Drilling of Carbon Fiber-Reinforced Plastic/Titanium alloy (CFRP/Ti) stacks represents one of the most widely used machining methods for making holes to fasten assemblies in civil aircraft. However, poor machinability of CFRP/Ti stacks in combination with the inhomogeneous behavior of CFRP and Ti alloy face manufacturing and scientific community with a problem of defining significant factors and conditions for ensuring hole quality in the CFRP/Ti alloy stacks. Herein, we investigate the effects of drilling parameters on drilling temperature and hole quality in CFRP/Ti alloy stacks by applying an artificial neuron network (ANN). We varied cutting speed, feed rate, and time delay factors according to the factorial design L9 Taguchi orthogonal array and measured the drilling temperature, hole diameter, and out of roundness by using a thermocouple and coordinate measuring machine methods for ANN analysis. The results show that the drilling temperature was sensitive to the effect of stack material layer, cutting speed, and time delay factors. The hole diameter was mainly affected by feed, stack material layer, and time delay, while out of roundness was influenced by the time delay, stack material layer, and cutting speed. Overall, ANN can be used for the identification of the drilling parameters–hole quality relationship.

The work deals with the technology of metal machining with a focus on the technology of broaching the internal shaped surfaces. The design of the drawing tools is a tool with several cutting edges, whereby the final shape of the inner shaped surface is made for one rectilinear movement of the tool. The cost of broaching tools is higher. For these reasons, the broaching technology is suitable for series production. One part of the paper covers the research of the achieved roughness of the internal grooves when changing the cutting environment, an emulsion was used instead of oil. The second part involves the use of statistical methods in the technology of broaching internal contoured surfaces. The research is focused on the machine capability. The results of measuring the accuracy of the tolerated dimension of the inner holes after broaching are used as input values to this statistic.

Currently, in view of the fact that almost all the tasks of the practice of controlling CNC machine and robot drives cannot be accurately represented by linear models, and there is no solution to non-linear models in the general case, a very important task is to develop control algorithms based on discrete models. Discrete models of nonlinear systems assume variable state, control, and measurement matrices that determine an infinite number of variants of this model. Therefore, some tool is needed to calculate the degree of adequacy of mathematical models and real objects. The paper considers theoretical statements related to the main directions of research in the field of theoretical issues - the study of the dynamics of CNC machine and robot drives and their modeling. The paper studies the drives of CNC machine and robots by the criterion of identifiability based on a discrete digital control model. Criteria of observability, controllability and identifiability of drives are considered as a function of the rank of an extended state matrix with a measurement matrix, in which the relative errors of the information-measuring system are analytically taken into account. An algorithm for calculating the identifiability criterion for a nonlinear control system in a discrete linearization version is proposed. It is proposed to use identification in terms of the correspondence of the mathematical model to the results of the operation of the object. Drive control by means of a discrete vector-matrix algorithm involves the calculation of the state matrix at each step. Therefore, at each step, the determinant of the extended matrix is calculated, which is compared with a constant that numerically divides the space of the state matrices. Thus, the operation of the drives itself makes it possible to determine its identifiability. As a criterion for the optimality of the identification algorithm, a decision-making optimality criterion is chosen in combination with an identifiability criterion for an optimal control algorithm by the criterion of minimum quadratic form. The vector-matrix model of drives in the state space is presented taking into account the relative accuracy of measuring the state of the information-measuring subsystem of drives. It is proposed for practical problems to determine the identifiability criterion by modeling the state matrix for cases when the state matrix parameters exit the space of realizable parameters of serviceable drives. The linearized model of CNC machine and robot drives has limitations in accordance with technical characteristic, for example, restrictions on the strength of electric current and voltage. The obtained research results can be used to build diagnostic systems for CNC machine and robot drives.

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