System for Sewing Machine Industry Standard Innovation——Constructive Research for a Technical Standards System for Sewing Machines

Constructive research of a market-oriented industry's system of technical standardization by redefining those technical standards is the basis of innovation. Through considering and implementing innovation of the industry's standards, rapid development and standardization of the industry can be achieved.

CHINA'S SEWING MACHINE INDUSTRY

China’s export of sewing machinesstarted in the 1950s.At that time theButterfly brand household sewingmachine produced by the Shanghai XiechangSewing Machine Factory and the Tiger brandsewing machine produced by the ShanghaiHuigong Sewing Machine Factory enjoyeda good reputation in Southeast Asia. Along with China’s reform and openingdrive and economic development,and on thebasis of changes in market demand,thesewing machine industry has conducted rapidadjustment to the product structure,withmore stress being placed on technicalintroduction and renovation.Under theleadership an support of the Ministry ofLight Industry,products have developed fromhousehold sewing machines to Industrialones.While meeting the domestic

Rock burst prediction based on genetic algorithms and extreme learning machine

Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.

Formation and Control of Drilling Burrs

In this paper, a new forming model of the feed direction burr for drilling process is presented. The feed direction burr formation is experimented and studied. The related theories are analyzed, and the influential factors of the feed direction burrs are pointed out. Furthermore, a certain number of new measures to prevent and decrease the burr in drilling process are advanced.

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data.The traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be programmable.Each switch in SDN keeps track of forwarding information in a flow table.The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets.Due to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding capabilities.So,the SDN must handle traffic from across the whole network.The flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy consumption.Whenever the flow table is abused and overflowing,the usual regulations cannot be executed quickly.In this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low rate.This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)controller.The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and throughput.The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network.By putting the proposed flow method and checking whether a packet may move from point A to point B without breaking certain regulations,the evaluation tool examines every flow against a set of criteria.The FFNN with DT and K-means algorithms obtain accuracies of 96.29%and 97.51%,respectively,in the identification of collision flows,according to the experimental outcome when associated with existing methods from the literature.

Theoretical Study on Non-transmission High Efficient Parallel Camber Grinding Machine

Be directed against the development trend of modern CNC grinding machine towards high precision and high efficiency, some general weaknesses of existing camber grinding machine are analyzed in detail. In order to develop new type CNC camber grinding machine that can grind complex die, and genuinely achieved accurate feed and high efficient grinding, a new type camber grinding machine is put forward, called non-transmission virtual-shaft CNC camber grinding machine. Its feed system is a parallel mechanism that is directly driven by linear step motor. Therefore, traditional transmission types, such as the ball lead-screw mechanisms, the gears, the hydraulic transmission system, etc. are cancelled, and the feed system of new type CNC camber grinding machine can truly possess non-creep, good accuracy retentiveness a wide range of feed-speed change, high kinematical accuracy and positioning precision, etc. In order to realize that the cutting motion is provided with high grinding speed, step-less speed variation, high rotational accuracy, good dynamic performance, and non-transmission, the driving technology of hollow rotor motor is applied to drive the spindle of new type grinding machine,thus leading to the elimination of the transmission parts of cutting motion. The principle structure model of new type camber grinding machine is advanced. The selection, control gist and driving circuit line of the linear step motor are expounded. The main technology characteristics and application advantages of non-transmission virtual-shaft CNC camber grinding machine are introduced.

Feed Drive Based upon Linear Motor for Ultraprecision Turning Machine

The characteristics of several different linear motors have been investigated, and the feed drive system with linear motor instead of screw-nut mechanism has been built for a submicro ultraprecision turning machine. In the control system for the feed drive system arranged as "T", both P-position and PI-speed control loops are used. The feedback variable is obtained from a double frequecy laser interferometor. Experiments show that the feed drive with linear motor is simple in construction, and that its dynamics is better than others. So the machining accuracy of the workpiece machined has been successfully improved.

Analysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine LeAnalysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine Learningarning

The innovation and development in data science have an impact in all trades of life.The commercialization of sport has encouraged players,coaches,and other concerns to use technology to be in better position than r their opponents.In the past,the focus was on improved training techniques for better physical performance.These days,sports analytics identify the patterns in the performance and highlight strengths and weaknesses of potential players.Sports analytics not only predict the performance of players in the near future but it also performs predictive modeling for a particular behavior of a player in the past.The impact of a smart player on the success of a team is always a big question mark before the start of a match.The fans always want to know performance analysis of these superstar players and they always are interested to get to know more about their favorite player and they always have high hopes from their favorite player.Machine learning(ML)based techniques help in predicting the performance of an individual player as well as for the whole team.The statistics are very vital and useful for management,fans,and expert analysis.In our proposed framework,the adaptive back propagation neural network(ABPNN)model is used for the prediction of a player’s performance.The data is collected from football websites,and the results are stored in the cloud for fast fetching of data.They can be retrieved anywhere in the world through cloud storage.The results are computed with 94%accuracy and the performance of the smart player is formulated for the success of a team.

缝衣针包装机的主要结构设计研究

大部分缝衣针用铝箔纸、外包装纸进行包装,该包装方式主要采用纯手工操作,针对该情况,充分考量包装机的工作稳定性、自动化程度和生产效率,并注重细节的处理,设计出高效的缝衣针包装机,以满足安全、环保等方面的要求.

基于模糊PI全电动工业平缝缝纫机多电机同步控制方法研究

目前工业平缝缝纫机主要以机械式和半自动化为主,效率低,可靠性和稳定性差,自动化程度不高。针对这一现状,提出一种引入模糊PI补偿的工业平缝缝纫机三电机同步控制算法,用偏差耦合式控制三电机同步转动,同时引入PID补偿和模糊控制融合算法消除系统同步偏差,很好解决了自动化多电机同步问题。实验结果表明,基于模糊PI补偿的偏差耦合同步控制算法可以很好改善系统的同步性能,提高系统的同步精度。

包覆面料裁片切割机横梁进给系统建模及动态特性分析

包覆面料裁片切割机是用于制作软体家具的主要设备之一,其横梁进给系统存在复杂的机电耦合作用。为在设计阶段研究其横梁进给系统切割定位工况下的动态特性,优化横梁进给伺服驱动控制系统参数,建立了横梁进给系统的机电-刚柔混合模型。拟定横梁进给系统的响应效率、定位精度和刀尖运动稳定性作为动态特性评价指标,探究了横梁进给伺服驱动控制系统参数对横梁进给系统动态特性的影响。结果表明:建立的横梁进给系统机电-刚柔混合模型,为横梁进给系统动态特性分析奠定了基础。增大位置环比例系数可提高横梁进给系统的响应效率和定位精度,增大速度环比例系数可有效提高横梁进给系统的刀尖运动稳定性。研究结果可为提高软体家具生产效率和制造质量提供技术保障。

基于改进遗传算法的饲料配方多目标优化研究

为提高动物饲料配方的营养,降低饲料成本,在保证饲料营养的前提下,构建最小成本的目标函数,并采用改进遗传算法对目标函数的最优配方进行求解。仿真实验表明,以20~50 kg的生猪为实验范例,分别采用标准遗传算法和改进遗传算法求解100 kg生猪饲料配方,并将两种方法求解得到的最终饲料配方进行对比。实验结果表明:通过标准遗传算法求解获得的100 kg生猪饲料配方的总成本为307.91元,而改进的遗传算法求解获取的100 kg生猪饲料配方的总成本为291.03元,比传统遗传算法获取的配方减少了16.88元;标准遗传算法在经过650次迭代后陷入了局部最优解,而改进遗传算法在经过700次迭代后变化曲线趋于平稳,输出全局最优解291.03元。由此得出,改进遗传算法的全局寻优能力更强,可快速、有效求解动物饲料最优配方。

生物质成型技术及LH-600型生物质颗粒成型机设计

针对大型养殖场固体粪污、生物质秸秆、茶园茶梗、果园枝条、木屑及蘑菇渣等农业固体废弃物资源化利用过程中,生产生物质颗粒存在产量过低、设备故障率高、易堵塞模孔和成型率差等问题,研发设计了LH-600型生物质制粒成型机。对成型机喂料装置、颗粒成型装置、动力传动机构进行了创新设计,并利用Solidworks软件进行辅助设计。试验、工程实践应用表明,成型机具有使用寿命长、维护成本低、传动机构润滑降温效果好及高效节能等优点,适合大中型饲料加工厂、养殖场及有机肥厂使用。

数控皮革裁剪机伺服进给三闭环控制系统PID控制研究

过窗裁剪是皮革机加工过程中的一大特点。本文针对皮革机加工过程中因存在大量过窗裁剪而带来的进给系统控制精度过高、外界工作环境干扰大等问题,构建了一种数控皮革裁剪机伺服进给三闭环控制方案,对该方案的永磁同步电机数学模型、PID控制模型、PID参数整定方案等进行分析,为皮革机加工工艺提升提供借鉴。

粉末饮片自动包装机的研究与设计

目前市场上针对粉末饮片、颗粒等物料包装时,存在难以有效剔除不合格原料、存有破碎的细小残渣和产品结块受潮等现象,严重影响产品的包装质量。团队结合多年实践经验,设计了一种粉末饮片自动包装机,集上料机构、送料机构、送风机构、收集组件及驱动机构等于一体,可有效解决以上痛点问题。具体针对该装置的工作原理和各个组成部分的结构设计进行了介绍。实践证明,该装置在工作时,各个组成部件相互配合,能够有效剔除不合格的原料、避免原料结块受潮,具有结构简单、成本低、效率高等优点。

基于Bootstrap-C_(pu)的机床进给机构装配过程能力评估方法

机床进给机构装配过程是其装配质量形成的过程,由于受5M1E因素影响,进给机构装配过程能力可能不足,会导致进给机构装配质量出现较大波动,甚至出现不合格产品。为此,基于C_(pu)和Bootstrap理论,构建了基于Bootstrap-C_(pu)的进给机构装配过程能力评估方法,并进行了实际应用。结果表明:在95%置信度条件下,该批机床进给机构装配过程能力是足够的,其装配质量波动较小。

基于SO-ELM的数控机床进给系统热误差分析

为了对数控机床进给系统热误差进行更加精确的预测,提出一种基于蛇优化(SO)算法和极限学习机(ELM)的数控机床进给系统热误差预测模型SO-ELM。利用模糊c均值聚类(FCM)和灰色关联度分析(GRA)筛选出进给系统的关键测温点;通过蛇优化算法优化极限学习机的输入层权重和隐藏层偏置,利用关键温度测点的温升数据和热误差数据构建SO-ELM热误差预测模型。为验证模型的准确性和适用性,与基于SSA-BP和LSMT的热误差预测模型进行对比分析,结果表明SO-ELM模型预测结果的均方根误差和决定系数均优于SSA-BP和LSTM模型,能够更精准地对机床进给系统热误差进行预测,为机床热误差预测补偿提供一种新的思路。

包衣柠条种子气力喷播机供种装置仿真与试验

为解决目前喷播机供种装置不适应包衣种子的问题,结合物理与仿真试验的方法,以喷播包衣柠条种子为对象,以降低排种破碎率、提高排种均匀性为目标,设计了一种4BQD-40型喷播机螺旋式供种装置。首先,根据喷播机已确定的电机参数、最大喷播量和种子休止角度等对供种装置进行参数计算及结构设计;然后,建立3组不同参数配置的供种装置离散元仿真模型并进行排种过程仿真,根据其排种过程中种子的受力情况及排种连续性情况得到螺旋叶片直径80mm、螺旋叶片螺距60mm、螺旋轴直径28mm、排种口直径60mm为最优参数配比。在相同的工作要求下,对螺旋式与槽轮式供种装置排种过程进行离散元仿真,在相同供种量组条件下进行二者的破碎率测定,结果表明:原有槽轮式供种装置5种供种量下的破碎率依次为17.68%、14.90%、12.88%、12.45%、11.78%;相同供种量下,螺旋式供种装置的破碎率依次为2.11%、2.65%、2.91%、3.01%、3.37%,表明包衣柠条种子螺旋式供种装置排种破碎率明显降低。

油管外接管生产线送料切断控制系统设计

针对油管外接管生产过程中切断精确度低、废料率高,各设备相互独立及自动化程度较低等问题,在传统钢管切割设备的基础上优化设计了精确送料单元、锁紧切割单元、自动下料单元。基于S7-1500 PLC进行了油管外接管自动生产线控制系统的硬件及软件设计,利用TIA Portal设计控制逻辑,采用西门子KTP1200人机交互面板对生产线工作状态进行实时监控,实现对整机的协调控制。实验测试表明,所设计的油管外接管自动生产线自动化程度高、运行稳定,显著提高了油管外接管的生产效率与自动化程度。

印花面料的边缘轮廓快速提取方法

工业协作型缝纫机器人代替操作工自动完成缝合是未来发展的必然趋势,但当前的工业协作型缝纫机器人难以快速精准地定位面料边缘轮廓信息,影响缝纫效率与缝纫质量。针对印花面料提出线下使用深度学习建立面料检测模型,线上调用面料检测模型分割面料与背景并结合传统轮廓检测算法快速准确提取面料边缘轮廓的方法。首先,建立面料图像数据集,并通过卷积拆分和融合损失函数对VGG-UNet模型进行优化,将面料数据集输入至优化的VGG-UNet模型进行训练学习并构建最优面料检测模型;其次,利用最优面料检测模型分割面料与背景;然后,采用数学形态学算法对分割后的面料图像进行自适应开运算去除面料边缘的毛边;最后,利用Canny算子对去除毛边后的面料图像进行轮廓提取。实验结果表明,本文方法可较好去除面料毛边并快速精准提取印花面料的边缘轮廓,所提取的轮廓与面料边缘轮廓高度拟合,轮廓提取精度高于99%,轮廓提取时间仅需0.216 s。本文研究可为后续机器人的轨迹规划提供快速准确的坐标信息,提高缝合质量和效率,推进无人化、自动化缝合生产线的实现。