Dimensional Metrology of Non-rigid Parts Without Specialized Inspection Fixtures =
Bibliographic record
Abstract
Quality control is an important factor for manufacturing companies looking to prosper in an era of globalization, market pressures and technological advances. Functionality and product quality cannot be guaranteed without this important aspect. Manufactured parts have deviations from their nominal (CAD) shape caused by the manufacturing process. Thus, geometric inspection is a very important element in the quality control of mechanical parts. We will focus here on the geometric inspection of non-rigid (flexible) parts which are widely used in the aeronautic and automotive industries. Non-rigid parts can have different forms in a free-state condition compared with their nominal models due to residual stress and gravity loads. To solve this problem, dedicated inspection fixtures are generally used in industry to compensate for the displacement of such parts for simulating the use state in order to perform geometric inspections. These fixtures and the installation and inspection processes are expensive and time-consuming. Our aim in this thesis is therefore to develop an inspection method which eliminates the need for specialized fixtures. This is done by acquiring a point cloud from the part in a free-state condition using a contactless measuring device such as optical scanning and comparing it with the CAD model for the deviation identification. Using a non-rigid registration method and finite element analysis, we numerically inspect the profile of a non-rigid part. To do so, a simulated displacement is performed using an improved definition of displacement boundary conditions for simulating unfixed parts. In addition, we propose a numerical method for dimensional metrology of non-rigid parts in a free-state condition based on the arc length measurement by calculating the geodesic distance using the Fast Marching Method (FMM). In this thesis, we apply our developed methods on industrial non-rigid parts with free-form surfaces simulated with different types of displacement, defect, and measurement noise in order to evaluate the metrological performance of the developed methods.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".