In this paper, a new approach for evaluating the cryogenic machining process of the carbon nanotube reinforced aluminum matrix composites is developed based on finite element method. Finite element modeling in commercial code ABAQUS/Explicit was used to simulate high-speed machining of carbon nanotube reinforced composites under dry and cryogenic conditions, where different parameters (carbon nanotubes loading and the cutting speed) were investigated. The matrix phases are given a Johnson–Cook failure criterion. For considering more realistic assumptions, mechanical and thermal properties of the materials are assumed as a function of temperature. Results shown that at the cutting velocity of 60 m/s, cryogenic cooling has caused decrease of workpiece plastic strain by 12% in comparison with the dry cooling. The model can be used to study the effect of weight fraction, orientation, and length of the carbon nanotubes on the manufacturing of the nanocomposites.
In this paper a novel optimal approach of control strategy is introduced by applying fractional calculus in the structure of sliding mode control for a range of dynamics system liable to ambiguity. So, a fractional sliding mode control was designed for dynamics of the two-link rigid-flexible manipulator. Furthermore, a multi-objective genetic algorithm was proposed in order to find the ideal variable structure of the sliding mode control. Optimal variables were achieved by the optimization of the conventional sliding mode control. Then the performance of both the conventional and the fractional sliding mode control were compared with respect to optimal variables. Results indicated that by applying the optimized fractional sliding mode control, the system's error was significantly reduced consequently tracking the desired value was done with a higher degree of accuracy and a smoother control action was achieved.
Accuracy of a five-axis CNC machine tool is affected by a vast number of error sources. This paper investigates volumetric error modeling and its compensation to the basis for creation of new tool path for improvement of work pieces accuracy. The volumetric error model of a five-axis machine tool with the configuration RTTTR (tilting head B-axis and rotary table in work piece side A΄) was set up taking into consideration rigid body kinematics and homogeneous transformation matrix, in which 37 error components are included. Volumetric error comprises 37 error components that can separately reduce geometrical and dimensional accuracy of work pieces. The machining accuracy of work piece is guaranteed due to the position of the cutting tool center point (TCP) relative to the work piece. The cutting tool is deviated from its ideal position relative to the work piece and machining error is experienced. For compensation process detection of the present tool path and analysis of the RTTTR fiveaxis CNC machine tools geometrical error, translating current position of component to compensated positions using the Kinematics error model, converting newly created component to new tool paths using the compensation algorithms and finally editing old G-codes using G-code generator algorithm have been employed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.