Thermal Error Compensation of Spindle System of Computer Numerically Controlled Machine Tools Through Experiments and Modeling
Bibliographic record
Abstract
This paper attempts to solve the insufficient machining precision of computer numerically controlled (CNC) machine tools, which is induced by the thermal error of the spindle. Firstly, the relationship between machining error and thermal sensitive points was analyzed through experiments. On this basis, the backpropagation neural network (BPNN) was improved by particle swarm optimization (PSO). Next, the improved network (PSO-BPNN) was used to build a thermal error compensation (TCE) model for the spindle of machine tools. Taking VM-500T precision machine tool as the object, the temperature data were grouped through the optimization based on thermal imaging, grey relational analysis (GRA), and fuzzy clustering, to determine the temperature sensitive items that causes the thermal error. To speed up network convergence, the PSO algorithm was introduced to optimize the number of hidden layers and the number of hidden layer nodes of the BPNN, lifting the network from the local optimum trap. To enhance the generalization ability, the weights and thresholds of the BPNN were also improved by the PSO. After that, two TCE models were established for the spindle of the machine tool, respectively based on the original BPNN and PSO-BPNN. Contrastive experiments show that the PSO-BPNN TCE model achieved the better generalization ability, and improved the prediction accuracy of the machining error of the CNC machine tool.
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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".