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Implementation Of Neural Network In Process Problem - Illustrations


To demonstrate neural network approach, a training set of example process plans was generated for spur gear with five features: a hole, a keyway, and two faces or identical in tolerances and dimensions, and the gear teeth. Each feature was associated with a set of attributes, defined in given table. For each illustration, the value for the attributes was randomly assigned inside common manufacturing ranges for this feature, subject to physical constraints.

                                                                  Table of: Example Features and Associated Attributes






Depth, diameter, size tolerance, position tolerance, circularity, straightness, surface finish



Diameter, thickness, size tolerance, parallelism, surface finish



Width, depth, inset, size tolerance, position tolerance, surface finish


Diameter pitch, error in action, surface finish

generated artificially by using rules. These rules after that constituted the domain transformation functions to be learned by the network. Above diagram shows some rules utilization to generate the operation sequences for hole features. Notice that these rules are non-trivial and, actually, need evaluation of relationships in between attributes. Following Figure shows some demonstrates process plans generated.

Table defines the machining operations considered to a network. A number of these operations, as like milling and honing, are utilized in the manufacture of more than one feature types.


If (depth/diameter ratio) >=3 then

If (diameter > 2) then "center drill" "trepan"



end if


 if (((diameter, 0.75) and

((size tolerance <=0.003) or

(position tolerance <=0.005))))

 else end if


"central drill" "twist drill"

"twist drill"

 if (straight <=0.001)


 end if


 if (circularity <=0.001)


end if

 "counter - bore"

 if (surface finish <=16)


 end if


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