Uncertainty is inevitable in problem solving and decision making. One way to reduce it, is by seeking the advice of an expert in related field. On the other hand, when computers are used to reduce uncertainty, the computer itself can become an expert in a specific field through a variety of methods. One such method is machine learning, which involves using a computer algorithm to capture hidden knowledge from data. The researchers conducted the prediction of CO2 laser cut quality to obtain singleton output using machine learning techniques. The researchers investigated a problem solving scenario for a metal cutting industry which faces some problems in determining the end quality final part considering several real life machining scenarios with some expert knowledge input from the industry and machine technology features. This large search space poses a challenge for both human experts and machine learning algorithms in achieving the objectives of the industry to reduce the cost of manufacturing by enabling the off hand prediction for laser cut quality to increase the production quality and rate while significantly reduce the production cost.

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