Sculptured parts, characterized by interconnected and bounded parametric surface
patches, are widely used in aerospace, automobile, shipbuilding and plastic mold
industries due to their functional and aesthetic properties. However, adoption
of these sculptured surfaces on mechanical products increases the complexity of
manufacturing and puts forward a challenge to achieve high machining quality
and productivity, as well as low machining cost.
Machining of sculptured parts is mostly carried out on a milling machine. The
milling process can be divided into: rough cut (roughing) and fine cut (finishing)
operations. Rough machining is used to remove excess stock material, while finish
machining is aimed at generating adequate tool paths for producing the final
shape of the part. When a sculptured part is machined from prismatic stock, a
large amount of rough cut, up to 90 percent of the total machining, is required.
Cutting time reduction in rough machining can considerably improve the efficiency
of sculptured part machining, lower production cost.
This research focuses on the productivity improvement of sculptured part
rough milling machining that is affected essentially by CNC tool path and machining
parameters. Two major strategies, machining path strategy and machining
parameter strategy are investigated. A number of new methods are introduced to
generate highly productive CNC tool path and machining parameters.
Study on machining path strategy involves approaches of generating 2½D CNC
tool path trajectory, creating new tool path patterns, and automatically identifying
optimal tool path pattern. While research on machining parameter strategy focuses
on the minimization of cutting time, based upon the changing part geometry
during machining and manufacturing constraints. A method that incorporates an
existing milling process model into the cutting parameter optimization to predict
instantaneous cutting force and identify the most effective cutting parameters is
introduced. An improved model cofficient determination scheme using numerical
optimization and artificial neural network techniques is developed, and extensive
cutting tests are carried to allow the milling process model to fit into the cutting
parameter optimization. A method for the automated formulation and solution
of the cutting time minimization problem is also introduced to allow important
machining parameters, including the number of cutting layers, depth of cut, feed
rate and cross-cutting depth, to be determined without human intervention.
The research directly contributes to automated sculptured part machining, and has a great potential to produce significant economical benefits to manufacturing
industry. The study also establishes a platform for further research and
development on intelligent sculptured part machining. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/8112 |
| Date | 15 May 2017 |
| Creators | Li, Hui |
| Contributors | Vickers, G.W., Dong, Zuomin |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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