Establishment And Application Of Prediction Models On The Diameter And Energy Consumption Of 3D Printer Filaments Production Line

This article relies on the intelligent upgrade project of the 3D printer filaments production extrusion line,based on the Internet of Things technology and artificial intelligence technology,from the perspective of the collection method of the core parameters of the equipment operating state,the accurate and rapid determination of the process conditions,and the accurate analysis of the equipment energy consumption Research work to digitize,network and intelligently upgrade the existing 3D printer filaments production line in the laboratory.The main contents of the study are as follows:(1)Aiming at the problem of single function of the diameter measurement device of the laboratory 3D printer filaments production line,the method of combining the 485 communication interface of the single-axis laser caliper display instrument and the Arduino serial communication technology was designed to design the consumable wire diameter monitoring system.The system not only realizes real-time collection,monitoring and recording of wire diameter data in the Matlab environment on the PC side,but also transmits wire diameter data to the OneNET Internet of Things platform by adding a wireless communication module to realize remote monitoring of wire diameter data.(2)Using the experimental method,the relationship between the process factors of the small single screw extruder consumables production line and the 3D printer filaments wire diameter was analyzed,and a prediction model based on GA-BP neural network was established.The sizing die head temperature(three stages),barrel temperature,screw speed,and tractor speed of the production line are used as input variables,and the wire diameter of consumables is used as the output variable.The results show that the network prediction model can obtain more accurate results.In addition,based on the established network model,a software with consumable wire diameter prediction function and production process prediction function is designed.This software has certain guiding significance for efficient and reasonable arrangement and adjustment of consumable production process.(3)Based on the linear diameter prediction model,comprehensively considering the influence of technological factors and environmental factors on energy consumption,an energy consumption prediction model based on RBF neural network was established.Among them,the input node of the network is the sizing die temperature(three sections),barrel temperature,screw speed,and ambient temperature,and the output node is the energy consumption of the production line.The results show that the network model has high prediction accuracy.And based on this network model,energy consumption prediction software has been developed,which provides software tool support for improving the energy saving potential of consumable production lines.