In order to solve the problems of high volume fraction silicon carbide particle reinforced aluminum matrix composite (SiC

p

/Al) machining difficulty and poor surface quality

the longitudinal torsional ultrasonic vibration assisted milling composite process was proposed. Taking ultrasonic amplitude

cutting speed

feed per tooth and cutting depth as variables

a four-factor and five-level orthogonal experimental study was designed. By using response surface method and artificial neural network

the pr

ediction models of cutting force

cutting temperature and surface roughness are established

the interaction effect of two indexes among the four parameter variables is analyzed

and the accuracy of the prediction models is compared and verified. Finally

the multi-objective parameters of cutting force

cutting temperature and surface roughness are optimized by genetic algorithm. The results show that both the response surface method and the artificial neural network have better predictive ability

but the artificial neural network is more accurate. The optimal parameter combination optimized by genetic algorithm is ultrasonic amplitude A=1.84 μm

cutting speed v

c

=20 m/min

feed per tooth f

z

=0.015 mm/z

cutting depth a

p

=0.8 mm. After verification experiment

it is found that the optimal parameter can effectively reduce the cutting force

cutting temperature and surface roughness

and the values are F

t

=7.23 N

T=40.18 ℃

R

a

=2.4673 μm

respectively. And the prediction errors were 6.91%

6.53% and 2.53%

respectively

which proved the accuracy of the prediction model and the effectiveness of the optimization parameters.