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Record W4382878107 · doi:10.23977/acss.2023.070502

The Specific Selection and Application of Metal Materials in Mechanical Engineering Design

2023· article· en· W4382878107 on OpenAlexvenueno aff
Ruijie Zhu

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMechanical designEngineering design processMaterial selectionProcess (computing)Quality (philosophy)Selection (genetic algorithm)Sustainable developmentUrbanizationDesign elements and principlesManufacturing engineeringConstruction engineeringEngineeringMechanical engineeringComputer scienceMaterials scienceSystems engineering

Abstract

fetched live from OpenAlex

At present, with the acceleration of the urbanization process, the mechanical design industry plays an important role in it gradually appears, which greatly promotes the national economic development, making the urban development more rapid and prosperous. However, in the process of mechanical design, the selection of metal materials is very critical. Because the quality of metal materials has a huge impact on the level of mechanical design, based on the realistic situation of mechanical design, choosing the most suitable and high-quality metal materials will promote the development of China's mechanical design industry. This paper focuses on the analysis and discussion of the selection of technical materials and specific use methods in mechanical design, to promote the efficiency of mechanical engineering and better implement the concept of sustainable development.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.023
GPT teacher head0.247
Teacher spread0.224 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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