High-precision modeling of CNCs’ spatial errors based on screw theory

A method for establishing machine tool’s spatial error model is put forward based on screw theory, which is different from the traditional error modeling method. By analyzing the position relationship between the ideal coordinate vector and the actual coordinate vector jointly affected by linear errors and angular errors, a single-axis screw conversion matrix error expression is brought up based on screw theory. Meanwhile, the comprehensive spatial error model of the CNC machine tool is derived by considering the influence of the workpiece motion chain and the tool motion chain on the model. Further, to compensating spatial errors of CNCs, such screw theory-based model is embedded in the error compensation system by means of integration of a few specific application examples. And in order to evaluate the compensation effects, an integrated evaluation method of quantitative spatial diagonal calculation and MATLAB simulation is proposed. Application results show that the screw theory-based spatial error model of tool has a very substantial compensation effect, which makes the position error of the machine tool decreased by about 80%.

Design and Development of IIoT Dashboard for Overall Equipment Effectiveness Monitoring of CNC Machine Tools

Smart Manufacturing systems are regarded as the fourth revolution in the manufacturing industry, which is shaped by widespread deployment of sensors and Internet of Things. The present work constitutes of ‘Development of Industrial Internet of Things (IIoT) Dashboard for ‘Overall Equipment Effectiveness’ (OEE) Monitoring of CNC Machine Tools’ for a legacy CNC machine which is converted to smart machine. Data fetched from the CNC controllers through OPCUA is sent to the connected cloud database which will be imported into PowerBI desktop and the data has been classified and processed according to the requirement to develop a data modelling architecture of OEE, the Working status of the machine is visualized by Creating Monitoring and Performance charts and graphs of different design in Microsoft PowerBI Desktop. The Advanced visualizations constitutes od various features along with different analysing capabilities that results is creating reports which enumerates the state of OEE as a Key Performance Indicator (KPI). As Microsoft Power BI pertains a set of pre-established steps for data processing, the situation designated may constitute a limitation to automatic data refresh, leading to a do-over to verify, the specific interval of time, the conformity of data so they can be imported into the system. Keywords: Industrial Internet of Things (IIoT), Open Platform Communications United Architecture (OPCUA), Computer Numerical Control (CNC), Overall Equipment Effectiveness (OEE), Key Performance Indicator (KPI).

Compilation of Drilling Load Spectrum Based on the Characteristics of Drilling Force

In order to overcome the problem that the existing methods of compiling load spectrum of spindle or machine tool mainly aim at the cutting force spectrum, torque spectrum and speed spectrum respectively, which ignore the connection between each spectrum, in this paper, a method for compiling drilling load spectrum of motorized spindle in CNC machine tool based on the characteristics of drilling force is proposed. Firstly, drilling tests under different processing technologies are carried out to measure its load, and the correction coefficient in the empirical formula of drilling force is obtained through fitting the measured drilling force, which makes the calculation of the axial force and torque more reasonable. Secondly, compared with the extended factor method, the transcendental probability method is optimized to solve the ultimate load of the axial force. Then, after setting the axial force as the main load of drilling, an eight-stage load spectrum for the main load is compiled. Finally, according to the relationship between the axial force and other loads, the eight-stage loading spectrum is transformed into a multi-dimensional drilling load spectrum.

Digital holographic camera with extended stochastic illumination for non-destructive inspection of silicon optics

In present work, we propose a compact digital holographic camera with extended stochastic illumination for full-field non-destructive inspection of silicon optics fabricated in computerized numerical control (CNC) machine. The developed technique overcomes the limitation of digital holography imparted by definite size of active area of the recording sensor to image a specular surface. The original aspect of this research work is to develop a system that enables reconstruction and testing of specular surface. For this a dual diffuser configuration is incorporated in a compact digital holographic camera developed for non-destructive testing applications. The generation of stochastic illumination beam using the diffusers is explained by simulating propagation of a light beam through random phase function of scattering medium. The stochastic optical field produced by the combination of diffusers in the digital holographic camera makes the camera suitable for non-destructive testing of specular surface of silicon optics. The effect of number of diffusers, and their relative positions on imaging area of specular object is studied for development of an optimized configuration of digital holographic camera. Applicability of proposed scheme is demonstrated through detection of defects in silicon optics using digital holographic interferometry.

Ubiquitous Control of a CNC Machine: Proof of Concept for Industrial IoT Applications

In this paper, an integrated system to control and manage a state-of-the-art industrial computer numerical control (CNC) machine (Studer S33) using a commercially available tablet (Samsung Galaxy Tablet S2) is presented as a proof of concept (PoC) for the ubiquitous control of industrial machines. As a PoC, the proposed system provides useful insights to support the further development of full-fledged systems for Industrial Internet of Things (IIoT) applications. The proposed system allows for the quasi-decentralisation of the control architecture of conventional programmable logic controller (PLC)-based industrial control systems (ICSs) through data and information exchange over the transmission control protocol and the internet protocol (TCP/IP) suite using multiple agents. Based on the TCP/IP suite, a network device (Samsung Galaxy Tablet S2) and a process field net (PROFINET) device (Siemens Simatic S7-1200) are interfaced using a single-board computer (Raspberry Pi 4). An override system mainly comprising emergency stop and acknowledge buttons is also configured using the single-board computer. The input signals from the override system are transmitted to the PROFINET device (i.e., the industrial control unit (ICU)) over TCP/IP. A fully functional working prototype is realised as a PoC for an integrated system designated for the wireless and ubiquitous control of the CNC machine. The working prototype as an entity mainly comprises a mobile (handheld) touch-sensitive human-machine interface (HMI), a shielded single-board computer, and an override system, all fitted into a compact case with physical dimensions of 300 mm by 180 mm by 175 mm. To avert potential cyber attacks or threats to a reasonable extent and to guarantee the security of the PoC, a multi-factor authentication (MFA) including an administrative password and an IP address is implemented to control the access to the web-based ubiquitous HMI proffered by the PoC.

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

Optimization of an autonomous robotic drilling system for the machining of aluminum aerospace alloys

This paper aims to identify the capability of a highly flexible industrial robot modified with a high-speed machine spindle for drilling of aluminum 6061-T6. With a focus on drilling feed rate, spindle speed, and pecking cycle, the hole surface roughness and exit burr heights were investigated using the Taguchi design methodology. A state of the art condition monitoring system was used to identify the vibrations experienced during drilling operation and to establish which robot pose had increased stiffness, and thus the optimum workspace for drilling. When benchmarked against a CNC machine the results show that the CNC was capable of producing the best surface finish and the lowest burr heights. However, the robot system matched and outperformed the CNC in several experiments and there is much scope for further optimization of the process. By identifying the optimum pose for drilling together with the idealized settings, the proposed drilling system is shown to be far more flexible than a CNC milling machine and when considering the optimized drilling of aerospace aluminum this robotic solution has the potential to drastically improve productivity.