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

Mesoscale part machining error prediction and control method

A technology of mesoscopic scale parts and prediction methods, applied in the direction of digital control, electrical program control, etc., can solve the problems that affect the machining accuracy, the rigidity of the tool and the workpiece, etc.

Active Publication Date: 2015-01-21
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF4 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, due to the small size of the part, the rigidity of the tool and the workpiece is small, and the rigidity of the workpiece changes dynamically with the cutting, which will inevitably affect the final machining accuracy

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
  • Mesoscale part machining error prediction and control method
  • Mesoscale part machining error prediction and control method
  • Mesoscale part machining error prediction and control method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0033] The method for predicting multi-station manufacturing errors of mesoscopic parts based on the scale effect proposed in this embodiment specifically includes the following steps:

[0034] Step 1. Perform feature analysis for the mesoscale parts to be predicted, extract the machining accuracy requirements of the machining features, and determine the key part feature KPC according to the machining accuracy requirements.

[0035] Mainly carry out the storage, processing and analysis of historical data including mesoscopic cutting characteristics, equipment status information and workpiece quality information. Cutting data is obtained through basic experiments, and related data of equipment and workpieces are obtained through historical databases and sensor networks during processing. , Through this module, Key Part Characteristics (KPC) and Key Control Characteristics (KCC) can be analyzed to provide data support for error transmission modeling, quality monitoring and error sourc...

Embodiment 2

[0090] The specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings:

[0091] The material of a certain block part is 310S stainless steel, and the workpiece is processed in 2 steps: firstly, milling the A surface with the C surface as the main positioning reference; and then milling the slot 1 and the slot 2 with the A surface as the main positioning reference. Carbide end mill with a diameter of 2mm is used for milling of slot 1 and slot 2, spindle speed n=8000r / min, feed per tooth f z =0.004mm / z. See Table 1 for specific procedures and requirements.

[0092] Table 1 Workpiece processing procedure

[0093]

[0094] Analysis of the manufacturing system and process shows that the main factors that affect the accuracy of part manufacturing in process 1 are fixture manufacturing errors, reference errors, and tool deformation errors. The feature surfaces S1 and S2 processed in process 2 are affected by fixture manufac...

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

PropertyMeasurementUnit
Diameteraaaaaaaaaa
Login to View More

Abstract

The invention discloses a mesoscale part machining error prediction and control method. The method comprises the following steps that firstly, characteristic analysis is carried out on a mesoscale part to be predicted, the machining precision requirement for machining features is extracted, and a key part characteristic (KPC) is determined according to the machining precision requirement; then, tool information, machine tool information, clamping information and cutting parameter information in each procedure of the multistation machining and manufacturing process of machining the mesoscale part to be predicted are obtained; the tool deformation (see the formula in the specification) and workpiece deformation (see the formula in the specification) caused by the action of milling force in the kth procedure are calculated, and a tool deformation error (see the formula in the specification) and a workpiece deformation error (see the formula in the specification) are obtained; according to the multistation machining and manufacturing process of machining the mesoscale part to be predicted, a dynamic procedure stream error propagation state space model is built and is linearized, the error prediction result q(k) of the KPC of the kth procedure is obtained, and the machining process is controlled through the result.

Description

Technical field [0001] A method for diagnosing machining errors and controlling machining accuracy of meso-scale parts belongs to the field of precision manufacturing, and specifically relates to a method for modeling and predicting meso-scale parts multi-station manufacturing errors based on scale effects. Background technique [0002] Mesoscopic parts have small sizes, many geometric features, and high precision requirements. They need to go through multiple processes to complete the processing. Therefore, there are many sources of error in the processing process and the processing accuracy is difficult to guarantee. At present, modeling diagnosis and control methods for error transmission in multi-process processing include state-space modeling, Group EWMA, dynamic process quality control mode and e-quality control mode, etc., but they are all modeling at the macro-scale. There are few methods for predicting and controlling machining errors at the mesoscopic scale. [0003] In ...

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
IPC IPC(8): G05B19/18
CPCG05B19/18
Inventor 焦黎王西彬余璐云谭方浩高守峰刘志兵梁志强解丽静
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com