A two-phase hybrid product design algorithm using learning vector quantization, design of experiments, and adaptive neuro-fuzzy interface systems to optimize geometric form in view of customers’ opinions
Bibliographic record
Abstract
One of the most important characteristics of a modern product is the extent to which it meets the needs of customers to gain market share. The conceptual design methods of products based on customer requirements are often feature-based, in which several features are identified between different types of a product. According to customer demands, these features are tuned and the closest is selected as the optimum. The great variety of features of a present-day product can often make this difficult because finding these common features is very complicated or even impossible. To solve this problem, choosing the optimal design is divided into two phases: In the first phase, the main product is divided into some basic categories and according to the customers' opinion, one is selected as the category. In the second phase, the selection of common geometrical features between the members of the winning category is made. Then, the optimization process is done based on customer rating and the closest design to the mentioned rating is selected. The house light switch is used as a case study and the proposed algorithm is implemented on it. High customer satisfaction with the optimized final design, high response rate to survey forms, and the low number of incompatible data, all, indicate the suitability of the proposed algorithm with human interface characteristics, simplicity and efficiency in adapting the product to the customers' view. This method can be used for other industrial products and even for non-industrial products or services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".