Post-process updating of model parameters to approximate thermal errors of machine tools operating in different configurations

Post-process updating of model parameters to approximate thermal errors of machine tools operating in different configurations

Divíšek, D.; Mareš, M.; Horejš, O. Precision Engineering. 2024, 88 241-250. ISSN 0141-6359.

Machine tool (MT) thermal errors induced by external and internal heat sources (e.g. changing environment, friction and power losses in MT components) are an important element in machined workpiece inaccuracies. In the past few decades, indirect software compensation techniques have been used to address thermal errors on account of their economic and ecological aspects. Many slightly different approaches are described in the literature. If thermal error models are properly identified based on experiments with sufficiently varying input parameters, they usually work within similar calibration and verification conditions. As the sensory equipment of machines increases, models can be adapted to regions with higher thermo-mechanical system nonlinearity and inhomogeneity through the introduction of deformation feedback from direct measurements into model structures. However, adaptive functionalities require discrete interruptions of the MT’s work cycle, which threaten the integrity of the machined surface and complicate the model structure and thus also implementation and industrial deployment possibilities. In this research, a novel approach of automatic post-process adaptation suitable for repetitive manufacturing, which takes advantage of transfer functions (TF), is proposed for thermal error reduction. The method respects basic heat transfer mechanisms in the MT structure by combining linear parametric models, requires a minimum of additional gauges, and feedback from direct measurements is obtained only during technological pauses in the production process. Post-process adaptation is solved retrospectively by updating model parameters using the heuristic method of genetic algorithms (GA). First, the approach was tuned on a case study of a heavy-duty milling machine with a horizontal headstock for a configuration with an exchangeable spindle head and a configuration with a change in the position of the headstock in the workspace, both of which were different from the initial model’s calibration range. Subsequently, the developed approach was applied to repeated production on a medium-sized milling centre. Another goal of the paper is to emphasize the need for quality input information for modelling efforts and the industrial applicability of scientific results.

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