Abstract
Introduction. It is shown that the digital twin (electronic passport) of a CNC machine is developed as a cyber-physical system. The work objective is to create neural network models to determine the operation of a CNC machine, its performance and dynamic stability under cutting.Materials and Methods. The development of mathematical models of machining processes using a sensor system and the Industrial Internet of Things is considered. Machine learning methods valid for the implementation of the above tasks are evaluated. A neural network model of dynamic stability of the cutting process is proposed, which enables to optimize the machining process at the stage of work preparation. On the basis of nonlinear dynamics approaches, the attractors of the dynamic cutting system are reconstructed, and their fractal dimensions are determined. Optimal characteristics of the equipment are selected by input parameters and debugging of the planned process based on digital twins.Research Results. Using machine learning methods allowed us to create and explore neural network models of technological systems for cutting, and the software for their implementation. The possibility of applying decision trees for the problem of diagnosing and classifying malfunctions of CNC machines is shown.Discussion and Conclusions. In real production, the technology of digital twins enables to optimize processing conditions considering the technical and dynamic state of CNC machines. This provides a highly accurate assessment of the production capacity of the enterprise under the development of the production program. In addition, equipment failures can be identified in real time on the basis of the intelligent analysis of the distributed sensor system data.
Highlights
It is shown that the digital twin of a CNC machine is developed as a cyber-physical system
The work objective is to create neural network models to determine the operation of a CNC machine, its performance and dynamic stability under cutting
Optimal characteristics of the equipment are selected by input parameters and debugging of the planned process based on digital twins
Summary
1, 2, 3, 4 Нижегородский государственный технический университет, г. Цель работы — создание нейросетевых моделей, определяющих функционирование станка с ЧПУ, его производительность и динамическую устойчивость при резании. Рассматриваются вопросы создания математических моделей процессов механической обработки с использованием системы сенсоров и промышленного интернета вещей. Предложена нейросетевая модель динамической устойчивости процесса резания, позволяющая оптимизировать процесс механической обработки на этапе технологической подготовки производства. Выбраны оптимальные характеристики оборудования по входным параметрам и отладке планируемого технологического процесса на основе цифровых двойников. Использование методов машинного обучения позволило создать и исследовать нейросетевые модели технологических систем обработки резанием и программное обеспечение для их реализации. В реальном производстве технология цифровых двойников позволяет оптимизировать режимы обработки с учетом технического и динамического состояния станков с ЧПУ. На основе интеллектуального анализа данных системы распределенных сенсоров можно выявить неисправности оборудования в режиме реального времени
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