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Parallel microassembly with a robotic manipulation system

2010· article· en· W2029055406 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 Micromechanics and Microengineering · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGrippersFlexibility (engineering)Parallel manipulatorSMT placement equipmentSerial manipulatorProcess (computing)RobotComputer scienceRobot end effectorSet (abstract data type)Mechanical engineeringEngineeringArtificial intelligenceComputer hardwareEngineering drawingControl engineering

Abstract

fetched live from OpenAlex

This paper proposes a methodology to assemble multiple micro-components simultaneously with a robotic manipulator using a parallel assembly method. Through manipulating and assembling the micro-components, intricate, out-of-plane, three-dimensional micro-devices can now be fabricated. Use of a parallel microassembly process rather that a serial approach can significantly increase the productivity and reduce the cost of assembling micro-devices. The parallel microassembly operation proposed in this work was developed and implemented on a 6-DOF robot manipulator to attain considerable manufacturing flexibility. In this study, three passive microgrippers were bonded in parallel to the end-effector of the manipulator. Three microparts were then grasped by the grippers from the worktable of the manipulator, rotated 90°, and assembled onto the base substrate simultaneously. During the parallel microassembly operation, the visual image may not be able to monitor all three gripper-part pairs simultaneously due to the limited field of view of the microscope. Through the use of an alignment-calibration algorithm with only one gripper-part set, the remaining two sets were successfully manipulated and inserted onto the desired assembly location.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.702

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 head0.005
GPT teacher head0.182
Teacher spread0.178 · 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