Open AccessMining and analyzing process similarity of product module for DPIPP based on PLM database
Author(s) -
Peng Wei,
T. Zhang,
Q. H. Zhang,
Shao-Zhou Ni
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/627/1/012004
Subject(s) - mass customization , product (mathematics) , similarity (geometry) , process (computing) , computer science , database , production (economics) , data mining , euclidean distance , function (biology) , personalization , mathematics , artificial intelligence , geometry , image (mathematics) , evolutionary biology , biology , world wide web , economics , macroeconomics , operating system
A distributed parametrized intelligent product platform (DPIPP) on basis of product lifecycle management database has been proposed to reveal the interrelationships between product attributes (i.e., structure, function or process) in recent years. Product modules in DPIPP are classified according to composition and structure of products. Components belonging to different product modules may have an identical or similar process route and could be manufacture with the same type of equipment. Mining and analysing the process similarity of product modules can effectively reduce production cost and improve production efficiency. In this paper, a novel product data mining approach with an improved Euclidean distance formula is proposed to research the product module process similarity, which aims to illustrate the effect of the structural change on the product process, then provide theoretical support for improving production patterns and reducing production costs to adapt to mass customization. The feasibility and effectiveness of the method are verified via the analysing of the process similarities among three different types of valves.