensor data, generates numerous alternative solutions targeted for additive manufacturing. To demonstrate this method, we present a case study of design iteration on a car chassis. First, we installed various sensors on the chassis and measured forces applied during various maneuvers. Second, we used these data to define a high-level engineering problem as a collection of design requirements and constraints. Third, using an ensemble of topology and beam-based optimization techniques, we created a number of novel solutions. Finally, we selected one of the design solutions and because of some manufacturability constraints we, 3D-printed a prototype for the next generation of design at one third scale. The results show that designs generated from the proposed method were up to 28% lighter than the existing design. This paper also presents various lessons learned to help engineers and designers with a better understanding of challenges applying new technologies in this research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it