Certification and Training for Automation and Mechatronics
Bibliographic record
Abstract
Abstract This article is a Work In Progress (WIP) paper that presents the faculty professional development regarding the new course titled Industrial Robotics in Electrical and Computer Engineering Technology (ECET) curriculum at ****. The course will also serve a platform for future Mechanical Engineering Technology (MET) for Manufacturing and Mechatronics concentrations at the same institution. The need and procedure for the authors to be certified by FANUC are described. One of the authors also received week-long training from Amatrol in Programmable Logic Controllers (PLC). Another author received trainings on CNC machining. The primary goal is to have the authors ready and equipped with hands on skills for a fully automated environment where interdisciplinary courses on Industrial Robotics, PLC, Manufacturing, and Automation can be taught. The focus will be to have our undergraduate students be involved in multi-disciplinary research experience based on the developed educational materials. Since the certification from FANUC, our institution **** has been recognized officially by FANUC as one of the training sites. The FANUC Robotics certification process has three steps – onsite training, online exam, and finally, an approved lecture session. The onsite training generally involves a week-long exposure to FANUC robotic arms and program them to perform different tasks. Based on the obtained training and curriculum, then the author needed to appear for an online exam and pass it with 80%. The final step to be certified was to send a recorded lecture/lab session and be approved by the FANUC certification body. Once the instructor receives the certification, he or she can teach the materials and certify students taking the course for onsite training at ****. The Amatrol training also required the author to be onsite at the Amatrol facility and go through extensive hands-on skills for different components of PLC in Allan-Bradley logic. The CNC training includes both milling and turning certifications authorized by Siemens and Fanuc for their CNC controllers. For the Siemens controller, the authors receive a four days on-site training from a Siemens authorized instructor. The Fanuc certifications are based on 60 hours of online training (30 hours each for the milling and turning training). If accepted, the authors requests for a traditional lecture type presentation during ASEE 2020 in Montreal, QC, Canada.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".