The prediction of cutting force in milling Inconel-718

Due to the enormous engineering advancement in modern industries, the competition in manufacturing technologies has been increasingly intense, as can be seen in automobile and aerospace industries. Nickel-based superalloys are widely in the manufacture of components for aircraft turbine engines for cryogenic tankage, in liquid rockets, reciprocating engines, space vehicles, heat-treating equipment, chemical and petrochemical industries, because of their ability to retain high-strength at elevated temperatures. But, because of its characteristics of high-strength, poor thermal diffusion and work hardening, the cutting of nickel-based superalloys results in decreased tool life and poor efficiency of works. This is much more prominent than in other materials. AISI4340 are widely used in the manufacture of component parts for gear, pistons, and automobiles.     

Soviet patents on heat-treating equipment

Maraging Steels

Soviet patents on heat-treating equipment     

The study of chip characteristics and tool wear in milling of SKD61 mold steel

In recent years, many machining applications have used dry cutting for high-speed cutting, for which, tool life prediction and research are important issues. In this study, a tungsten carbide tool cutting steel SKD61 was used, and the actual value of the chip shooting and the wear of the flank were determined using an industrial camera. The chip type and chip color are also discussed. After color calibration and chromaticity conversion, the model was trained through the neural network and the results were predicted. The tool wear curve and the theoretical curve were found to be closely related, and the chip color changed regularly. In the CIE XY chromaticity coordinate value, when the initial cutting was observed, the chip chromaticity coordinate point range was small, but as the wear amount increased, the chip chromaticity coordinate point range expanded gradually. The tool wear prediction established in this study was determined from the experimental results. The maximum errors of the test and verification were 0.031 mm and 0.022 mm. After calculation, the mean absolute percentage error (MAPE) was within 3 % and the accuracy level is MAPE is less than 10 %, so it has a high accurate prediction ability. Established tool wear predictions are also provided.