Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

SAL 3D printing time estimation method and system based on learning algorithm

A learning algorithm and time technology, applied in the field of 3D printing, can solve the problems affecting printing efficiency, printing deviation, printing time deviation, etc., and achieve the effect of improving the estimation accuracy

Active Publication Date: 2021-02-19
武汉奇造科技有限公司
View PDF12 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the prior art, the printing time is usually calculated during printing. The calculation of the printing time is usually to first determine the scanning path according to the printing file, and then calculate the printing time according to the preset printing parameters. However, in the actual printing process, due to the The delay will be affected by factors such as jumping and scanning distance, time lag between electrical signals and actuators, etc. The printing time will often deviate, such as the printing deviation of each action, and then the printing time error of each layer will be superimposed , eventually the printing time of all layers will form a large deviation, which will seriously affect the printing efficiency

Method used

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
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • SAL 3D printing time estimation method and system based on learning algorithm
  • SAL 3D printing time estimation method and system based on learning algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0055] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0056] Such as figure 1 As shown, a SAL 3D printing time estimation method based on learning algorithm includes the following steps:

[0057] Step 1: Import a graphic file, and extract the outline data information of the object to be printed according to the graphic file;

[0058] Step 2: Calculate the execution time of each action according to the profile data information and the preset printing parameter information, and obtain the actual time of each action;

[0059] Step 3: Calculate the time deviation coefficient of each action according to the execution time and the actual time, and determine the estimated printing time of each layer according to the time deviation coefficient of each action;

[0060] Step 4...

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

PUM

No PUM Login to View More

Abstract

The invention relates to an SAL 3D printing time estimation method and system based on a learning algorithm. The method comprises the steps that a graphic file is imported, and contour data information of a to-be-printed object is extracted; the execution time of each action is calculated according to the contour data information and preset printing parameter information, and the actual time of each action is acquired; the time deviation coefficient of each action is calculated according to the execution time and the actual time, and the estimated printing time of each layer is estimated according to the time deviation coefficient of each action; and a total estimated time is calculated. The time deviation coefficient conforming to the machine characteristics and the working environment isdetermined in a learning mode, and the execution time of each action is corrected, so that the printing estimation time of each layer is obtained, finally, the accurate total estimation time is obtained, learning iteration is continuously performed along with use of the machine, the accurate estimation effect is achieved. Estimation precision of the printing time is greatly improved.

Description

technical field [0001] The present invention relates to the technical field of 3D printing, in particular to a learning algorithm-based SAL 3D printing time estimation method and system. Background technique [0002] With the development of science and technology, 3D printing technology is becoming more and more popular and widely used in many technical fields. 3D printing technology is a rapid prototyping technology. It is based on digital three-dimensional model files, and it is a technology that forms objects such as metal, plastic, and photosensitive resin through layer-by-layer printing. It belongs to additive manufacturing. Stereolithography (SLA) 3D printer is based on the principle that photosensitive resin is solidified by ultraviolet light. The computer controls the laser to scan and solidify the liquid photosensitive resin layer by layer. Obtain the physical prototype of photosensitive resin. Light-curing rapid prototyping should have the highest precision and t...

Claims

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

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F17/11G06F113/10
CPCG06F17/11G06F30/20G06F2113/10
Inventor 王康胡汉伟赵祖烨
Owner 武汉奇造科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products