Neural network multi layer perceptron modeling of surface quality in laser machining

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 we use computers 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 laser machining quality, namely surface roughness with seven significant parameters to obtain singleton output using machine learning techniques based on Quick Back Propagation Algorithm. In this research, we investigated a problem solving scenario for a metal cutting industry which faces some problems in determining the end product quality of Manganese Molybdenum (Mn-Mo) pressure vessel plates. We considered several real life machining scenarios with some expert knowledge input and machine technology features. The input variables are the design parameters which have been selected after a critical parametric investigation of 14 process parameters available on the machine. The elimination of non-significant parameters out of 14 total parameters were carried out by single factor and interaction factor investigation through design of experiment (DOE) analysis. Total number of 128 experiments was conducted based on 2
^k
factorial design. 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 of laser cut quality and further increase the production rate and quality.