The invention provides a dynamic evaluation method for roundness uncertainty based on the Bayesian principle. The method comprises the steps: carrying out the roundness sampling on a measured object,and obtaining a group of measurement point data; fitting a circle according to the measurement point data, and further calculating a roundness error; collecting groups of measurement point data, calculating roundness errors of the groups of measurement point data, and taking the roundness errors as a group of random variables; establishing a random variable probability density function, and constructing constraint conditions of probability density; estimating a probability density function of the group of random variables by taking a sample origin moment as a condition and taking the probability density constraint condition as an objective function, and taking the probability density function as prior distribution; and acquiring another group of random variables, calculating to generate another probability density function, and fusing the probability density function of the another group of random variables into prior distribution to form posteriori distribution. The method can realizefusion of geometric tolerance measurement information, and achieves the dynamic evaluation of uncertainty.

Dynamic evaluation method for roundness uncertainty based on Bayesian principle

The invention discloses a deep hole roundness error prediction method based on a Bayesian network, which is used for solving the technical problem of poor practicability of the deep hole roundness error prediction method in the prior art. According to the technical scheme, the mapping relation between the deep hole processing parameter and the deep hole roundness error is constructed by using theBayesian network. The deep hole processing parameters are used as input nodes of the Bayesian network model, and the deep hole roundness error is used as the output class variable. Meanwhile, the axial force, the torque and the vibration characteristics of the cutter in the deep hole machining process are used as the hidden nodes of the network by using the Bayesian network. According to the Bayesian network learning algorithm lacking value data, the network parameters are calculated. The validity of the model under incomplete processing data is ensured, and the applicability of processing data of different deep holes is improved. Meanwhile, the causal relationship of each network node is built according to the intrinsic mechanism of the deep hole processing, and the model is expressed asan interpretable probability model, and the processing parameters are optimized by using the model when the prediction error does not meet the tolerance requirement.

Deep hole roundness error prediction method based on Bayesian network

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Deep hole roundness error prediction method based on Bayesian network