Hybrid neural network framework for predicting tool tip dynamics via receptance coupling
Bibliographic record
Abstract
Tool tip frequency response functions (FRFs) are fundamental to predicting stability lobe diagrams and mitigating chatter in machining operations. This study introduces a hybrid framework that integrates physics-based modeling with data-driven learning to reduce approximation errors in tool holder–tool geometries and mitigate uncertainties in their contact parameters. The tools and tool holders are modeled using a Timoshenko beam-based finite element formulation and assembled as free-free structures via receptance coupling substructure analysis (RCSA). Uncertainties in the elastic modulus, Poisson’s ratio, and density of the tool and holder materials are minimized by aligning the measured and simulated natural frequencies of representative tool and holder samples. Neural network models are pre-trained using simulated FRFs with approximate contact parameters and subsequently fine-tuned through a limited number of experimental free-free impact tests on holder–tool assemblies. The optimized contact parameters are then archived in the database for each holder type. The finite element models of the tools and holders are coupled using the tuned contact parameters and subsequently assembled with the stored spindle model via RCSA. The proposed hybrid approach is experimentally validated through impact testing of diverse holder–tool configurations mounted on machine tools. The resulting methodology contributes to the establishment of a robust digital machine tool database, thereby facilitating more reliable stability predictions and enabling enhanced productivity in NC part programming within CAM systems.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".