Get Computer-Aided Design, Engineering, and Manufacturing: PDF

By Cornelius Leondes, Cornelius T. Leondes

Within the aggressive enterprise area businesses needs to always try to create new and higher items swifter, extra successfully, and extra cheaply than their rivals to achieve and continue the aggressive virtue. Computer-aided layout (CAD), computer-aided engineering (CAE), and computer-aided production (CAM) at the moment are the common. those seven volumes supply the reader a finished remedy of the thoughts and functions of CAD, CAE, and CAM.

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Read or Download Computer-Aided Design, Engineering, and Manufacturing: Systems Techniques and Applications, Volume V, The Design of Manufacturing Systems PDF

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Additional resources for Computer-Aided Design, Engineering, and Manufacturing: Systems Techniques and Applications, Volume V, The Design of Manufacturing Systems

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Multiple Interpretation of Features Physically interacting features may result in multiple interpretation of features. A given set of interacting features can have multiple interpretations. Multiple interpretation of features is especially useful to generate alternate process plans. The process planning system reported by Chang [1990] uses heuristic techniques for refining features (either combining features or splitting features for machining). However, heuristic systems may not produce alternative feature interpretations for some cases.

Primarily, the designer will use the in-house technology knowledge base to perform manufacturability checks. If the evaluation is not satisfactory, the designer can perform a manufacturability check using an expanded knowledge-base (which should include the manufacturing capabilities of vendor companies). A vast amount of literature exists in the area of feature-based manufacturability evaluation. Readers are referred to the paper by Gupta et al. [1995] for a comprehensive review of the work on automated manufacturability analysis.

Then, the deterministic equivalent of the stochastic programming formulation is modified as follows. 49) where ␳ is a goal programming weight. By changing the weight ␳, the relative importance of the expectation and variability of the second stage costs in the objective can be controlled to construct an ‘‘efficient frontier’’ of solutions. Another approach for enforcing robustness is the restricted recourse framework of Vladimirou and Zenios [17]. This method differs from robust optimization in that, here, the variability of the second stage solution is reduced by adjusting the bounds on the second stage variables.

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