Research On Dynamic Adjustment Scheme Of Stone Cutting Parameters Based On Cutting Force Information

Manufacturing is an important indicator to measure the country’s overall national strength and international competitiveness.The rapid development of science and technology puts new demands on the transparency and flexibility of manufacturing systems.Traditional manufacturing models and decision-making methods are difficult to adapt to modern production systems with more obvious dynamic changes.Manufacturing systems need to be able to realize active sensing and dynamic scheduling of production systems based on real-time manufacturing process information.Firstly,this paper analyzes the removal mechanism of stone,studies the influence of physical and chemical properties of stone on the processing performance of stone,and analyzes the relationship between cutting force and cutting parameters through cutting force experiment,and obtains the cutting force with the spindle speed.The experimental results show that the cutting force decreases with the increase of the spindle speed,increases with the increase of the feed speed and the cutting depth,and the influence of the cutting depth on the cutting force and the inhomogeneity of the stone will lead to the cutting force,It provides theoretical support for cutting force information to be used as the basis for dynamic adjustment of cutting parameters;secondly,the design of the cutting force data acquisition scheme,including the cutting force node data sensing scheme and the cutting force data information network transmission scheme,analyzes the existing industrial network level and each level After the function,the application of IoT technology and sensor technology in cutting force data acquisition was studied.The cutting force information acquisition system based on LoRa technology and piezoelectric cutting force sensor was proposed,and a fusion with processing equipment was established.And suitable for data transmission systems in noisy environments;The density clustering algorithm in the learning technology establishes a data analysis model,analyzes and processes the cutting force data collected by the LoRa network system in real time,and combines the intelligent techniques such as data analysis and machine learning to judge the machinability of the stone and identify the running state of the equipment.The data model was tested experimentally and the test results were visualized.Finally,the setting requirements of cutting parameters in the actual machining process were analyzed,and the dynamic scheduling method of cutting parameters was designed to automatically adjust the machine tool equipment to the optimal cutting parameter state.At present,many industries would be actively to seek ways to integrate with emerging information technology.This thesis would take the stone processing process as the research object,and studies a manufacturing scheme that can active sensing and judge the processing performance of stone in real time and use it as the decision information to dynamically schedule the process parameters,which provides a new way and method for the development and application of intelligent manufacturing.