A
machine vision-based method for automatic measurement and deviation correction of workpiece dimensions, relating to the field of precision
manufacturing technology, comprises the following steps: First, data from multi-process workpieces is collected and preprocessed to form a complete basic dataset; then, a dedicated feature
anchor point benchmark
library is constructed; next, self-calibration and coordinate unification are performed based on this
library; subsequently, error decoupling and
weight analysis are conducted to calculate the total deviation and the weights of various errors; finally, errors are corrected based on the relevant results, measurement results are output, a residual optimization model is fed back, the correction accuracy is verified, and the
processing accuracy is improved. This invention constructs a dedicated feature
anchor point benchmark
library, achieving precise correlation and dynamic self-calibration between anchor points and errors, and unifying the coordinate
system; through algorithms such as spatiotemporal decoupling of
anchor point constraints, the total dimensional deviation across the entire process is accurately decomposed,
error separation and correction are completed, and feedback correction is introduced to form a fully closed-loop
system, improving the dimensional accuracy and overall stability of precision workpiece
processing.