Variation analysis for custom manufacturing processes

Discovering and addressing unknown, including unanticipated, part-to-part variation sources is an important, yet challenging problem in manufacturing variation reduction. The state-of-art methods for solving this problem have focused solely on traditional mass manufacturing settings, in which abundant measurement data of parts with the same design are available. Applying these methods to custom manufacturing processes is problematic because the number of parts with the same design in custom manufacturing is often small. This paper proposes a new variation model that considers custom manufacturing parameters to aggregate measurement data across all custom parts. We also propose to estimate this model via a conditional autoencoder. The advantages of the proposed approach are demonstrated with a simulated toy-building brick example and a real cylindrical machining example. The approach successfully reveals unknown variation patterns even with a relatively small number of parts in these examples. Our approach is also generally applicable to any mainstream manufacturing processes that produce multiple part designs.