The invention discloses a heat treatment process for spring
processing and production, and belongs to the technical field of
metal material heat treatment, the heat treatment process for spring
processing and production comprises the following steps: detecting element content through
laser-induced breakdown
spectroscopy, and dynamically adapting parameters such as
quenching temperature by using a
reinforcement learning algorithm; calculating the critical concentration of
titanium carbide based on JMatPro, monitoring the component gradient of a cladding layer in real time, and adjusting the
laser power, the
powder feeding rate and the cladding path; acquiring temperature field and
stress field data by using a sensor, and adjusting the
magnetic field intensity, the pre-deformation amount and the flow velocity of a
cooling medium; a metallographic image is analyzed through a
convolutional neural network, subzero treatment is intelligently triggered, and
tempering parameters are optimized; and according to the
surface roughness, parameters are reversely corrected, and self-optimization is carried out through the block chain. Intelligent and accurate regulation and control of the whole process of component detection,
process control and quality detection are achieved, the
mechanical property and production efficiency of the spring are improved, and the method has the advantages of dynamic
adaptation, real-time monitoring, multi-field
coupling control, data closed-
loop optimization and the like.