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.

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.

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.

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.

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.

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.