Concurrent Process Tolerancing Based on Manufacturing Cost And Quality Loss
Bibliographic record
Abstract
sembly requirements are satisfied. Their method integrates product tolerance design and process tolerance design. The objective is to maximize the summation of weighted process tolerances. Huang and Gao (2002) presented a discrete hierarchy optimal approach for allocating the optimum component tolerance based on estimated process capability. They minimize the total manufacturing cost by using a cost-tolerance function. In process tolerance design, manufacturing engineers develop component process planning to determine manufacturing methods, machine tools, fixtures, cutting tools, cutting conditions, manufacturing routines, and process tolerances. At this stage, BP tolerances are the most important factors. If they are too tight and cannot guarantee the economic fabrication for components by using selected process planning, more precise machine tools, special fixtures, and expensive measurements should be introduced This inevitably increases the manufacturing cost of the product. The manufacturing engineers may ask for revision of BP tolerances or of the process plan. In process tolerance design, the most popular methods are also the optimal design for minimum manufacturing cost or maximum process tolerances. Huang et al. (2002) presented an optimal planar tolerance design approach to allocate dimensional and orientation geometric tolerances. A special relevance graph (SRG) was used to represent the relationships between manufactured elements and their size and tolerance information. In addition, the SRG is also applied for the geometric dimensions and tolerances. A linear programming model was established to solve the problem. A linear programming model to determine process dimensions and process tolerances was used in Similar methods to determine optimum process tolerances were proposed by Though the above methods have been used successfully to distribute both component design tolerances and process tolerances in two different phases, they over-emphasize manufacturing factors and seldom consider quality aspects. Systematically, product satisfaction conflicts with manufacturing cost. In other words, a better product satisfaction requires smaller tolerances and a higher manufacturing cost. Taguchi quality loss is a useful monetary specification to evaluate the quality factors Therefore the best policy is to consolidate manufacturing cost and quality loss in the same optimization objective to best balance quality satisfaction Manufacturing the Future: Concepts, Technologies & Visions
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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 | 0.000 |
| 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".