Hobbing-Machine-Diagnosis system with artificial intelligence (Learning-data collection through hobbing simulation and their compression with network representation)

Abstract

In a hobbing process, many factors bring pitch, profile, and helix deviations to hobbed gears, and it is generally difficult to identify which factors cause the deviations. Therefore, a system that allows causal factors to be determined is required especially in the after-sales service of the hobbing machine manufactures. In the present study, a hobbing-machine-diagnosis system is being developed. Artificial intelligence will be employed in the system to easily determine the cause of deviations that occur in hobbed gears. The development of the system requires a lot of learning data for artificial intelligence. The data should be obtained in various conditions. However, collecting data from real cutting is almost impossible because of cost and time, so that hobbing simulation was carried out. The hobbing simulation has already been developed by many researchers. But those simulations are not enough for manufacturers of hobbing machines to evaluate problems in the machines. In addition, the conventional tooth profile/helix deviations diagrams are not appropriate for images used in artificial-intelligence systems, because they include redundant data. In the present paper, therefore, a network graph representing the correlation between every two teeth were discussed to examine whether the graph can be used as data for artificial-intelligence systems. As a result, an image of the network graph could be suitable for the system and could enable its data size to be reduced.

Keywords

• hobbing-machine diagnosis system
• hobbing simulation
• profile and helix deviations
• network