Expectations and limitations of Cyber-Physical Systems (CPS) for Advanced Manufacturing: A View from the Grinding Industry

Grinding is a critical technology in the manufacturing of high added-value precision parts, accounting for approximately 20–25% of all machining costs in the industrialized world. It is a commonly used process in the finishing of parts in numerous key industrial sectors such as transport (including the aeronautical, automotive and railway industries), and energy or biomedical industries. As in the case of many other manufacturing technologies, grinding relies heavily on the experience and knowledge of the operatives. For this reason, considerable efforts have been devoted to generating a systematic and sustainable approach that reduces and eventually eliminates costly trial-and-error strategies. The main contribution of this work is that, for the first time, a complete digital twin (DT) for the grinding industry is presented. The required flow of information between numerical simulations, advanced mechanical testing and industrial practice has been defined, thus producing a virtual mirror of the real process. The structure of the DT comprises four layers, which integrate: (1) scientific knowledge of the process (advanced process modeling and numerical simulation); (2) characterization of materials through specialized mechanical testing; (3) advanced sensing techniques, to provide feedback for process models; and (4) knowledge integration in a configurable open-source industrial tool. To this end, intensive collaboration between all the involved agents (from university to industry) is essential. One of the most remarkable results is the development of new and more realistic models for predicting wheel wear, which currently can only be known in industry through costly trial-and-error strategies. Also, current work is focused on the development of an intelligent grinding wheel, which will provide on-line information about process variables such as temperature and forces. This is a critical issue in the advance towards a zero-defect grinding process.The authors gratefully acknowledge the funding support received from the Spanish Ministry of Economy and Competitiveness and the FEDER operation program for funding the project “Scientific models and machine-tool advanced sensing techniques for efficient machining of precision components of Low-Pressure Turbines” (DPI2017-82239-P)

An Original Tribometer to Analyze the Behavior of Abrasive Grains in the Grinding Process

Manufacturing of grinding wheels is continuously adapting to new industrial requirements. New abrasives and new wheel configurations, together with wheel wear control allow for grinding process optimization. However, the wear behavior of the new abrasive materials is not usually studied from a scientific point of view due to the difficulty to control and monitor all the variables affecting the tribochemical wear mechanisms. In this work, an original design of pin-on-disk tribometer is developed in a CNC (Computer Numerical Control) grinding machine. An Alumina grinding wheel with special characteristics is employed and two types of abrasive are compared: White Fused Alumina (WFA) and Sol-Gel Alumina (SG). The implemented tribometer reaches sliding speeds of between 20 and 30 m/s and real contact pressures up to 190 MPa. The results show that the wear behavior of the abrasive grains is strongly influenced by their crystallographic structure and the tribometer appears to be a very good tool for characterizing the wear mechanisms of grinding wheels, depending on the abrasive grains.The authors gratefully acknowledge the funding support received from the Spanish Ministry of Economy and Competitiveness and the FEDER operation program for funding the project "Scientific models and machine-tool advanced sensing techniques for efficient machining of precision components of Low Pressure Turbines" (DPI2017-82239-P). Funding support was also received from the contracting call for the training of research staff in UPV/EHU 2016, of Vice-rectorate of research to develop this project

On the Influence of Infra-Red Sensor in the Accurate Estimation of Grinding Temperatures

Workpiece rejection originated by thermal damage is of great concern in high added-value industries, such as automotive or aerospace. Surface temperature control is vital to avoid this kind of damage. Difficulties in empirical measurement of surface temperatures in-process imply the measurement in points other than the ground surface. Indirect estimation of temperatures demands the use of thermal models. Among the numerous temperature measuring techniques, infra-red measurement devices excel for their speed and accurate measurements. With all of this in mind, the current work presents a novel temperature estimation system, capable of accurate measurements below the surface as well as correct interpretation and estimation of temperatures. The estimation system was validated by using a series of tests in different grinding conditions that confirm the hypotheses of the error made when measuring temperatures in the workpiece below the surface in grinding. This method provides a flexible and precise way of estimating surface temperatures in grinding processes and has shown to reduce measurement error by up to 60%.Ministerio de Educacion,Cultura y Deporte DPI2017-82239-P AIE/FEDER, U

Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels

Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grinding wheel wear status monitoring using an AE sensor. The most relevant finding is the possibility of efficient feature extraction form frequency plots using CNNs. Feature extraction from FFT plots requires sound domain-expert knowledge, and thus we present a new approach to automated feature extraction using a pre-trained CNN. Using the features extracted for different industrial grinding conditions, t-SNE and PCA clustering algorithms were tested for wheel wear state identification. Results are compared for different industrial grinding conditions. The initial state of the wheel, resulting from the dressing operation, is clearly identified for all the experiments carried out. This is a very important finding, since dressing strongly affects operation performance. When grinding parameters produce acute wear of the wheel, the algorithms show very good clustering performance using the features extracted by the CNN. Performance of both t-SNE and PCA was very much the same, thus confirming the excellent efficiency of the pre-trained CNN for automated feature extraction from FFT plots.The authors gratefully acknowledge the funding support received from the Spanish Ministry MCIN/AEI/10.13039/501100011033 to the Research Project PID2020-114686RB-I00. The research has also received partial funding from TwinGrind (RTC2019-007064-2) supported by the Spanish Science and Innovation Ministry

Analysis of the dressing process using stationary dressing tools

Grinding process is a very important process in machining industry to manufacture high quality part. The correct preparation of grinding wheel involves dressing process taking importance to optimize grinding process. Due to the different dressing tools types, it is very difficult to find the most adequate tool for a particular application. In this work, a systematic analysis of stationary multipoint and blade dressing tools have been carried out attending to the influence of dressing parameters in wheel performance and in its wear. The obtained results reflect the importance of a correct choosing both of the dressing tools and dressing parameters

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.Thanks are given to the Spanish Ministry of Economy and Competitiveness for their support of the Research Project. Integration of numerical models and experimental techniques for improving the added value in grinding of precision parts. (DPI2010-21652-C02-01). This work was also supported in part by the Regional Government of the Basque Country through the Departamento de Educacion, Universidades e Investigacion (Project IT719-13) and UPV/EHU under grant UFI11/28

Arteztutako gainazaletan diamantatze prozesuko patroiak karakterizatzeko metodologiaren garapena

One of the most influential effects of the dressing process is that this process conditions the surface finish of the final part. In fact, due to the transversal dressing process, some periodical heli-cal patterns appear on the surface of the ground parts. The presence of these marks can lead to a nega-tive impact on the performance of certain sealing applications. One of the most outstanding applications are the rotating shafts where the radial seals are mounted, where it is essential to guarantee sealing per-formance and extend the seal life as long as possible. To do this, it is essential to measure the surface of the ground part and to characterize its helical patterns appropriately. In this article, in addition to a thorough review of the usual methods currently used, the measurement of surfaced surfaces and a meth-odology for the characterization of dressing-lead developing at IK4-Ideko will be reported.; Diamantatze prozesuaren eragin nabarmenetako bat da prozesuak pieza arteztuaren gainazalaren akabera baldintzatzen duela. Hain zuzen, aitzinapen transbertsaldun diamantatze prozesuen eraginez, arteztutako piezen gainazalean helize itxurako markak agertu ohi dira. Gisa horretako markak agertzeak eragin kaltegarriak izan ditzake zenbait aplikazioren errendimenduan, piezen funtzio nagusia zigilatzea edo koipeztatzea bada. Horren aplikazio nabarmenetako bat zigilu erradialak daramatzaten ar-datz birakariak dira, non ezinbestekoa gertatzen den zigiluaren bizitza ahalik eta gehien luzatzea betiere zigilatzea bermatuaz. Horretarako, ezinbestekoa gertatzen da piezaren gainazala neurtu eta helize itxu-rako patroiak era egoki batean karakterizatzea. Artikulu honetan, patroi horiek ebaluatzeko erabiltzen diren ohiko metodoen berrikusketa sakon bat egiteaz gain, metodologia berri baten berri emango da, IK4-Ideko garatzen ari dena arteztutako gainazalak neurtzeko eta dressing-leada karakterizatzeko