Integrated design and manufacturing in mechanical engineering : proceedings of the Third IDMME conference held in Montreal, Canada, May 2000
Bibliographic record
Abstract
Preface. Acknowledgements. List of contributors. 1: Design Theory: Methodology. Confrontation of viewpoints in a concurrent engineering process G. Martin, et al. A concurrent engineering experience based on a cooperative and object oriented design methodology S. Gomes, J.C. Sagot. A method and a support for a better integration of mechanical simulation in the design process F. Pourroy, et al. Representation of design activities using neural networks: application to fuzzy dimensioning F. Bennis, et al. Quantitative constraints in integrated design X. Fischer, et al. Handling imprecision in pairwise comparison F. Limayem, B. Yannou. 2: Design Theory: Models. A declarative approach to a 2D variational modeler D. Lesage, et al. Copying of free-forms from digitized data A. Contri, et al. Flexible parts modelling for virtual reality assembly simulations J.C. Leon, U. Gandiaga. A rectification algorithm for manifold Boundary representation models R.I.M. Young, et al. A methodology for modeling process information R. Bacha, B. Yannou. 3: Control, Measurement and Tolerancing. Tight fit design taking into account form and surface defects J.F. Fontaine, et al. Calculation of virtual and resultant part for variational assembly analysis L. Pino, et al. 3D Quantification of machining defects F. Villeneuve, et al. Formal definition of tolerancing in CAD and metrology P. Serre, et al. Quality measurement on CMM J.M. Linares, et al. Toward the use of statistical analysis in positional tolerancing F. Bennis,et al. 4: Manufacturing and Modelling. Prediction and simulation of milling burr formation for edge-precision process planning C.H.Chu, etal. Optimal workpiece localization for machining applications J.-F. Chatelain, C. Fortin. Analysis and mapping of the dynamic performance of high-precision motion systems E.V. Bordatchev. Integration of laser material processing into computer-aided product and process development M. Geiger, A. Kach. Investigation of sheet metal blanking process M. Rachik, et al. The concept of the machining surface in 5-axis milling of free-form surfaces C. Tournier, et al. 5: Manufacturing and Process Planning. Design process modelling of process planning for flexible lines based on conceptual graphs and design rules: applied to cylinder head machining A. Lefebvre, et al. Determination of virtual means for the integrated design K. Mawussi, et al. Selecting material handling equipment with PROMETHEE P. de Lit, et al. A methodology for cost and quality optimization in a design system by linking quality methods G. Dragoli, D. Brissaud. Disassembly sequencing using technological data N. Rejneri, et al. Application of fuzzy logic for an assembly methodology A. Sinzinkayo, et al. 6: Optimal Design of Machines, Structures and Components. Design of closed planar mechanisms of grippers to clutch flat parts L. Slutski. Interaction of gear epicyclic planets and the effect of web for internal gears J.-P. de Vaujany, et al. Kinematics of robots with roller-constrained ball-wheels S. Ostrovskaya, J. Angeles. Structural synthesis of kinematic chains and mechanisms L. Notash, J. Zhang. General manipulators synthesis for a given workspace S. Guerry, et al. Object manipulation and mannequin driving based on multi-agent architecture P. Chedmail, et al. 18 additional articles.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.017 |
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".