Research on optimization of milling parameters for circular thin-walled parts by improving particle swarm Optimization algorithm

Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle's flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.

Series capacitors compensations may cause subsynchronous resonance (SSR) of steam turbine-generators. Using the principle which is depressed SSR by STATCOM and choosing appropriate measured data to design the subsynchronous damping control (SSDC) based on improved particle swarm optimization algorithm which is presented in this paper. Regard the controller parameter of STATCOM as adaptive parameter. Treating the damping ratio as objective function and basing on improved particle swarm optimization algorithm to damp SSR. Take the subsystem within Mohong and Yanchong series capacitors of China Southern Power Grid as example to verify the validity of SSDC.

In recent years, with the increase of network users, people's requirements for communication quality have been gradually improved. In order to increase the coverage of wireless communication(WC) network and improve the efficiency of using communication network, this paper improves the quality of WC by optimizing the base station location, and analyzes the optimal location of base station construction by using the location-seeking characteristics of improved particle swarm optimization algorithm. In this regard, based on the improved particle swarm algorithm, the optimal network coverage optimization goal is achieved by designing the base station location optimization scheme with the least number of network nodes and the least energy consumption. The experimental results show that the network energy consumption is smaller and the network survival time is more after the original PSO algorithm and the improved PSO algorithm compared to the unoptimized base station location, and the improved PSO algorithm is more effective than the original PSO algorithm.

The task scheduling policy is the important factors for achieving efficient calculation in a cloud computing environment. This article put forwards a task scheduling method based on improved particle swarm algorithm against the present inefficiency. Particle Swarm Optimization (PSO) algorithm is used to solve task scheduling optimization by introducing the iterative selection operator. Improved particle swarm optimization algorithm (IPSO) can improve the ability of the optimization, as much as possible avoiding falling into a local optimum. The convergence effect is so better that the task scheduling time costs can be reduced. By simulation on a CloudSim simulation platform, the experimental results show that the algorithm has the advantages of improving optimization and taking less time. So it can be used to research and practice about cloud computing problem for complex scheduling optimization.

In response to the deficiencies of commonly used optimization methods for assembly lines, a production demand-oriented optimization method for assembly lines is proposed. Taking a certain compressor assembly line as an example, the production rhythm and the number of workstations are calculated based on production requirements and working systems. With assembly rhythm and smoothing index as optimization goals, an improved particle swarm optimization algorithm is employed for process allocation. Subsequently, Flexsim simulation is used to analyze the assembly line. The final results show that after optimization using the improved particle swarm algorithm, the assembly line balance rate increased from 71.1% to 85.9%, and the assembly line smoothing index decreased from 47.4 to 29.8, significantly enhancing assembly efficiency. This demonstrates the effectiveness of the proposed optimization method for the assembly line and provides a reference for other products in the same industry.

With the rapid development of global trade, the turnover of shipping containers has increased rapidly. How to use port resources reasonably and efficiently has become one of the main challenges that ports need to deal with when planning for the future. In order to develop scientific and efficient berth plans to improve operational efficiency and service level, this paper proposes a hybrid prediction model based on Principal Component Analysis (PCA) and Extreme Learning Machine (ELM) optimized by Improved Particle Swarm Optimization (IPSO), namely, the PCA-IPSO-ELM model. After assessing the uncertain factors influencing the operation time of the container ship at berth, this work reduces the dimensionality of the investigational data by the PCA method. Aiming to solve easy premature convergence of the traditional particle swarm algorithm, this paper introduces an improved particle swarm optimization algorithm via dynamic adjustment of nonlinear parameters. This improved particle swarm algorithm is mainly used to optimize the weights and thresholds of the extreme learning machine. Thus, a PCA-IPSO-ELM model which aims to forecast the operation time of a container ship at berth, is constructed. Using the historical operation data of the Tianjin Port Container Shipping Company as the prediction sample, this PCA-IPSO-ELM model is compared and assessed with traditional models. The results show that compared with other models, the PCA-IPSO-ELM prediction model has the characteristics of high prediction accuracy, fast running rate and strong stability, and it has a higher coefficient of determination and a better fitting degree.

In order to fit in with the demands of the development of electricity market in China, a multi-objective optimization mathematical model is presented to dispatch load within the units, taking economy, environmental protection and quick responsiveness to dispatching commands into consideration at the same time. And take the minimal whole plants power-supply coal cost rate, the minimal pollutant emissions and the minimal load adjusting time as these three objective functions respectively. The four constraint conditions are unit power balance constraint, load bound constraint, ramping constraint and pollution discharge standards constraint. An improved particle swarm optimization algorithm is used to get the Pareto solution set. The optimal solution was obtained by using the method of multi-attribute decision making, through sequencing the solution set by comprehensive evaluation. A case study based on a power plant with 4×600MW units was carried out. The result shows that the method can solve the multi-objective optimal load distribution problem accurately and quickly, and get the good effect in energy conservation and emissions reduction.

To explore the milling characteristics of Ni-based superalloy under minimal quantity lubrication (MQL), a single-factor experiment was adopted to investigate the milling machinability of GH4169 Ni-based superalloy. The influences of milling parameters and cooling methods on workpiece surface roughness, milling force, and surface hardness were analyzed. The results showed that the milling parameters and cooling methods have significant effects on the milling machinability of GH4169 Ni-based superalloy. The milling force was Fy > Fx > Fz, and the milling force decreased first and then increased with the increasing cutting speed. The surface roughness, surface hardness, and work hardening rate of the workpieces decreased with the increasing cutting speed and increased with the increasing feed per tooth and radial cutting depth. The milling parameters had a nonsignificant effect on the depth of the work hardening layer in GH4169 Ni-based superalloy. The order of cooling methods to obtain the minimum surface roughness and minimum milling force is nanofluid MQL > MQL > air cooling > no cooling.

Deformations of thin-walled plate due to static end milling force

An improved particle swarm optimization algorithm for the reliability–redundancy allocation problem with global reliability

Milling force prediction is one of the most important ways to improve the quality of products and stability in robot stone machining. In this paper, support vector machines (SVMs) are introduced to model the milling force of white marble, and the model parameters in the SVMs are optimized by the improved quantum-behaved particle swarm optimization (IQPSO) algorithm. A set of online inspection data from stone-machining robotic manipulators is adopted to train and test the model. The overall performance of the model is evaluated based on the decision coefficient (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE), and the results obtained by IQPSO-SVM are superior to those of the PSO-SVM model. On this basis, the relationship between the milling force of white marble and various machining parameters is explored to obtain optimal machining parameters. The proposed model provides a tool for the adjustment of machining parameters to ensure stable machining quality. This approach is a new method and concept for milling force control and optimization research in the robotic stone milling process.

Micromilling mechanism studies are the fundamentals for high-quality micro components fabrications. Based on the finite element method (FEM), the simulation model for micromilling of the oxygen-free copper (OFC) was established in this paper. The influences of the key process parameters (feed engagement fz, axial depth of cut ap, radial depth of cut ae, and spindle speed n) on the milling forces were investigated. According to the simulation results, to achieve the minimum undeformed chip thickness, the critical feed engagement was identified to be 2.5 μm/z. when the axial depth of cut increased, the milling force was increasing linearly, but at the same time the milling force took an oscillator decreases due to the increasing of spindle speed. The overall trend of milling force under different radial depths of cut is upward. Therefore, the feed engagement should be greater than 2.5μm/z for micro milling of oxygen-free copper. Small radial depth of cut, small axial depth of cut, and appropriate spindle speed should be selected to obtain a high machining quality. Introduction As an excellent metal with extremely low oxygen content, high conductivity and high corrosion resistance, oxygen-free copper C10100 is widely used in the manufacturing of audio-visual equipment and communication components. With good ductility and no hydrogen embrittlement, it can also be processed into various precision micro parts. Compared with traditional cutting, high-speed micromilling is more suitable for machining micro-small parts. In order to obtain higher dimensional accuracy and better surface quality, it is necessary to study the milling parameters of OFC in the processing of precision parts. In the actual machining process, the changes of milling force caused by the changes of milling parameters can reflect the quality of the final machined parts more intuitively. Therefore, the adjustment of milling parameters can be guided by measuring the milling force in the milling process. In order to reveal the difference in mechanism between traditional cutting and micromilling, scholars have conducted a lot of studies. In the study of cutting poly-crystalling OFC with single crystalline diamond (SOD) micro-tools, X. Ding has proposed that the cutting strategy of reducing cross-feed can improve the cutting performance with using micro-tools. Constant cutting forces, burr size and an improved roughness of the machined surface have been obtained by employing this cutting strategy [1]. Niu has presented an innovative cutting force modeling concept by taking account of micro-cutting dynamics. Experimental cutting trial is conducted to validate the modeling approach and the cutting force model [2]. Qu has studied the impact of MQCL method on the machining results and mechanical properties of the component [3]. Yuan has proposed an innovative uncut chip thickness algorithm and developed the model for determining the instantaneous uncut chip thickness in micro end-milling processes [4]. D.DAS has studied the effect of stress parameters on ratcheting deformation stages of polycrystalline OFHC copper [5]. For the micromilling of pure copper, M. Rahman has studied the failure mechanism and factors which affect the micro end mill [6]. Liu has studied the relationship between size effect and burr in micromilling OFC and constructed a geometric model of exit burrs [7]. Liu has established SPH simulation model and carried out systematic research on formation mechanism of chip [8].For oxygen-free copper materials, Tian has established a constitutive model and carried out simulation analysis based on Abaqus [9]. Cheng has proposed 211

Thin-walled frame parts are widely used in aerospace and other fields. However, part deformation caused by cutting load and residual stress poses significant challenges to manufacturing accuracy and parts performance. To enhance the machining accuracy and part performance, this research proposes a novel clamping layout optimization method on the basis of deconvolution neural network (DCNN) and particle swarm optimization algorithm (PSO). The method integrates simulation model to simulate deformation and uses DCNN to predict the deformation field efficiently. The PSO is then applied to optimize the clamping layout while meeting physical and interference constraints. The case study shows that this method effectively reduces the deformation in the web area of thin-walled frame parts. Compared with the uniform layout, the optimization objective function value is reduced by 21.4%, and the maximal deformation and average deformation are separately decreased by 16.0% and 39.8%, which verifies the effectiveness of the presented method. This research not only addresses the critical issue of machining deformation in thin-walled parts but also demonstrates a novel combination of artificial intelligence based modeling and simulation techniques, which can be expanded to other advanced manufacturing challenges.

Analytical model of milling forces based on time-variant sculptured shear surface

An opposition-based improved particle swarm optimization algorithm (OIPSO) is presented for solving multiobjective reactive power optimization problem. OIPSO uses the opposition learning to improve search efficiency, adopts inertia weight factors to balance global and local exploration, and takes crossover and mutation and neighborhood model strategy to enhance population diversity. Then, a new multiobjective model is built, which includes system network loss, voltage dissatisfaction, and switching operation. Based on the market cost prices, objective functions are converted to least-cost model. In modeling process, switching operation cost is described according to the life cycle cost of transformer, and voltage dissatisfaction penalty is developed considering different voltage quality requirements of customers. The experiment is done on the new mathematical model. Through the simulation of IEEE 30-, 118-bus power systems, the results prove that OIPSO is more efficient to solve reactive power optimization problems and the model is more accurate to reflect the real power system operation.