This study proposes a grinding process from a control perspective to improve surface quality and precision in the machining process and speed up intelligent production. This method provides a cognitive decision-making process. On this basis, a new approach was developed to measure the amount of grinding wheel wear (GWW) and predict surface roughness (SR) in accordance with the compressed air measuring head and hybrid algorithms fuzzy neural networks (ANFIS)–Gaussian regression function (GPR) and Taguchi empirical analysis. A series of experiments were conducted in various processing conditions. The results showed the efficiency of the grinding process in measuring the amount of GWW and predicting the SR of the Ti–6Al–4V alloy accurately. The proposed model is able to predict SR values with 99.69% precision and 98% confidence interval. This study laid the foundation for monitoring GWW and SR in actual industrial environments.

This paper studies the influencing factors on the magnetic material (nickel-phosphorus coated SKD11 steel) polishing process were determined by investigating the distribution of magnetic iron (MIGs) and abrasive grains (AGs) in the working surface of magnetic liquid slurry (MLS). The influence of the factors, namely the diameters of the MIGs and the AGs and working distance (K), on the polishing surface quality was assessed by fuzzy gray analysis. Results showed that the values of the fuzzy gray relationships with the factors were 0.6361, 0.5181, 0.8804, and 0.6464. Result showed that the fuzzy gray relationship point of factors known as K exerted the most remarkable influence on the polishing effect, whereas the effect of MIG diameter was insignificant. The experimental results indicated that MLS containing MIGs with a diameter of 5 μm, AGs with a diameter of 1.5 μm and K = 1 mm should be prepared to create a mirror surface with nickel–phosphorus coated SKD11 steel. An extremely smooth mirror surface with Ra = 0.926 nm is produced under these experimental conditions, leaving no scratches and impurities on the machined surface. The proposed polishing method that uses MLS with MIGs and AGs (which are low-cost and commercially available) is suitable and effective for polishing the surface of magnetic materials with extremely high accuracy.

In this paper, a new algorithm developing to solve optimization problems with many nonlinear factors in ultra-precision machining by magnetic liquid mixture. The presented algorithm is a collective global search inspired by artificial intelligence based on the coordination of nonlinear systems occurring in machining processes. Combining multiple nonlinear systems is established to coordinate various nonlinear objects based on simple physical techniques during machining. The ultimate aim is to create a robust optimization algorithm based on the optimization collaborative of multiple nonlinear systems (OCMNO) with the same flexibility and high convergence established in optimizing surface quality and material removal rate (MRR) when polishing the SKD61-coated Ni-P material. The benchmark functions analyzing and the established optimization polishing process SKD61-coated Ni-P material to show the effectiveness of the proposed OCMNO algorithm. Polishing experiments demonstrate the optimal technological parameters based on a new algorithm and rotary magnetic polishing method to give the best-machined surface quality. From the analysis and experiment results when polishing magnetic SKD 61 coated Ni-P materials in a rotating magnetic field when using a Magnetic Compound Fluid (MCF). The technological parameters according to the OCMNO algorithm for ultra-smooth surface quality with Ra = 1.137 nm without leaving any scratches on the after-polishing surface. The study aims to provide an excellent reference value in optimizing the surface polishing of difficult-to-machine materials, such as SKD 61 coated Ni-P material, materials in the mould industry, and magnetized materials.

High-speed milling (HSM) aims to create high-quality surfaces and complex profiles and to increase the ability to eliminate excess material. This affects total energy consumption (EC), which in turn has an impact not only on the environment but also on production costs. Therefore, accurately evaluating how much energy is consumed by HSM is very important. EC is affected by stochastic tool wear, that is, when the latter increases, it leads to a corresponding increase in cutting force and vibration, thereby increasing EC. Therefore, the nonlinear processes caused by tool wear are determined through singularity vibration analysis combined with consideration of the geometry of the cutting edge with the aim of providing a predictive model for real-time prediction of the cutting force. Based on the cutting force components, an EC calculation model has been established. In particular, a new hybrid algorithm based on back-propagation neural network and multi-objective particle swarm optimisation is developed to determine the optimal cutting parameters, which can help minimise the total EC. HSM experiments were conducted to confirm the accuracy and availability of the proposed online monitoring and optimisation model. The proposed optimisation method based on the improvement model can reduce EC by 10.49% compared to the empirical option method. The feasibility and effectiveness of the proposed model is also verified through experimental processes.