Identification of Flexible Joint Multibody Dynamic Models for Machine Tool Feed Drive Assemblies Via Inertial Measurement Unit and Computer Numerical Control Data
Bibliographic record
Abstract
Abstract High-accuracy modeling of machine tool dynamics is essential for advanced process planning and monitoring. However, modeling high-speed multi-axis machines is challenging due to the inherent coupled and nonlinear multibody dynamics and structural flexibility. This complex modeling task is addressed by a new approach in which the control dynamics and the open-loop plant dynamics are characterized by a multiple-input and multiple-output (MIMO) linear time-invariant (LTI) system coupled with a generalized disturbance, which is able to capture the open-loop coupled nonlinear dynamics. As a case study, different machine tool topologies of a flexible linear drive coupled with a rotary drive are systematically analyzed using the proposed modeling approach. The identification procedure for the proposed method requires capturing the internal structural vibration between the drives. This article also presents a method to reconstruct the internal structural vibration using data from the embedded encoders as well as a low-cost microelectromechanical systems (MEMS) inertial measurement unit (IMU) mounted on the machine table. This modeling-building approach is nonintrusive and practical for industrial implementation. The experimental validation shows a 2–6% error in predicting the tracking error and motor force/torque. Especially, the vibratory inter-axis coupling effect and posture-dependency are accurately predicted.
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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 | 0.001 | 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 | 0.001 |
| 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".