Development of a STEP-NC – Enabled Interoperable and Open Architecture CNC Controller

In this paper are proposed two new approaches to maintenance strategies for Computer Numerical Control (CNC) machine tools. The analysis is done for different families of CNC machine tools from S.C. Elmet Bacau, a company specialized in aviation. In maintenance actions applied to CNC machine tools is very important to know the evolution of defects and critical state of electrical and mechanical components. The results of this analysis concludes that maintenance actions can be judged by the developing time period diagram, between failure appearance and interruptions in operation. It is also analyzed the financial impact, revealed from known maintenance strategies adopted on CNC machine tools, resulting in a positive approach of condition based maintenance.

Multiaxis machining (e.g., milling) of large aerospace structures is typically carried out using large monument scale expensive computer numerical control (CNC) machine tools. In contrast, multiple degree-of-freedom-articulated robotic arm-based machining has several potential advantages over CNC machine tools, owing to its flexibility (reconfigurability) and lower cost. However, its application is limited by its much lower stiffness and accuracy compared with a CNC machine tool. This paper presents a new method that combines wireless force sensing with a mechanistic model of the milling process to increase the accuracy of robotic milling while preserving its flexibility. Articulated-arm robotic milling experiments are conducted to validate the concept. Experimental results show significant improvement (> 70%) in the dimensional accuracy of simple geometric features machined using the new method.

High-speed and high-accuracy control technology has been developed in Japan and applied to CNC (Computer Numerical Control) machine tools. Therefore, a CNC machine tool, which is made in Japan, has shown a successful history of its performance. Further development of these technologies is important for Japan to maintain its leadership in advanced manufacturing. The purpose of this research is to develop a high-speed and high-accuracy machining system. Two methods are implemented in the proposed prototype system. The first method is “geometric-model interpolation,” which is used to generate high-speed and highaccuracy command positions, and the second method is “predictive compensation,” which is used to correct machining errors. In the proposed prototype system, these errors are simulated preliminarily and compensated for in the servo data by controlling the position and the feed rate. This report describes the configuration of the proposed prototype system, and the performance of the prototype is evaluated by comparing its speed and accuracy with an existing commercial CNC machine tool. The results show that the prototype system is able to control a CNC machine tool with higher speed and greater accuracy than current CNC machine tool systems.

Thermal error caused by the thermal deformation of computer numerical control (CNC) machine tools is one of the main factors to affect the machining accuracy. With monitoring data of the temperature field, establishing data-driven thermal error model is considered as a more convenient, effective and cost-efficient way to reduce the thermal error. As a matter of fact, it is very difficult to develop a thermal error model with perfect generalization adapting to different working conditions of machining tools. In this paper, a method of an ensemble modeling (EM) based on Convolution Neural Network (CNN) and Back Propagation (BP) Neural Network for modeling thermal error is presented. This ensemble model takes full advantages of two different neural networks, namely CNN having self-extracting feature to solve collinear problem in temperature field and BP can process heat source to thermal error by mapping nonlinear function, then combined into a EM. To demonstrate the effectiveness of the proposed model, an experiment platform was set up based on a heavy-duty CNC machine tool. The results show that the proposed model achieves better accuracy and strong robustness in comparison with only with BP network and CNN network respectively.

The paper presents a new approach to the modelling and simulation of a computer numerical control (CNC) machine tool feed drive. The hybrid model of the drive incorporates a distributed load, explicit damping factors and measured non-linear effects in order to achieve a realistic dynamic performance. In this way, the shortcomings of traditional modelling methods applied to CNC machine tool axis drives are overcome. A MATLAB/SIMULINK package was used to simulate this new model for a CNC machine tool feed drive. A novel hybrid model with a distributed load, explicit damping factors, backlash and friction was developed and shown to have a similar dynamic response to the machine tool feed drive system under the same conditions. The simulated results display resonance frequencies, while the lumped-parameter models generate only the response of a second-order element.

Method for generating CNC programs based on block-processing time to improve speed and accuracy of machining curved shapes

Many techniques for the acceleration and deceleration of industrial robots and computer numerical control (CNC) machine tools have been proposed in order to make industrial robots and CNC machine tools perform given tasks efficiently. Although the techniques selecting polynomial functions can generate various acceleration and deceleration characteristics, the major problem is the computational load. The digital convolution techniques are more efficient than the techniques selecting polynomial functions. However, neither velocity profiles of which the deceleration characteristics is independent from the acceleration characteristics nor those of which the acceleration interval is different from the deceleration interval can be generated by the digital convolution techniques. This paper proposes a generalized approach for generating velocity profiles that cannot be generated by the digital convolution techniques. According to the desired characteristics of acceleration and deceleration, each set of coefficients is calculated and is stored. Given a moving distance, and acceleration and deceleration intervals, a velocity profile having the desired characteristics of acceleration and deceleration can be efficiently generated by using these coefficients. Several velocity profiles generated by the proposed technique are applied to one single-axis control system.

Previous techniques for accelerating and decelerating industrial robots and computer numerical control (CNC) machine tools either cannot be computed in real-time, fail to generate various motion profiles, or cannot fully utilise the maximum values of velocity and acceleration. An efficient technique is proposed that has been designed to generate various motion profiles that fully utilise the maximum values of velocity and acceleration.

From the middle of the 20th century, CNC(Computer numerical control) machine tool has brought a revolutionary change to the mechanical manufacturing industry. CNC machine tool is a kind of highly mechatronics product. It has the Characteristic of high precision and high productivity. Therefore it develops quite fast and improves various kind of the quality of the products. It speeds up the development of machinery industry. In order to meet the requirements of the processing accuracy and speed of modern society, the structure and components of the machine have also become complicated. In order to protect the safety of operators and reduce time of machine repaired as well as improve efficiency, the requirements of the reliability of CNC machine tool should be greatly improved. This study will briefly introduce the concept of reliability and put forward some measures to improve the reliability.

Nowadays, computer numerical control (CNC) machine tool undertakes more processing tasks than other common machine tools because of its highly automated machining ability and high performance. However, due to the lack of intelligence in machining process planning, machining procedure of products mostly depends on process planners rather than CNC machine tools. To make product quality less dependable on process planner’s ability and improve the efficiency of process planning in order to fulfill changeable market, this paper presents an approach to design and develop CNC machining process knowledge base using cloud technology. The general standard STEP-NC is mapped to web ontology language (OWL) to describe machining process-related knowledge in a readable and comprehensible way. This mapping relation also makes knowledge suitable for storage in HBase. Through this ontology model, descriptive and logical knowledge can be collected. Hadoop platform is used in this approach to provide the NoSQL database HBase for large-scale knowledge storage and MapReduce programming model for large-scale knowledge processing. Taking advantage of MapReduce, knowledge query engine and reasoning engine can be developed. Users can submit task and resource descriptive files to the cloud through CNC controller and get machining process solutions from knowledge base. Evaluation mechanism is also adopted to filter low-quality knoweldge.

In order to guarantee the preciseness and scientific nature of Computerized Numerical Control (CNC) machine tools appearance design, appearance design evaluation has to be made on CNC machine tools so as to change the randomness and uncertainty existed in traditional appearance design, and to establish a scientific method for CNC machine tools appearance design that meets the modern aesthetic needs. The evaluation index of hierarchical structure has been set up from four aspects as stylization, form-orientation, colorization and agreeableness that may influence the appearance design, adopting both qualitative and quantitative methods; weight for each evaluation index has been determined in accordance with Analytic Hierarchy Process ; and a two-level fuzzy synthetic evaluation model for CNC machine tools appearance design evaluation has been made and a comprehensive evaluation has been conducted through fuzzy mathematical method. By analyzing the appearance design of 2MKM95 series CNC precision vertical universal machine tools determined by Tianjin No.2 Machine Tool Corporation, this paper illustrates the preciseness and scientific nature of fuzzy synthetic evaluation model.

To address the problems of the low accuracy of geometric error identification and incomplete identification results of the linear axis detection of computer numerical control (CNC) machine tools, a new 21-item geometric error identification method based on double ball-bar measurement was proposed. The model between the double ball-bar reading and the geometric error term in each plane was obtained according to the three-plane arc trajectory measurement. The mathematical model of geometric error components of CNC machine tools is established, and the error fitting coefficients are solved through the beetle antennae search particle swarm optimization (BAS–PSO) algorithm, in which 21 geometric errors, including roll angle errors, were identified. Experiments were performed to compare the optimization effect of the BAS–PSO and PSO and BAS and genetic particle swarm optimization (GA–PSO) algorithms. Experimental results show that the PSO algorithm is trapped in the local optimum, and the BAS–PSO is superior to the other three algorithms in terms of convergence speed and stability, has higher identification accuracy, has better optimization performance, and is suitable for identifying the geometric error coefficient of CNC machine tools. The accuracy and validity of the identification results are verified by the comparison with the results of the individual geometric errors detected through laser interferometer experiments. The identification accuracy of the double ball-bar is below 2.7 µm. The proposed identification method is inexpensive, has a short processing time, is easy to operate, and possesses a reference value for the identification and compensation of the linear axes of machine tools.

With the increasingly common use of industrial automation for mass production, there are many computer numerical control (CNC) machine tools that require the collection of data from intelligent sensors in order to analyze their processing quality. In general, for high speed rotating machines, an accelerometer can be attached on the spindle to collect the data from the detected vibration of the CNC. However, due to their cost, accelerometers have not been widely adopted for use with typical CNC machine tools. This study sought to develop an embedded miniature MEMS microphone array system (Radius 5.25 cm, 8 channels) to discover the vibration source of the CNC from spatial phase array processing. The proposed method utilizes voice activity detection (VAD) to distinguish between the presence and absence of abnormal noise in the pre-stage, and utilizes the traditional direction of arrival method (DOA) via multiple signal classification (MUSIC) to isolate the spatial orientation of the noise source in post-processing. In the numerical simulation, the non-interfering noise source location is calibrated in the anechoic chamber, and is tested with real milling processing in the milling machine. As this results in a high background noise level, the vibration sound source is more accurate in the presented energy gradation graphs as compared to the traditional MUSIC method.

The positioning accuracy of computer numerical control (CNC) machine tools is mainly limited by the manufacturing accuracy of their linear and circular motion axes and by the long-term dimensional stability of their structures. Maximizing this accuracy can prove to be a particularly challenging task, especially for large-sized systems. In fact, heat-induced deformations, long-period deformation of foundations and the manufacturing process itself, these all cause time-dependent structural deformations of the machine body, which are difficult to model and to predict. The usual approach is a model-based prediction of structural deformations, which is followed by a compensation of positioning errors at CNC level. This approach is often limited by the complexity of the problem from both geometrical (system geometry can be very complex and it can vary in time) and physical (it is difficult to model and consider any possible load type and loading condition) point of view. As a consequence, only limited success has been achieved in active error compensation based on the modelling of the relationship between the generalized dynamic loads and the structural deformation field. This paper illustrates a different approach in active error compensation, which exploits a new measurement system able to provide real-time measurement of the displacement field of a given structural component, without any model about its dynamic/thermal structural behavior.

Contour error, primarily induced by the coupling of multi-axis dynamics and servo lag, is a critical factor limiting the machining accuracy of computer numerical control (CNC) machine tools during complex surface machining. Traditional theoretical modeling methods based on dynamics and parameter identification are difficult to cope with complex nonlinear errors. To address the challenges, this study proposes a data-driven deep learning method to achieve high-precision prediction of contour errors. In this paper, the VMC2100B vertical machining center is used as the experimental platform, and the high-speed dynamic coordinate data acquisition research is carried out with the help of dynamic coordinate acquisition technology, which provides strong data support for the training of deep learning model. A hybrid deep learning prediction model named CLA-Net (CNN-LSTMAttention Network) was constructed: 1D Convolutional Neural Networks (1D-CNN) were employed to extract local spatial features, Long Short-Term Memory networks (LSTM) were used to learn long-term temporal dependencies, and an Attention was introduced to dynamically focus on critical historical information. The experimental results show that the proposed CLA-Net model can control the mean square error (MSE) at about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 ~ \mu m}$</tex>, which verifies the effectiveness and superiority of the model in the prediction task. This study provides a new solution for the contour error prediction task of five-axis machine tools.