Experimental Data and Operator Knowledge used for Classifying Welding Quality

M. Kristiansen and O. Madsen (Denmark)


Knowledge acquisition, automation, artificial neuralnetworks, Bayesian network, welding, processplanningmodels.


Automation of many manufacturing processes requires a process-planning model. These models are hard and time consuming to construct because they are often non-linear and the physics of the process is not completely understood. In the literature these models are usually constructed from experiments. However, the operator possesses a lot of knowledge about the process, but it is rarely used because it is hard to formalize. This paper presents the results of a feasibility study in the use of the operator knowledge for building a process-planning model. A method is presented in which operator knowledge in the construction of process-planning models is used. The method consists of three steps: 1) Generating knowledge from operator interviews, 2) Construction of training data, 3) Construct process-planning models. Use of operator knowledge compared with use of experiments shows that the same precision of the process-planning modes is achieved but the time consumption is reduced considerably.

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