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MBD Based Automotive Products Process Planning Technology

2011· article· en· W2018177079 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Mechanics and Materials · 2011
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsNorth York General Hospital
Fundersnot available
KeywordsProcess (computing)Automotive industryExpression (computer science)ConnotationProduct (mathematics)Computer scienceProcess managementManufacturing engineeringInformation modelEngineeringSoftware engineeringMathematics

Abstract

fetched live from OpenAlex

Process planning is the important link in connection of automotive product development process design and production, and the expression and transfer of process planning information is a key affecting the development process. For promoting the process planning viability, this paper puts forward the idea of MBD based automotive products process planning information expression and its transfer method. The connotation and essence of MBD which completely expresses product definition with the integrated three-dimensional entity model and use the label and attributes solution to express the products non-geometrical manufacturing information in three-dimensional tagging, is expounded. Based on the analysis of process planning process, the concept of MBD process model and the joint expression way with mark and attributes to solve the expression of process planning information in the three-dimensional entity model was put forward. At the same time, the MBD based process planning process was expounded, as well as the expression method of all kinds of process planning information in MBD process mode.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.197
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it