Integrated Product / System Design and Planning for New Product Family in a Changeable Learning Factory
Bibliographic record
Abstract
The large product variety driven by customers’ preferences and fluctuation in number of product variants produced annually impose manufacturing challenges. Changeable, reconfigurable, adaptable and smart manufacturing (Industry 4.0) paradigms aim at dealing with these challenges. The implementation of such paradigms presents many challenges to industry. Learning factories can be used as a research test bed and in educating engineering students and practitioners with the required knowledge and continuing professional development. This paper demonstrates the steps involved in introducing a new product family to an existing changeable learning factory characterized by changeability enablers including mobility, modularity, scalability and convertibility. A new product family of belt tensioners is introduced as a new product for the assembly learning factory, the iFactory, in the Intelligent Manufacturing Systems (IMS) Center, which initially assembled a family of desk sets with 265 variants. All required steps starting with the rationale of selecting the new product family, process planning, redesign of fixtures, pallets, and system re-configuration are discussed. The ability of the modular learning factory to change and adapt to the new product family, the involved experiential learning objectives and benefits and research projects along with the experience with the transition to new products family are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".