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Record W3185363836 · doi:10.53759/7669/jmc202101005

An Assembly Approach for Determining the Maintainability index for Engineered Products

2021· article· en· W3185363836 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

VenueJournal of Machine and Computing · 2021
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaintainabilityReliability engineeringIndex (typography)Systems engineeringManufacturing engineeringComputer scienceEngineeringRisk analysis (engineering)Business

Abstract

fetched live from OpenAlex

It is challenging for maintenance of activities to be assured during products’ life cycle when poor maintenance is the case. Poor maintenance of engineered products will lead to an increment in cost and time is fundamental in the development of maintainability tasks for engineering firms. Maintenance design had played a significant role in complex designing of engineering products. This research presents a critical approach to evaluate and determine a maintainability index using the assembly principle. Normally, time is a critical indicator and parameter being utilized to measure maintenance; however, minimal efforts have been focused on assembly components and the principle of assembly. In the past literature works, customer study and survey on the effects of operators’ skills have been done. In this research, maintainability index is determined. Every assembly type is weighted with reference to features such as assembly direction, costs and disassemblability. Resultantly, this research seeks to enhance the efficiency of maintenance of engineered products.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.601
Threshold uncertainty score0.236

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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