The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.
Based on the theory of exact boundary controllability of nodal profile for hyperbolic systems, the authors propose the concept of exact boundary controllability of partial nodal profile to expand the scope of applications. With the new concept, we can shorten the controllability time, save the number of controls, and increase the number of charged nodes with given nodal profiles. Furthermore, we introduce the concept of in-situ controlled node to deal with a new situation that one node can be charged and controlled simultaneously. The minimum number of boundary controls on the entire tree-like network is determined by using the concept of ‘degree of freedom of charged nodes’ introduced. And the concept of ‘control path’ is introduced to appropriately divide the network, so that we can determine the infimum of controllability time. General frameworks of constructive proof are given on a single interval, a star-like network, a chain-like network and a planar tree-like network for linear wave equation(s) with Dirichlet, Neumann, Robin and dissipative boundary conditions to establish a complete theory on the exact boundary controllability of partial nodal profile. 相似文献
Let G be a graph, the order of G, the connectivity of G and k a positive integer such that . Then G is said to be k-extendable if it has a matching of size k and every matching of size k extends to a perfect matching of G. A Hamiltonian path of a graph G is a spanning path of G. A bipartite graph G with vertex sets and is defined to be Hamiltonian-laceable if such that and for every pair of vertices and , there exists a Hamiltonian path in G with endpoints p and q, or and for every pair of vertices , there exists a Hamiltonian path in G with endpoints p and q. Let G be a bipartite graph with bipartition . Define to be a maximum integer such that and (1) for each non-empty subset S of X, if , then , and if , then ; and (2) for each non-empty subset S of Y, if , then , and if , then ; and (3) if there is no non-negative integer satisfying (1) and (2).Let G be a bipartite graph with bipartition such that and . In this paper, we show that if , then G is Hamiltonian-laceable; or if , then for every pair of vertices and , there is an -path P in G with . We show some of its corollaries in k-extendable, bipartite graphs and a conjecture in k-extendable graphs. 相似文献