Electric wire extrusion molding process parameter optimization method for vehicles based on chaotic wavelet neural network

Abstract

The invention discloses an electric wire extrusion molding process parameter optimization method for vehicles based on a chaotic wavelet neural network, and relates to the field of production process condition optimization of electric wires for vehicles. The method comprises the following steps: firstly establishing a wavelet neural network prediction model which takes the extrusion flow rate of plastics, the main traction speed of a system and the cooling temperature of an extruding machine as input quantities, optimizing an initial parameter of the prediction model according to a chaos theory, and also introducing a chaos mechanism into a model algorithm to ensure that a learning process of network parameters has characteristics of chaotic dynamics; then learning and training the model to obtain a stable prediction model; and by taking the stable prediction model as the core, taking wire diameters and surface smoothness expected by the system as expected output values, and taking the acquisition of optimal process parameters as a target, performing optimization search by using the chaos algorithm for two times to finally find a group of optimal process parameters. By adopting the electric wire extrusion molding process parameter optimization method disclosed by the invention, wire diameter values and surface smoothness values of produced electric wires can be predicted, and key factors which influence the quality of the produced electric wires can be reasonably controlled according to a predicted value so as to ensure that the production efficiency can be effectively improved.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to View More

AI Technical Summary

Benefits of technology

In this new approach for predicting an optimum processing parameter in a specific type of metal wires called plastisol or high smoke density materials (HFSM), it uses advanced mathematical techniques such as Chaozer's equation with random variables to optimize certain features like the current velocity at different points along the path through the material being processed. These models can be applied to various industrial processes where they help improve productivity while reducing environmental pollution caused by harmful gases released during production.

Problems solved by technology

This patents discusses different methods or techniques involved with producing low smoke density flame retardant steel cables for cars. These include extruded copper tubings made from conventional lead alloys containing bismuth oxide, zinc sulfate, tin nitride, silicon dioxynitrides, tantalum disiliconium salt, etc., while ensuring proper lubrication between parts can reduce wear caused by frictions. However, it's challenging to determine optimal processing conditions such as velocity speeds and temperatures within an effective range without complicated calculations involving trial values.

Images