This paper presents the influence of an inorganic Al₂O₃ layer over MoS₂ particles on the tribological performance of electroless Ni–P–MoS₂/Al₂O₃ composite coatings fabricated without using surfactants. The Al₂O₃-coated MoS₂ particles were prepared by a heterogeneous nucleation process. The dry sliding tests of the composite coatings were tested against a WC ball. SEM was used to observe the surface morphology of particles, composite coatings, and worn surfaces. The results indicate that the coverage of an Al₂O₃ coating on MoS₂ particles significantly affects the surface morphology, frictional coefficient and wear loss of the composite coatings. The incorporation of Al₂O₃-coated MoS₂ particles with lower coverage (up to 7% of Al₂O₃) could obtain compact surface structure of composite coatings, which contribute to reduced wear loss. However, higher coverage would lead to loose surface structure of the composite coatings, and thus increase their wear loss.

The traditional machine tool design method with metal materials makes large-scale moving structures very heavy, which seriously impacts dynamic performance and results in significant energy consumption. Using sandwich structures of composite materials to replace metal materials is an important strategy for lightweight large-scale moving structures. However, this kind of substitution is generally believed to be difficult because foam-filled sandwich structures usually show nonlinear characteristics and must balance the moving mass, material costs, and structural stiffness. In the present study, we proposed a design optimization approach for a large-scale moving framework in a large 5-axis machining center (L5AMC) considering large dimensions in the x, y, and z work space and high machining speed with the aim of minimizing the displacements of the milling head. An improved approach, named the 3-step design optimization, was executed to obtain the optimum framework structures to solve the contradiction between the moving mass, material costs, and structural stiffness. This approach was based on multi-objective optimization and finite element analysis. The structural stiffness of the framework after optimization increased by 89% compared with before optimization although the mass increased by 6% and the material costs increased by 9%. A finite element simulation under four given operational loads showed that the displacements of the milling head were all less than the design requirement of 0.25 mm. The results indicated that the proposed 3-step design optimization approach for the optimal design of a large-scale moving framework was feasible and successful. A 40 m × 6 m × 4 m L5AMC prototype was manufactured, and the actual verification results indicated that the large-scale moving framework fully met the design requirements of the L5AMC and reduced energy consumption.

A multivariable generalized predictive decoupling control algorithm with constraints is proposed for a series multivariable system composed of complex industrial plants. The tracking error caused by the coupling of the system can be reduced by changing the weight of the error, so that the coupling degree of the system can be reduced. The constraint conditions of the system variables are considered, and the control variables are obtained by quadratic programming. The simulation results of the proposed method and the conventional multivariable constrained generalized predictive control algorithm on a series plant show the superiority of the algorithm.