Abstract: A separator includes a substrate and a coating. The coating is formed on at least a portion of surface of the substrate. The coating includes composite particles and a binder. The composite particles form bulges on surface of the coating. The composite particles include polyacrylate particles and first inorganic particles. The first inorganic particle is present between at least two of the polyacrylate particles.

Abstract: The invention provides a measurement method and system of undeformed chip thickness in micro-milling. This method includes the steps of: S1: acquiring a surface topography picture of the bottom of a flute after micro-milling; S2: extracting a tool mark at the central line of the flute from the surface topography picture; S3: calculating a spacing distance between adjacent tool marks and calculating the difference of equivalent cutting radius between adjacent cutter teeth based on the spacing distance between adjacent tool marks; and S4: reconstructing the instant undeformed chip thickness in micro-milling based on the difference of equivalent cutting radius between adjacent cutter teeth.

Abstract: The invention provides a measurement method and system of undeformed chip thickness in micro-milling. This method includes the steps of: S1: acquiring a surface topography picture of the bottom of a flute after micro-milling; S2: extracting a tool mark at the central line of the flute from the surface topography picture; S3: calculating a spacing distance between adjacent tool marks and calculating the difference of equivalent cutting radius between adjacent cutter teeth based on the spacing distance between adjacent tool marks; and S4: reconstructing the instant undeformed chip thickness in micro-milling based on the difference of equivalent cutting radius between adjacent cutter teeth.

Abstract: A hybrid causal framework applies properties of probabilistic models, such as Bayesian belief networks, to causal logic models, such as fault trees and event sequence diagrams. The probabilistic model establishes a joint probability distribution of causal relationships between events and conditions in the logic models. The probability of the events and conditions are found by propagating probabilities from the probabilistic model through the logic models.

Abstract: A hybrid causal framework applies properties of probabilistic models, such as Bayesian belief networks, to causal logic models, such as fault trees and event sequence diagrams. The probabilistic model establishes a joint probability distribution of causal relationships between events and conditions in the logic models. The probability of the events and conditions are found by propagating probabilities from the probabilistic model through the logic models.