数控龙门铣床主轴磨削工艺

数控龙门铣床主轴的加工精度,对机床精度标准中几项主要精度有着直接的影响,鉴于其结构和精度要求,在加工过程中对定位基准面的选择、深孔加工和热处理变形等方面,应给予足够的重视。通过对数控龙门铣床主轴XK2127/22310技术条件的分析和工艺过程的讨     

FANUC用户宏程序的零点自动偏移问题研究

针对FANUC数控铣床系统中G92指令只能在当前机床对话框中有效的局限性,本文利用宏变量允许变量号进行数学运算的特点,开发一款利用用户宏程序进行零点自动偏移的程序。在龙门加工中心GLU23X30上验证表明,该用户宏程序可通过处理存储在系统变量中的值完成零点偏移,并可用于包括当前坐标系在内的所有坐标系。通过改变参数设定,也可将该用户宏程序从数控铣床系统移植到数控车床系统中使用,减少了占机时间,提高了加工效率。     

大型数控机床的管理与使用

目前，国内许多企业为了增强生产应变能力，引进了价值数千万元的龙门或落地式大型数控镗铣床，但由于各种原因，这些机床使用情况不尽人意。投资大、效益低这一矛盾比较突出。我厂在使用价值3000万元的龙门式五面加工中心SL242的过程中，总结出了一些成功的经验。     

有限元分析在数控铣床热变形方面的研究

在多种热源的作用下,数控铣床产生热变形,影响工件与刀具间的相对位移,造成加工误差,从而影响零件的加工精度,因此减小热误差对提高机床的加工精度至关重要。控制机床热误差涉及到如何查找敏感点,然而找出机床敏感点是个非常棘手的问题。本文在对数控铣床热边界条件进行分析的基础上,应用有限元分析软件ANSYS,对ZK7640数控铣床进行整机热特性分析,为机床敏感点的查找提供依据,对数控机床热误差进行了定量计算,并通过实验检测验证其正确性。     

基于LabVIEW的数控磨床振动分析

数控磨削加工过程中容易产生振动,在工件表面形成振动波纹及形状误差,进而恶化加工质量,加剧砂轮磨损,降低生产效率,同时振动对机床的性能和使用寿命有一定危害。因此,研究磨削加工过程中产生的振动信号具有重要意义。本文选用LabVIEW作为开发环境,开发了一套数控磨床振动监控系统,实现了数控磨床振动信号的数据采集、分析、处理以及存储,并在数控磨削加工现场进行测试,验证了软件的可靠性和实用性。     

数控交叉补偿功能在龙门镗铣床上的应用

介绍数控系统交叉补偿功能在提高数控龙门镗铣床横梁定位精度方面的应用。     

FANUC 0i系统与SINUMERIK 802D系统的编程指令分析及应用

FANUC Oi系统和SINUMERIK802D系统都是目前国内最流行的机床控制系统,它们都适用于数控车床、数控铣床、加工中心和专用数控机床等.该文作者主要列举了在加工中心上应用FANUC Oi系统和SINUMERIK802D系统在编程指令、编程方法上的不同之处,目的是供机床操作编程人员参考与借鉴.     

基于SIMATIC NET的数控磨床车间SCADA系统设计

针对公司对数控磨床车间的掌控力度不足问题,提出了基于西门子通信技术的数控磨床车间数据采集与监控系统,通过SIMATIC NET工业以太网,实现SIMATIC NET OPC 服务器与西门子数控系统中的S7系列PLC进行数据交换,进而利用SIMATIC NET所提供的通信模块把数据提供给SIMATIC NET OPC客户端。在数控磨床车间SCADA系统中,采用WinCC作为SIMATIC NET OPC的客户端,同时开发了基于VB高级语言的SIMATIC NET OPC客户端,以增加SCADA系统的功能。基于SIMATIC NET的SCADA系统实现了对数控车间的数据采集与设备监控,为公司下阶段的信息化建设奠定了基础。     

试验模态技术在机床结构动态特性研究中的应用

试验模态分析是研究机械动态特性的重要方法之一。为提高数控机床的动力学性能,阐述了试验模态分析技术在数控机床结构动态特性研究中的应用。基于模态分析理论和模态测试技术,结合LabVIEW和Madab软件开发了一套机床模态分析系统。并以一台数控铣床主轴部件为实例,给出了测试结果,验证了系统的各项功能。     

三菱变频系统在大型龙门刨床中的应用设计

针对传统的大型龙门刨床调速系统存在的问题,提出了变频调速系统。介绍以CC-Link为控制网络,MOVICON X为监控软件,三菱PLC为控制核心的龙门刨床变频调速监控系统。结合龙门刨床的现场数据采集、工作周期、数据传输机制及变频节能等多方面因素,给出具体实现方法。所开发的系统不仅实现了生产过程中信息的采集处理和监控,而且提供了故障诊断与报警,自动生成错误报表,实现了系统的实时监控,使系统高效节能地运行。     

基于CRIO的数控机械在线监测诊断系统设计

目前提出的数控机械在线监测诊断系统温度采集准确度较低,导致诊断过程稳定性及准确性较差,为此基于CRIO设计了一种新的数控机械在线监测诊断系统,以期更好地分析数控机械的运行状态;通过数控机床在线监测模块、远程设备管理模块及故障诊断模块组成系统总结构,硬件设备包括NTI CRIO驱动器、新型伺服电动机、采集卡、传感器以及PC上位机,设计多参数在线监测模块、网络控制模块、设备管理模块和故障诊断模块;在采集信号中提取数控机械运行状态数据,通过格拉布斯准则估算标准偏差,去掉误差数据后,输出有效数据,完成数据的采集及存储,实现数控机械在线监测诊断系统设计;实验结果表明,基于CRIO的数控机械在线监测诊断系统的温度采集准确率为99%,振动信号采集准确率为95%,可以有效提高采集精准度,增强数控机械在线监测诊断系统的准确性及稳定性。     

A Unified Manufacturing Resource Model for representing CNC machining systems

Standard information representation in the manufacturing business has always focused on products with information relating to their design, geometry and required processes. However there is no standardized information model available to represent the manufacturing equipment used to produce such products. This equipment contributes as a significant investment in setting up a CNC manufacturing facility and is considered as a major asset of the business. Thus the authors see a need for an effective model to represent such equipment particularly the most expensive assets namely CNC machining systems. Such a model could represent the machine resource and also its additional auxiliary devices such as workpiece and cutting tool changing mechanisms, bar feeders, pallet/gantry systems and robotic arms. These devices can function with a CNC only when the controller's resource data model can represent their functionality. Currently every machine tool and auxiliary device manufacturer promotes their own resource data models; resulting in a plethora of representation methods which are bespoke and incomplete in terms of representing the entire system's resources. This paper proposes a Unified Manufacturing Resource Model termed UMRM. UMRM not only has the novel capability to provide the information to define the various elements of the CNC machining system, but also has the added capability to provide support for automation of process planning decision making.     

Web-based virtual operating of CNC milling machine tools

This paper attempts to propose a virtual operating system applied to operation training of manufacturing facility and manufacturing process simulation. The system is based on VRML and browser/server structure, so user only needs to install a free plug-in, and run the package normally via Microsoft Internet Explorer. Initially this paper studies the system framework, structure models and concept models. Then, a communication approach based on VRML, Java and HTML, which is key to realize the virtual operating of CNC machines, has been presented. The algorithm of material removed simulation based on VRML Z-map is also presented in this paper. It has the advantages such as a lower memory requirement, and a faster computation speed. Finally, in order to validate the feasibility of the proposed approach, the CNC milling machine has been taken as an illustrative example for the prototype development.     

Three tiered web-based manufacturing system—Part 1: System development

This paper provides a demonstration of harnessing the power of the Internet to enable collaborative manufacturing in such a setup where the key components such as the human resource personnel and the manufacturing equipment are geographically separated. As part of the framework a platform independent and cost-effective means of remotely operating a 3-axis computer numerical control (CNC) drilling machine securely via the Internet has been developed. In addition, an infrastructure to allow remote manufacturers to collaborate on a product design as well to monitor the drilling process in real time is presented. The framework rests on sound security foundation implemented using technologies such as firewall, authentication modules and transport layer security (TLS) protocol to promote safe usage of the drilling machine. It is worth mentioning that the architectural design is generic and modular in nature and as such, any other genre of CNC machine such as milling and turning could be substituted in place of drilling with only machine-specific changes to the existing system.     

基于Attention-LSTM的CNC刀具破损在线检测系统

为了在数控（CNC）机床批量加工过程中对刀具破损进行检测以减少残次产品,提出一种利用机床主轴功率信息,基于Attention-LSTM的CNC生产线刀具破损在线监测方法。该方法以数控系统内置传感器作为数据源获取机床主轴功率时间序列,在数据采集环节中,需要分辨加工过程中的不同工序以及该工序所使用的刀具编号。因此,在数据采集环节中,同时对数控代码和主轴功率进行采集,使用数控代码解析方式对采集数据进行处理,完成加工过程的工序识别,再使用Attention-LSTM算法对主轴功率数据进行预测,然后利用DTW算法计算时间序列相似度。加工过程功率时间序列和标准时间序列之间的相似程度应当处于合理的阈值范围内,否则认为此次加工过程中发生刀具破损。以FANUC数控系统为平台进行实验,验证了刀具破损识别的准确率。     

Design of a decision support system for machine tool selection based on machine characteristics and performance tests

Economic globalization, together with heightened market competition and increasingly short product life cycles are motivating companies to use advanced manufacturing technologies. Use of high speed machining is increasingly widespread; however, as the technology is relatively new, it lacks a deep-rooted knowledge base which would facilitate implementation. One of the most frequent problems facing companies wishing to adopt this technology is selecting the most appropriate machine tool for the product in question and own enterprise characteristics. This paper presents a decision support system for high speed milling machine tool selection based on machine characteristics and performance tests. Profile machining tests are designed and conducted in participating machining centers. The decision support system is based on product dimension accuracy, process parameters such as feed rate and interpolation scheme used by CNC and machine characteristics such as machine accuracy and cost. Experimental data for process error and cycle operation time are obtained from profile machining tests with different geometrical feature zones that are often used in manufacturing of discrete parts or die/moulds. All those input parameters have direct impact on productivity and manufacturing cost. Artificial neural network models are utilized for decision support system with reasonable prediction capability.     

Force based tool wear monitoring system for milling process based on relevance vector machine

The monitoring of tool wear status is paramount for guaranteeing the workpiece quality and improving the manufacturing efficiency. In some cases, classifier based on small training samples is preferred because of the complex tool wear process and time consuming samples collection process. In this paper, a tool wear monitoring system based on relevance vector machine (RVM) classifier is constructed to realize multi categories classification of tool wear status during milling process. As a Bayesian algorithm alternative to the support vector machine (SVM), RVM has stronger generalization ability under small training samples. Moreover, RVM classifier results in fewer relevance vectors (RVs) compared with SVM classifier. Hence, it can be carried out much faster compared to the SVM. To show the advantages of the RVM classifier, milling experiment of Titanium alloy was carried out and the multi categories classification of tool wear status under different numbers of training samples and test samples are realized by using SVM and RVM classifier respectively. The comparison of SVM with RVM shows that the RVM can get more accurate results under different number of small training samples. Moreover, the speed of classification is faster than SVM. This method casts some new lights on the industrial environment of the tool condition monitoring.     

A PNN self-learning tool breakage detection system in end milling operations

With the advance of technology over the years, computer numerical control (CNC) has been utilized in end milling operations in many industries such as the automotive and aerospace industry. As a result, the need for end milling operations has increased, and the enhancement of CNC end milling technology has also become an issue for automation industry. There have been a considerable number of researches on the capability of CNC machines to detect the tool condition. A traditional tool detection system lacks the ability of self-learning. Once the decision-making system has been built, it cannot be modified. If error detection occurs during the detection process, the system cannot be adjusted.To overcome these shortcomings, a probabilistic neural network (PNN) approach for decision-making analysis of a tool breakage detection system is proposed in this study. The fast learning characteristic of a PNN is utilized to develop a real-time high accurate self-learning tool breakage detection system. Once an error occurs during the machining process, the new error data set is sent back to the PNN decision-making model to re-train the network structure, and a new self-learning tool breakage detection system is reconstructed. Through a self-learning process, the result shows the system can 100% monitor the tool condition. The detection capability of this adjustable tool detection system is enhanced as sampling data increases and eventually the goal of a smart CNC machine is achieved.     

A method of NC machine tools intelligent monitoring system in smart factories

The construction of effectual connection to bridge the gap between physical machine tools and upper software applications is one of the inherent requirements for smart factories. The difficulties in this issue lies in the lack of effective and appropriate means for real-time data acquisition, storage and processing in monitoring and the post workflows. The rapid advancements in Internet of things (IoT) and information technology have made it possible for the realization of this scheme, which have become an important module of the concepts such as “Industry 4.0”, etc. In this paper, a framework of bi-directional data and control flows between various machine tools and upper-level software system is proposed, within which several key stumbling blocks are presented, and corresponding solutions are subsequently deeply investigated and analyzed. Through monitoring manufacturing big data, potential essential information are extracted, providing useful guides for practical production and enterprise decision-making. Based on the integrated model, an NC machine tool intelligent monitoring and data processing system in smart factories is developed. Typical machine tools, such as Siemens series, are the main objects for investigation. The system validates the concept and performs well in the complex manufacturing environment, which will be a beneficial attempt and gain its value in smart factories.     

基于dyna971的数控镗铣机床动态模拟仿真

落地式数控镗铣机床结构复杂,为保证机床具有良好的动态性能,在开发设计该类机床时要进行整机的动态模拟仿真分析。针对这一要求,提出一种实际设计中实用化程度较高的机床动态模拟仿真思路,首先对数控落地镗铣机床样机进行模态试验,获取床身〖CD*2〗滑座、滑座〖CD*2〗立柱、立柱〖CD*2〗主轴箱等结合面的模态参数,在对模态参数进行有限元方法优化识别后,利用COMBIN14单元模拟优化后的结合部参数建立机床整机有限元模型。在dyna971软件平台对整机有限元模型进行动态模拟仿真,从模拟仿真分析结果可以看出机床在受到外界作用力时,其输出的应力和应变变化波形平稳,说明经过上述分析而设计出的机床具有良好的动态性能。目前,该研究成果已应用于此类型机床的批量生产。