Dynamically changing machining conditions and uncertain manufacturing resource availability are forcing manufacturing enterprises to search advanced process planning in order to increase productivity and ensure product quality. As growing quantities of the three-dimensional process models are gradually applied, reusing the embedded manufacturing information in process models with less time and lower cost attracts a lot of attention. In this paper, a new flexible method is presented to reuse the existing process information based on retrieval of the similar machining feature. First, the three-level organization model is introduced to represent the process information; the machining feature which is seen as the parent layer carries the corresponding manufacturing information. To ensure accurately that the process information are obtained, the associated mechanism between the machining feature and process information is created. Second, an eight-node representation scheme is designed to represent the similar machining feature having same variations in topology and geometry. For accelerating similar feature retrieval, the extension-attributed adjacency graph and the topological relationship of the machining feature faces are built. Finally, some aircraft structural parts are utilized in the developed prototype module to verify the effectiveness of the proposed method. This method can be used as the basis for accumulation of the process information; it can promote the development and application of the intelligent process planning.
The International Journal of Advanced Manufacturing Technology – Springer Journals
Published: Feb 22, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera