Search

A constrained optimisation problem (COP) is solved by solving an equivalent dynamic loosely-constrained multiobjective optimisation problem in this paper. Two strategies are considered. 1) An additional objective (constrained-violation objective) is introduced to obtain a two-objective optimisation problem. This provides a framework for adopting multi-objective techniques to solve the COP, 2) A dynamic constraint boundary is introduced to obtain an equivalent dynamic loosely-constrained multiobjective optimisation problem since a broad boundary is gradually slightly reduced to the original constraint boundary. This suggests that an dynamic constrained multiobjective evolutionary algorithm (DCMOEA) can performs as effective as that of a multiobjective evolutionary algorithm (MOEA) in solving an unconstrained multiobjective optimisation problem. The idea is implemented into three major types of MOEAs, i.e., Pareto ranking based method, decomposition based method, preference-inspired co-evolutionary method. These three instantiations are tested on two sets of benchmark problems. Experimental results show that they are better than or competitive to two state-of-the-art constraint optimisers, especially for the problems with high dimensions.

Tribological properties of nano–particle jet minimum quantity lubrication (MQL) grinding were mainly evaluated in K–P36 NC surface grinder. Under dry grinding, flood grinding, MQL grinding and nano–particle jet MQL grinding working conditions, the tribological properties were evaluated from the perspectives of grinding force, frictional coefficient, G–ratio, grinding temperature and surface roughness. The results showed that nano–particle jet MQL grinding improved the heat exchange capability; the temperature reduced by nearly 150°C compared with that in dry grinding. Compared with dry grinding, frictional coefficient in MQL grinding, nano–particle jet MQL grinding and flood grinding was decreased successively and respectively by 11.22%, 29.21% and 32.18%. The surface roughness Ra of workpiece prepared by dry grinding was 1.2 µm, and the Ra value of nano–particle jet MQL grinding was 0.58 µm. The G ratio of nano–particle jet MQL grinding was 33, the highest of all four conditions. The G–ratio of dry grinding was only 12, which was about twice the value in other conditions.

In differential evolution (DE), many adaptive algorithms have been proposed for parameter adaptation. However, they mainly focus on tuning the mutation factor F and crossover probability CR. The adaptation of population size NP has not been widely studied in the scope of DE. Reducing population size could save computational resources and hence accelerate the convergence speed of algorithms. This is beneficial for algorithms to solve the optimisation problems which need expensive evaluations. However, population reduction may weaken the population diversity, hence, it will result in population premature. In this paper, we propose a novel population reduction method for jDE algorithm, called dynNPMinD-jDE. When the population reduction criterion is satisfied, dynNPMinD-jDE selects the best individual and the pair individuals with minimal-step difference vectors to form a new population. To enhance the population diversity, dynNPMinD-jDE adopts the adaptive mechanisms of F and CR. dynNPMinDjDE is tested on a set of 38 scalable benchmark functions. The results show that dynNPMinD-jDE can get better results on most functions, and the convergence speed becomes faster and faster as each population reduction.

The heat-transfer model of a surface grinding temperature field with a nanoparticle jet flow of minimum quantity lubrication (MQL), as well as the proportionality coefficient model of the energy input workpiece, was established. Numerical simulation of the surface grinding temperature field of a nano-ZrO2 dental ceramic workpiece material was conducted. With increased cut depth, the peak values of grinding temperature rocketed. With increased workpiece feed speed, grinding temperature on the finished surface decreased. With increased wheel peripheral speed, a high amount of heat energy accumulated on the surface because of the low heat-transfer coefficient of the ceramic material, and a large temperature gradient appeared in the temperature distribution layer. Under the same cooling and lubrication conditions, grinding temperature insignificantly changed along the direction of grinding width. Conversely, under different cooling conditions, the temperature variation was significant. MQL grinding conditions with additive nanoparticles significantly affected the weakening of temperature effect on the grinding zone.

The material removal mechanism of abrasive jet precision finishing with grinding wheel as restraint was investigated based on the size ratio of the characteristic particle size to the film thickness between a grinding wheel and a workpiece. The single abrasive movement characteristic was studied under the two-body lapping and three-body machining modes. The critical condition for the transformation from two-body lapping to three-body polishing was analysed. Experiments were conducted for theoretical model verification, and the experimental results showed that the surface roughness value decreased with the increase of the number of cycles. Furthermore, as the micrographs show, the machined surface changed from directionally continuous micro-grooves to random pits as machining progressed. The average spacing of finished surface profile waviness was decreased and the ripples and peak density were increased. It was found that the theoretical analysis is accordant with the experimental results.

This paper presents a novel particle swarm optimiser (PSO) called PSO with simple and efficient neighbourhood search strategies (NSPSO), which employs one local and two global neighbourhood search strategies. By this way, one strong and two weak locality perturbation operators are embedded in the standard PSO. The NSPSO consists of two main steps. First, for each particle, three trail particles are generated by the mentioned three neighbourhood search strategies, respectively. Then, the best one among the three trail particles is selected to compete with the current particle, and the fitter one is accepted as a current particle. In order to verify the performance of NSPSO, it experimentally has been tested on 12 unimodal and multimodal benchmark functions. The results show that NPSO significantly outperforms other seven PSO variants.

This article describes the validity of a compound precision finishing process that combines grinding with abrasive jet finishing for micro-removal machining. Experiments were carried out with a M7120 plane grinder and a workpiece material of 40Cr steel, which was ground to a surface roughness with a mean value of Ra = 0.6 μm. The machined surface morphology was studied using a scanning electron microscope (SEM), and a metallography microscope. Microcosmic geometry parameters were measured with a TALYSURF5 instrument. The experimental results show that this novel processing method can effectively diminish longitudinal geometry parameter values of the ground surface, moreover attain an isotropic surface and uniform veins in the parallel and perpendicular machining directions. Furthermore, the finished surface has little comparability compared with a grinding machining surface. The effectiveness of the process was verified.