Challenges and solutions in laser micro fabrication of micro parts, mechanisms, and sensors
Bibliographic record
Abstract
Non-conventional micro fabrication technologies, such as laser micro machining, micro EDM, etc., are [the] most suitable and cost effective choice for prototyping of micro parts/systems, and also in some cases, for batch production. This is especially true for complex microdevices which require functional and/or composite materials other than silicon. In this presentation, NRC-IMTI's capabilities in laser micro fabrication of micro mechanisms, eg., actuators and grippers, integrated with force/displacement capacitive sensors, and in micro fabrication of 2D/3D micro moulds and dies for microfluidics applications are discussed. Systematic representation of the high-precision laser material removal process uncovers main disturbances that affect fabrication with nano/micro-scale accuracy, precision, and geometric quality. Using case studies of micro parts and mechanisms fabricated by the laser micromachining technology, several machining challenges related to non-uniformity and dynamic errors of motions, corner accuracy, asynchronization of motions and laser on/off events in space and time with respect to the part geometry, are evaluated. Also, effect and optimization of several process parameters, such as laser power, focal distance, number of machining passes, etc., are evaluated with respect to the machined surface topology. Results of these studies allowed achieving accuracy and precision of fabricated micro parts and mechanisms within +/- one micrometer or less and surface roughness of 70 nm.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".