Variant Design and Data Mining

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Description


In the distributed and horizontally integrated manufacturing environment in Agile Manufacturing (AM), there is a great demand for new product development methods that are capable of generating new customized assembly designs based on mature component designs that might be dispersed at geographically distributed partner sites.

Assembly Modeling: Fundamental research in models that encapsulate pertinent assembly information to support a number of downstream activities (assembly sequence generation and evaluation, tolerance chain analysis, etc.) in addition to the variant design methodology. More specifically, the complementary assembly modeling concept is proposed with two kinds of assembly models, the Hierarchical Assembly Model (HAM) and the Relational Assembly Model (RAM). The HAM explicitly captures the hierarchical and functional relationships between constituent components while the RAM explicitly captures the mating relationships at the manufacturing feature level. While all concepts here are not new, the concept of open modeling and detailed mating relationships captured at the manufacturing feature level advance the current state of assembly modeling.
Assembly variant design methodology is based on these assembly models mentioned above. The matching components are searched and retrieved based on the Assembly Variants Model (an instance of HAM) and then the Constraint Groups (CG) are identified by manipulating the Assembly Mating Graph (an instance of RAM). Graph theoretic tools/algorithms are invoked for automatic CG extraction. Then the assembly variant design process encodes the CG information along with redesign cost/time data to formulate a Mixed Integer Linear Programming (MILP) problem which is solved using a standard solver (or heuristic). This methodology provides a systematic approach to facilitate the variant design of complex assembly products in the agile manufacturing environment. Finally, a prototype system is developed and examples are presented. While variant design of piece parts has been prevalent, to our belief, this work is the first systematic optimization-based methodology for mechanical assemblies.
Data-mining methods in variant design: Our research proposes using data mining to incorporate heterogeneous and distributed information into the beginning stages of design – thereby reducing iterations and lowering the cost of product design.

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