Optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm

Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.

Optimal design of structural parameters for shield cutterhead based on fuzzy mathematics and multi-objective genetic algorithm

In order to improve the strength and stiffness of shield cutterhead, the method of fuzzy mathematics theory in combination with the finite element analysis is adopted. An optimal design model of structural parameters for shield cutterhead is formulated,based on the complex engineering technical requirements. In the model, as the objective function of the model is a composite function of the strength and stiffness, the response surface method is applied to formulate the approximate function of objective function in order to reduce the solution scale of optimal problem. A multi-objective genetic algorithm is used to solve the cutterhead structure design problem and the change rule of the stress-strain with various structural parameters as well as their optimal values were researched under specific geological conditions. The results show that compared with original cutterhead structure scheme, the obtained optimal scheme of the cutterhead structure can greatly improve the strength and stiffness of the cutterhead, which can be seen from the reduction of its maximum equivalent stress by 21.2%, that of its maximum deformation by 0.75%, and that of its mass by 1.04%.

Surface Characteristics Measurement Using Computer Vision:A Review

Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured.For any components to execute their intended functions and operations,surface quality is considered equally significant to dimensional quality.Surface Roughness(Ra)is a widely recognized measure to evaluate and investigate the surface quality of machined parts.Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100%inspection and examination because of the time and efforts involved in performing the measurement.However,Machine vision has emerged as the innovative approach to executing the surface roughness measurement.It can provide economic,automated,quick,and reliable solutions.This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics,i.e.,surface roughness,waviness,flatness,surface texture,etc.,machine vision parameters.This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.

Experimental Study of Electrical‑Assisted Nanomachining of Monocrystalline Copper Using Customized Tungsten Tip

As a promising micro/nanofabrication method,electrical-assisted nanomachining has obtained substantial attention due to its high material removal rate and attainable superior surface quality.In this study,a rectangular wave electrical signal was applied for nanomachining by a customized tungsten tip.Owing to the coupling effect between the electric field and mechanical force,the cutting depth of the machined grooves can be expanded.In electrical-assisted groove processing,a depth of 270 nm and an aspect ratio of 0.6 on the copper sample can be achieved.The influence of operation parameters including applied voltage,frequency,duty ratio,normal force and cutting speed on the machining performance was investigated in terms of the groove depth,width,aspect ratio,and surface roughness.The potential machining mechanisms should be a combination of electric field force,nanoscale electric discharge,electric contact thermal effects,possible annealing behavior,and scraping and plowing actions induced by mechanical forces.

Efficient and Stable Optimization of Multi‑pass End Milling Using a Cloud Drop‑Enabled Particle Swarm Optimization Algorithm

Optimization of machining parameters is of great importance for multi-pass end milling because machining parameters adversely or positively affect the time and quality of production.This paper develops a second-order fulldiscretization method(2ndFDM)-based 3-D stability prediction model for simultaneous optimization of spindle speed,axial cutting depth and radial cutting depth.The optimal machining parameters in each pass are obtained to achieve the minimum production time comprehensive considering constraints of 3-D stability,machine tool performance,tool life and machining requirements.A cloud drop-enabled particle swarm optimization(CDPSO)algorithm is proposed to solve the developed machining parameter optimization,and 13 benchmark problems are used to evaluate CDPSO algorithm.Numerical results show that CDPSO algorithm has a certain advantage in computational cost as well as comparable search quality and robustness.A demonstrative example is provided.

A Regular Study on Yarn Count, Size Box Temperature and Machine Speed to Increase Weaving Efficiency of Cotton Fabric

Sizing is an inherent part of weaving works, consisting in the coating of the warp yarn with a polymeric adhesive, such as starch, in order to assist efficient weaving. The study is aimed to assess the effects of squeezed roller pressure, dryer temperature, yarn count, machine speed (rpm) on cotton fabric weaving. Coarser and finer cotton yarn samples were prepared using sizing solution BENSIZE 850. Different size box temperature, yarn count, fabric construction, machine speed, squeeze roller pressure were considered to construct different weaving designs to study yarn breakages parameter. A warping plan was designed on TAROKO V5.4 (190325) software. The results established that size box lower temperature and higher machine speed provide the smallest amount yarn break during weaving for coarser cotton yarn and the highest for finer cotton yarn. Size box higher temperature and lower machine speed provide maximum yarn breakage during weaving coarser cotton yarn and minimum for fine yarn. Size penetration is uniform, which provides a higher strength of the yarn to less breakage. This aspect of the research suggested that higher yarn strength gives a lesser amount of breakage.

Dosimetric evaluation of CR, 3DCRT and two types of IMRT for breast cancer after conservative surgery

Objective: The purpose of this study was to compare the dose distribution and dose volume histogram (DVH) of the planning target volume (PTV) and organs at risk (OARs) among conventional radiation therapy (CR), three-dimensional conformal radiation therapy (3DCRT), two-step intensity-modulated radiation therapy (TS-IMRT) and direct machine parameter optimization intensity-modulated radiation therapy (DMPO-IMRT) after breast-conserving surgery. Methods: For each of 20 randomly chosen patients, 4 plans were designed using 4 irradiation techniques. The prescribed dose was 50 Gy/2 Gy/25 f, 95% of the planning target volume received this dose. The cumulated DVHs and 3D dose distributions of CR, 3DCRT, TS-IMRT and DMPO-IMRT plans were compared. Results: For the homogeneity indices, no statistically significant difference was observed among CR, 3DCRT, TS-IMRT and DMPO-IMRT while the difference of the conformality indices were statistically significant. With regard to the organs at risk, IMRT and 3DCRT showed a significantly fewer exposure dose to the ipsilateral lung than CR in the high-dose area while in the low-dose area, IMRT demonstrated a significant increase of exposure dose to ipsilateral lung, heart and contralateral breast compared with 3DCRT and CR. In addition, the monitor units (MUs) for DMPO-IMRT were approximately 26% more than those of TS-IMRT and the segments of the former were approximately 24% less than those of the latter. Conclusion: Compared with CR, 3DCRT and IMRT improved the homogeneity and conformity of PTV, reduced the irradiated volume of OARs in high dose area but IMRT increased the irradiated volume of OARs in low dose area. DMPO-IMRT plan has fewer delivery time but more MUs than TS-IMRT.

METHOD OF ENHANCING THE POSITIONING ACCURACY FOR NC MACHINE TOOLS

Based on the kinematics of the multi-body system , a general model for the positioning errors of NC machine tools by means of the lower numbered body array and the geometric constraint is presented. The parameters identification of geometric errors by an improved 22-line method is discussed. Moreover , an intelligent error compensation controller has been developed. All these are verified by a series of experiments on XH714 machining center. The results show that the prosition- ing errors with compensation have been reduced to ±7 μm from 50 μm.

Inflatable Wing Design Parameter Optimization Using Orthogonal Testing and Support Vector Machines

The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing parameter design method, this paper proposes an optimization design scheme based on orthogonal testing and support vector machines （SVMs）. Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency. Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure, uniform applied load and the number of chambers that affect the bending response of inflatable wings. An SVM intelligent model is established and limited orthogonal test swatches are studied. Thus, the precise relationships between each parameter and product quality features, as well the signal-to-noise ratio （SNR）, can be obtained. This can guide general technological design optimization.

Active Disturbance Rejection Control for PMLM Servo System in CNC Machining

Uncertain friction is a key factor that influences the accuracy of servo system in CNC machine.In this paper,based on the principle of Active Disturbance Rejection Control(ADRC),a control method is proposed,where both the extended state observer(ESO) and the reduced order extended state observer(RESO) are used to estimate and compensate for the disturbance.The authors prove that both approaches ensure high accuracy in theory,and give the criterion for parameters selection.The authors also prove that ADRC with RESO performs better than that with ESO both in disturbance estimation and tracking error.The simulation results on CNC machine show the effectiveness and feasibility of our control approaches.