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Enregistrement W2592298314 · doi:10.18260/1-2--1053

Computer Aided Engineering Introduction In A Multi Disciplinary Engineering Program

2020· article· en· W2592298314 sur OpenAlexafffundabout
J. J. Phillips, Michele Oliver, Bill van Heyst, D. B. Joy, Warren Stiver

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueExperimental Learning in Engineering
Établissements canadiensUniversity of Guelph
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaUniversity of Guelph
Mots-clésSoftware engineeringAccreditationComputer scienceComputer-aided engineeringDisciplineDomain (mathematical analysis)Context (archaeology)SoftwareComputer Aided DesignEngineering educationEngineering managementEngineeringProgramming language

Résumé

récupéré en direct d'OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Computer-Aided-Engineering: Introduction in a Multi-disciplinary Engineering Program Introduction Computer-aided engineering (CAE) and computer-aided design (CAD) are taking an increasing role for the practicing engineer in both design and analysis context. For the practicing engineer, it provides an opportunity to explore creative ideas without the initial expense of prototypes and/or pilot facilities. The importance of CAE in engineering education is well established and many engineering programs now include some type of formal education utilizing solid modelling software packages1,2,3. Many studies have shown that the use of CAE software enhances the learning of students at all levels from first year4,5,6 to fourth year7,8. The CAD/CAE software packages themselves tend to be broad based, offer extensive tools for the experienced practitioner and hence they may not be intuitive to the novice user. CAE is not a trivial domain. It is easy to generate results that are incorrect and dangerous9. Therefore, it is essential to know what is going on within a software program and recognize the software’s limitations10. Unfortunately, it is easy for a student or the practitioner to generate impressive pictures with CAE software which display completely erroneous results. Engineering education must develop strategies to ensure that our graduates recognize the power of CAE while respecting the risks and responsibilities associated with its use. The University of Guelph offers fully accredited engineering programs in Biological, Systems and Computing, Environmental, and Water Resources. Each program at Guelph is multidisciplinary blend of traditional engineering disciplines. Design is recognized as the essence of engineering and has been delivered at the School through a core sequence of design courses since the early 1970s11. These core design courses offer all of our students the experience of working in multi-disciplinary teams. The introduction of CAD/CAE tools into the curriculum began in Fall 2000 with a single course and has evolved to be offered in three second year courses. This paper presents the current approach, which has largely been in practice for the past 2 or 3 years. The paper then discusses some of the key attributes that are believed to be important for success and assesses the level of intelligent usage by students in their fourth year capstone design projects. CAD/CAE Delivery in Second Year The participating second year courses are Engineering & Design II and Material Science (fall offerings), and Fluid Mechanics (winter offering). All of these courses are taken by all engineering students at Guelph. Solid modelling is introduced in Engineering and Design II. The basics of finite element modelling related to solid mechanics problems are introduced in Material Science. Computational fluid dynamics is introduced in Fluid Mechanics. Engineering & Design II is the core course supporting CAE. The students, in teams of approximately 10, start in the machine shop dissecting a product. Over the years, the products have ranged from refrigerators, to computers, to the components of a 1986 Honda Prelude. This

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,299
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,233
Écart entre enseignants0,220 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission3
Résumé présentoui

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