Design and Interaction Interface using Augmented Reality for Smart Manufacturing
Bibliographic record
Abstract
In this paper, we apply Augmented Reality (AR) technologies to develop a design and interaction interface for Smart Manufacturing (SmartMFG). This work is motivated by the lack of appropriate human-machine-interaction (HMI) tools to support interaction and customization in SmartMFG environment. Trying to address this research problem, we hypothesize that AR-based design interfaces that communicate with Machine Control Unit (MCU) directly will increase the degree of interaction and the complexity of instructions performed in Manual Data Input (MDI) systems. To test this hypothesis, we developed a prototyping system consisting of an AR-tablet device as the input interface and an Ultimaker 3 printer as the machine tool. Firstly, this AR-based system has sensing, design and control capabilities to interact and communicate with the machine tool via Wifi. Secondly, a set of sketch-based computational tools is developed for users to design shapes on existing objects easily and efficiently within the AR environment. Finally, The customized design is converted to machine code, which is also customized based on the machine tool and the registration of the virtual model and the existing object. We tested our system by designing two customized shapes onto an existing shape in the AR environment and generating the G-code to control the printer to fabricate them onto the physical object.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".