Automatic Realignment of Defective Assemblies Using an Inverse Kinematics Analogy
Bibliographic record
Abstract
Alignment and plumbness of construction assemblies is a challenging and fundamental problem because it relies on manual solutions to the underlying geometric feedback control problems inherent in practices such as pipefitting and steel structures erection. Where defective components and segments are not well controlled, the errors propagate in larger components and therefore cause more severe challenges and large costs consequently. This paper presents a framework for automatic and systematic development of realignment actions required to achieve a desired state by employing and combining two theories: (1) three-dimensional (3D) imaging that enables the identification of the as-built status and then quantification of incurred discrepancies as a feedback signal by comparing the captured as-built status with the designed state existing in the building information model (BIM), and (2) an inverse kinematics analogy that results in the calculation of required changes in the degrees of freedom defined where manipulations and changes can be applied. Experimental results show that the framework can generate the required actions for achieving a desired state systematically and with a high level of accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".