This application discloses a method and
system for preparing aged oil-based water-based
metal cutting fluid based on
machine learning, comprising: obtaining stability characterization parameters of aged oil for stability classification and determining pretreatment parameters; sequentially performing preheating and
dilution, ultrasonic treatment, electrostatic coalescence,
centrifugal separation and
solid-liquid separation to obtain a
base oil for the
cutting fluid; using the obtained
base oil as the
oil phase, compounding it with emulsifier, oiliness agent, extreme pressure anti-wear agent,
rust inhibitor, pH stabilizer, bactericide and
defoamer to construct a formulation sample and collect performance and cost data; using the formulation sample to
train a
wavelet neural network to establish a formulation-performance mapping model; using
simulated annealing-
particle swarm optimization algorithm to obtain the target formulation; preparing aged oil-based water-based
metal cutting fluid according to the target formulation and verifying it; this method realizes the high-value
resource utilization of aged oil, effectively demulsifies and dehydrates through multi-stage synergistic treatment, and efficiently obtains the globally optimal formulation by combining
machine learning algorithms. The resulting
cutting fluid has cooling,
rust prevention,
lubrication and cleaning properties, reducing production costs and reducing environmental
pollution.