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Record W2527468616 · doi:10.1115/msec2016-8723

A System Design for Computer-Aided Manufacturing Shared, Interactive Process Setup Sheets

2016· article· en· W2527468616 on OpenAlexaff
Robert Hedrick, Jill Urbanic, Ashley Novak

Bibliographic record

VenueVolume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing · 2016
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceDocumentationProcess (computing)SoftwareKey (lock)Computer-aided manufacturingEngineering drawingComputer Aided DesignEngineeringOperating system

Abstract

fetched live from OpenAlex

Computer-aided manufacturing (CAM) software is used to develop a process plan, which consists of an operations list, tool paths, tooling, process parameters, and depending on the system, material handling operations. Upon completing the development of a process plan, setup sheets are generated for the personnel involved in the setup, production, testing, and product validation activities for a product. Typically, this documentation is in a hardcopy format, or is a static electronic document, and the direction of the communication is unidirectional — from the process planner to the support personnel. With the ubiquitous communications tools available to individuals today, a more sophisticated approach should be taken to transmit, store, and communicate changes to and from the shop floor. Presently, standard setup documentation consists of the project information utilized for the developed process plan. Pictures such as screen captures of the tool path, virtual verification images, and physical elements such as specialty tools may be included. However, modifications are made continuously to improve the cycle time, quality, or to adjust for other product or process changes. This research focuses on the development of interactive setup sheets that utilize existing desktop CAD/CAM software and mobile technologies, with the potential for leveraging the advantages of manufacturing cloud computing. Videos, links to additional documentation, and the ability to edit a subset of process parameters such as a tool diameter are incorporated. The operator is able to physically change tools or other key process setup information, and then send the information to the CAM system in order to regenerate the updated tool paths and documentation. Complementing the flexible, agile, and reconfigurable paradigms is the communication flexibility provided by fast wireless networks along with, cloud computing resources that can accessed with mobile devices, which are ubiquitous in today’s society. This technology that has not yet been heavily employed in the manufacturing environment, and research leveraging these new tools need to be explored.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0550.023

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 head0.203
Teacher spread0.189 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2016
Admission routes1
Has abstractyes

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