Parametric process planning based on feature parameters of parts

Chen, Yongfu; Huang, Zhengdong; Chen, Liping; Wang, Qifu

doi:10.1007/s00170-004-2428-5

Cited by 20 publications

(8 citation statements)

References 12 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method identifies the machining features and datum, and optimizes setup groups based on the manufacturing resource capability and tolerance analysis. Chen [31] presented a parametric process plan that is dependent on feature parameters. Features are applied for optimal process plan template selection.…”

Section: B Feature-based Machining Decision Makingmentioning

confidence: 99%

Combining Dynamic Machining Feature With Function Blocks for Adaptive Machining

Liu

Wang

2016

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

Feature-based technologies are widely researched for manufacturing automation. However, in current feature models, features once defined remain constant throughout the whole manufacturing lifecycle. This static feature model is inflexible to support adaptive machining when facing frequent changes to manufacturing resources. This paper presents a new machining feature concept that facilitates responsive changes to the dynamics of machining features in 2.5/3D machining. Basic geometry information for feature construction of complex parts with various intersecting features is represented as a set of meta machining features (MMF). Optimum feature definition is generated adaptively by choosing optimum merging strategies of MMFs according to the capabilities of the selected machine tool, cutter, and cutting parameters. A composite function block for dynamic machining feature modelling is designed with Basic Machining Feature Function Block, Meta Machining Feature Extraction Function Block and Feature Interpreter Function Block. Once changes of the selected machining resources occur, they are informed as input events and machining features are then updated automatically and adaptively based on the event-driven model of function blocks. An example is provided to demonstrate the feasibility and benefits of the developed methodology.Note to Practitioners-In this paper, an important problem in most feature-based machining systems is addressed: when the selected machine tool, cutters or cutting parameters are changed due to reasons like product changeover, urgent job insertion, and broken tools, the adjustment or optimization to the machining plan is carried out based on the existing feature model. Influences of the machining resources changes to the feature model are ignored. Thus, it is impossible to get the real optimal machining result. The proposed concept of dynamic machining feature integrates the feature definition of a part to the capabilities of the selected machining resources. By applying the event-driven model of function blocks, features can be generated adaptively and automatically during the whole dynamic manufacturing lifecycle.Index Terms-Adaptive machining, dynamic feature, function block.

show abstract

Section: B Feature-based Machining Decision Makingmentioning

confidence: 99%

Combining Dynamic Machining Feature With Function Blocks for Adaptive Machining

Liu

Wang

2016

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…One method that is within the scope of the state of the art for automated process planning today is a blend of variant and generative modeling (Swift 1987), based on product classes and templates. In Chen (2006) they develop a parametric process planning approach which shares similarities with both variant and generative approaches. It is similar to variant in that parts are classed into similar topologies that can be parameterized to be fully understood.…”

Section: Parametric Process Planningmentioning

confidence: 99%

“…Automotive connecting rod, fully parameterized with 20 features and 100 parameters(Chen, Huang et al 2006) …”

mentioning

confidence: 99%

Configuring and Exploring the Foundry Trade Space

Yukish¹

2012

View full text Add to dashboard Cite

Using Government drawings, specifications, or other data included in this document for any purpose other than Government procurement does not in any way obligate the U.S. Government. The fact that the Government formulated or supplied the drawings, specifications, or other data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented invention that may relate to them. This report was cleared for public release by the USAF 88 th Air Base Wing (88 ABW) Public Affairs Office (PAO) and is available to the general public, including foreign nationals. Copies may be obtained from the Defense Technical Information Center (DTIC) (http://www.dtic.mil).

show abstract

“…Tremendous effort has been put into developing an automated process planner as documented in a number of past studies. Those works evolved around feature recognition [12][13][14][15][16][17][18], knowledge representation [19][20][21][22][23][24][25] and inference engine [26,27], and integration of process planning, and upstream or downstream processes [28,29]. Some researchers applied different methods/technologies such as OPPS-PRI 2.0 system [30], genetic algorithms (GA) [31][32][33][34][35][36], imperialist competitive algorithm [37], energy-efficient oriented method [38], neural network-based system [39][40][41], fuzzy set theory/fuzzy logic method [39,42,43], agent-based methodology [44,45], Internet-based technology [46,47], functional blocks [48,49], Petri net model [50] and STEP-compliant method [51][52][53][54], just to name a few, f...…”

Section: Introductionmentioning

confidence: 99%