Computational Intelligence in Manufacturing Handbook (The by Jun Wang, Andrew Kusiak

By Jun Wang, Andrew Kusiak

Regardless of the big quantity of guides dedicated to neural networks, fuzzy common sense, and evolutionary programming, few deal with the purposes of computational intelligence in layout and production. Computational Intelligence in production Handbook fills this void because it covers the newest advances during this zone and state of the art purposes. This complete guide includes an outstanding stability of tutorials and new effects, that permits you to

  • receive present information
  • comprehend technical details
  • determine study potentials, and
  • outline destiny instructions of the field
  • Manufacturing purposes play a number one function in development, and this guide delivers a prepared connection with advisor you simply via those advancements.

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    Extra resources for Computational Intelligence in Manufacturing Handbook (The Mechanical Engineering Handbook Series)

    Sample text

    The outputs of the network indicate the type of defect on the seal lip aperture. A similar neural network is used to classify defects on the seal lip surface. The accuracy of defect classification in both perimeter and surface inspection is in excess of 80%. Note that this figure is not the same as that for the accuracy in detecting defective seals, that is, differentiating between good and defective seals. The latter task is also implemented using a neural network which achieves an accuracy of almost 100%.

    Binary matrix representations of parts based on geometric shapes were stored in the Hopfield network. Test cases carried out on rotational and nonrotational parts showed the high percentage of correct retrieval of stored part information using the neural network. The retrieval rapidity is another major advantage of the neural network model. Such a storage/retrieval system could benefit the design process by minimizing duplications and variety, thus increasing productivity of both designer and planner, aiding standardization, and indirectly facilitating quotations.

    Furthermore, this approach offers flexibility and could adjust to changes in products. Unfortunately, the limited capacity of the Hopfield network constrained the possible number of stored designs. Chakraborty and Roy [10] applied neural networks to part-family classification based on part geometric information. The neural system consisted of two neural networks: a Kohonen’s SOM network and a multilayer feedforward network trained using the BP algorithm. The former was used to cluster parts into families and provide data to train the latter to learn part-family relationships.

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