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.

[1]  Tianbiao Yu,et al.  Modeling and simulation of grinding wheel by discrete element method and experimental validation , 2015 .

[2]  João Fernando Gomes de Oliveira,et al.  Development of an optical scanner to study wear on the working surface of grinding wheels , 1999 .

[3]  W. Liu,et al.  Parametric evaluation and three-dimensional modelling for surface topography of grinding wheel , 2019, International Journal of Mechanical Sciences.

[4]  Qing Miao,et al.  Comparison on grindability and surface integrity in creep feed grinding of GH4169, K403, DZ408 and DD6 nickel-based superalloys , 2020 .

[5]  Xianguang Kong,et al.  An adaptive grinding chatter detection method considering the chatter frequency shift characteristic , 2020 .

[6]  Jian Xu,et al.  Non-linear analysis and quench control of chatter in plunge grinding , 2015 .

[7]  Christophe Lescalier,et al.  Numerical analysis of grinding temperature measurement by the foil/workpiece thermocouple method , 2006 .

[8]  Fritz Klocke,et al.  Abrasive machining of advanced aerospace alloys and composites , 2015 .

[9]  Sandro Wartzack,et al.  Shaping the digital twin for design and production engineering , 2017 .

[10]  Zhaoyao Shi,et al.  A survey of methods for detecting metallic grinding burn , 2019, Measurement.

[11]  Xipeng Xu,et al.  Effect of grinding temperatures on the surface integrity of a nickel-based superalloy , 2002 .

[12]  Haiying Huang,et al.  A displacement-softening contact model for discrete element modeling of quasi-brittle materials , 2018 .

[13]  Stephen Malkin,et al.  Thermal Aspects of Grinding: Part 2—Surface Temperatures and Workpiece Burn , 1974 .

[14]  Robert Bauer,et al.  Experimental and numerical investigations of single abrasive-grain cutting , 2011 .

[15]  T. Jayakumar,et al.  A review of the application of acoustic emission techniques for monitoring forming and grinding processes , 2005 .

[16]  José Antonio Sánchez,et al.  On the development and evolution of wear flats in microcrystalline sintered alumina grinding wheels , 2018 .

[17]  Konrad Wegener,et al.  In-process workpiece based temperature measurement in cylindrical grinding , 2018 .

[18]  Hiroshi Yamada,et al.  Elastic moduli of grinding wheel based on a simplified model. , 1982 .

[19]  Eraldo Jannone da Silva,et al.  Global strategy of grinding wheel performance evaluation applied to grinding of superalloys , 2019, Precision Engineering.

[20]  Christoph Löpenhaus,et al.  Application of Hall effect for assessing grinding thermal damage , 2019, Journal of Materials Processing Technology.

[21]  L. Uriarte,et al.  Continuous workpiece speed variation (CWSV): Model based practical application to avoid chatter in grinding , 2009 .

[22]  Garret E. O’Donnell,et al.  Acoustic emission in dressing of grinding wheels: AE intensity, dressing energy, and quantification of dressing sharpness and increase in diamond wear-flat size , 2018 .

[23]  Lida Zhu,et al.  Analysis of loads on grinding wheel binder in grinding process: insights from discontinuum-hypothesis-based grinding simulation , 2015 .

[24]  Zhonghua Yu,et al.  Application of Hilbert–Huang Transform to acoustic emission signal for burn feature extraction in surface grinding process , 2014 .

[25]  Tien-Chien Jen,et al.  Thermal aspects of grinding with CBN wheels , 1989 .

[26]  S. Malkin,et al.  Energy Partition to the Workpiece for Grinding with Aluminum Oxide and CBN Abrasive Wheels , 1995 .

[27]  Tien-Chien Jen,et al.  Coupled heat transfer to workpiece, wheel and fluid in grinding, and the occurrence of workpiece burn , 1991 .

[28]  Fan Fan,et al.  Methodology for the immediate detection and treatment of wheel wear in contour grinding , 2019 .

[29]  N. Arunachalam,et al.  A Digital Clone for Grinding Wheel ? An Information Sharing Platform for Sustainable Grinding Process , 2019 .

[30]  I. Iordanoff,et al.  Discrete-element modelling of the grinding contact length combining the wheel-body structure and the surface-topography models , 2016 .

[31]  Virginia Pilloni,et al.  How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0 , 2018, Future Internet.

[32]  Guoqin Huang,et al.  Modeling and simulation of the distribution of undeformed chip thicknesses in surface grinding , 2018 .

[33]  Kevin I-Kai Wang,et al.  Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues , 2020, Robotics Comput. Integr. Manuf..

[34]  Ekkard Brinksmeier,et al.  Wheel Based Temperature Measurement in Grinding , 2011 .

[35]  Takashi Ueda,et al.  Cooling Characteristics of Cutting Grain in Grinding , 1996 .

[36]  Krzysztof Nadolny,et al.  Wear phenomena of grinding wheels with sol–gel alumina abrasive grains and glass–ceramic vitrified bond during internal cylindrical traverse grinding of 100Cr6 steel , 2015 .

[37]  Jérôme Néauport,et al.  Discrete element method to simulate continuous material by using the cohesive beam model , 2012 .

[38]  Berend Denkena,et al.  Abrasion Monitoring and Automatic Chatter Detection in Cylindrical Plunge Grinding , 2013 .

[39]  Robert Bauer,et al.  Application of region growing method to evaluate the surface condition of grinding wheels , 2004 .

[40]  Tianbiao Yu,et al.  Grinding temperature field prediction by meshless finite block method with double infinite element , 2019, International Journal of Mechanical Sciences.

[41]  Damien André,et al.  A novel DEM approach for modeling brittle elastic media based on distinct lattice spring model , 2019, Computer Methods in Applied Mechanics and Engineering.

[42]  W. Rowe,et al.  Experimental Investigation of Heat Transfer in Grinding , 1995 .

[43]  Ali Tarokh,et al.  Discrete element simulation of the effect of particle size on the size of fracture process zone in quasi-brittle materials , 2014 .

[44]  Itziar Cabanes,et al.  Experimental and numerical analysis of thermal phenomena in the wear of single point diamond dressing tools , 2017 .

[45]  I. Iordanoff,et al.  Modelling the wear evolution of a single alumina abrasive grain: Analyzing the influence of crystalline structure , 2020 .

[46]  Jokin Munoa,et al.  Analysis of the beating frequencies in dressing and its effect in surface waviness , 2019, CIRP Annals.

[47]  Robert Bauer,et al.  Finite element modeling approaches in grinding , 2009 .

[48]  Jorge Álvarez,et al.  On the Influence of Infra-Red Sensor in the Accurate Estimation of Grinding Temperatures , 2018, Sensors.

[49]  Qing Miao,et al.  Tool wear behavior of vitrified microcrystalline alumina wheels in creep feed profile grinding of turbine blade root of single crystal nickel-based superalloy , 2020 .

[50]  W. Rowe,et al.  The Effect of Deformation on the Contact Area in Grinding , 1993 .

[51]  Zhao Rongli,et al.  Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system , 2020 .

[52]  Jie Chen,et al.  A cyber-physical system deployment based on pull strategies for one-of-a-kind production with limited resources , 2020, Journal of Intelligent Manufacturing.

[53]  Takashi Ueda,et al.  On The Measurement of Temperature in Material Removal Processes , 2007 .