Functional Requirement and User's Needs for Food Serving Robots
Bibliographic record
Abstract
The integration of food serving robots in the hospitality industry presents a significant advancement in enhancing customer service and operational efficiency. By employing Axiomatic Design (AD) principles, this research has identified and addressed key Customer Needs (CNs) and Functional Requirements (FRs), ensuring that these robots can provide a smooth, human-like interaction, quick and accurate service, and a safe dining environment. The systematic approach of AD allows for the effective decomposition of complex requirements into manageable design parameters, ultimately leading to robust and reliable robotic solutions that meet both customer expectations and business objectives. This methodology not only improves customer satisfaction but also facilitates the seamless integration of technological advancements in the food service industry, paving the way for sustainable growth and innovation. In addition, the focus on Reliability and Maintainability (R&M) ensures that the food serving robots are robust, easy to maintain, and supported throughout their lifecycle, addressing key FRs like operational efficiency, safety, user interface, and versatility. The successful deployment of food serving robots relies on a comprehensive approach that integrates AD with R&M principles, emphasizing robust design, ease of maintenance, and user-centric features. This approach ensures that the robots meet the needs of various stakeholders, providing long-term viability and customer satisfaction.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".