On-machine error compensation for right first time manufacture

H. M. Eldessouky, J. M. Flynn, S. T. Newman

Research output: Contribution to journalConference article

Abstract

Today, high levels of precision and accuracy are needed in manufacturing to meet the increased complexities in product designs. Most products consist of multiple assembled parts, and fitting these parts together can present a major challenge, especially for complex products. Batch production systems are severely affected by scrap especially if the raw material cost is high. Producing parts right first time is a major factor for industry with manufacturing at high precision a critical requirement. This research introduces a method for compensating the machining errors using in-process measurement with the aim to machine parts right first time providing advantages over traditional methods. The method thus improves the positional accuracy of machined features while maintaining the relationships between them, compared to traditional machining. A computational model has been developed, where an algorithm within this model can handle different types of feature relationships and is able to update feature positions based on on-machine measurements. In order to validate the system, different experimental scenarios have been designed, tested with verified results. Based on these results and analysis, the proposed system showed that it can improve the error compensation on machining features by up to 77% for feature positioning, and up to 71% for feature relationships compared to traditional machining methods.

Original languageEnglish
Pages (from-to)1362-1371
Number of pages10
JournalProcedia Manufacturing
Volume38
DOIs
Publication statusPublished - 31 Dec 2019
Event29th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2019 - Limerick, Ireland
Duration: 24 Jun 201928 Jun 2019

Keywords

  • CNC machining
  • Inspection
  • Machining Features
  • Zero defect manufacture

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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