Towards the Digital Model of Tool Lifecycle Management in Sheet Metal Forming
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Production Engineering, University of Belgrade, Serbia
Research Department, Metalac Company, Serbia
Engineering Department, Metalac Company, Serbia
Vidosav D. Majstorovic   

Production Engineering, University of Belgrade, Serbia
Submission date: 2023-05-05
Final revision date: 2023-08-28
Acceptance date: 2023-08-29
Online publication date: 2023-09-15
Sheet metal forming is a critical process in the manufacturing industry, which involves shaping sheet metal into desired configurations and structures. The use of digital tools in the lifecycle management of sheet metal forming tools has become increasingly important to ensure the efficiency and effectiveness of the process. The digital model of tool lifecycle management (TLM) in sheet metal forming provides a complete approach to manage the entire lifecycle of tools used in sheet metal forming. It enables optimization of tool design, simulation of the tooling process, real-time monitoring of tool conditions, and retirement and replacement of tools. This approach improves efficiency, reduces costs, and ensures optimal performance in sheet metal forming. The paper presents an elaborate analysis of the development of TLM models concerning the progress in ICT modeling and its implementation in the field of sheet metal forming. Furthermore, the paper includes an exemplary TLM model for an industrial enterprise.
KUSIAK A., 2018, Smart Manufacturing, International Journal of Production Research, 56/1–2, 508–517,
WANG B., TAO F., FANG X., LIU Ch., Liu Y., FREIHEIT T., 2021, Smart Manufacturing and Intelligent Manufacturing: A Comparative Review, Engineering, 7/6, 738–757, /10.1016/j.eng.2020.07.017.
LOPEZ F., SHAO Y., MORLEY M., MOYNE J., BARTON K., TILBURY., 2018, A Software-Defined Framework for the Integrated Management of Smart Manufacturing Systems, Manufacturing Letters, 15, Part A, January 18–21,
BERNARD A., 1993, The Feature Approach for the Integrated Design and Machining of Forming Dies, Robotics & Computer-Integrated Manufacturing, 10/l–2, 71–76.
KARAFILLISFOT A., BOYCET M., 1996, Tooling and Binder Design for Sheet Metal Forming Processes Compensating Spring Back Error, Int. J. Math. Tools Manufact. 36/4, 503–526.
MAROPOULOS P.G., ALAMIN B., 1996, Integrated Tool Life Prediction and Management for an Intelligent Tool Selection System, Journal of Materials Processing Technology 61 225–230.
CAO J., KINSEY B.L., YAO H., VISWANATHAN V., 2001, Nan Song, Next Generation Stamping Dies - Controllability and Flexibility, Robotics and Computer Integrated Manufacturing 17, 49–56.
SAENZ de ARGANDONA E., AZTIRIA A., GARCIA C., ARANA N., IZAGUIRRE A., FILLATREAU P., 2008, Forming Processes Control by Means of Artificial Intelligence Techniques, Robotics and Computer-Integrated Manufacturing, 24, 773–779,
FILLATREAU P., BERNARD F.X., AZTIRIA A., SAENZ de ARGANDON E., GARCIA C ARANA., N., IZAGUIRRE A., 2008, Sheet Metal Forming Global Control System Based on Artificial Vision System and Force-Acoustic Sensors, Robotics and Computer- Integrated Manufacturing, 24, 780–787,
QIN Yi., 2006, Forming-Tool Design Innovation and Intelligent Tool-Structure/System Concepts, International Journal of Machine Tools & Manufacture , 46, 1253–1260.
GARCIA C., 2005, Artificial Intelligence Applied to Automatic Supervision, Diagnosis and Control in Sheet Metal Stamping Processes, Journal of Materials Processing Technology, 164–165, 1351–1357,
GAO C.Y, LOURS G., BERNHART P., 2005, Thermo Mechanical Stress Analysis of Superplastic Forming Tools, Journal of Materials Processing Technology, 169, 281–291.
BASSIUNYA A.B., LIB X., DUC R., 2007, Fault Diagnosis of Stamping Process Based on Empirical Mode Decomposition and Learning Vector Quantization, International Journal of Machine Tools & Manufacture, 47, 2298–2306.
LEPADATU D., HAMBLI R., KOBI A., BARREAU A., 2004, Tool Life Prediction in Metal Forming Processes Using Numerical Analysis, IFAC Automation in Mining, Mineral and Metal Processing, Nancy. France.
DOEGE E., SCHMIDT-JÜRGENSEN R., HUININK S., YUN J.-W., 2003, Development of an Optical Sensor for the Measurement of the Material Flow in Deep Drawing Processes, CIRP Annals, 52/1, 225–228.
DOEGE E., SEIDEL H.-J., GRIESBACH B., YUN J.-W., 2002, Contactless on-Line Measurement of Material Flow for Closed Loop Control of Deep Drawing, Journal of Materials Processing Technology, 130, 95–99.
MAHAYOTSANUN N., SAH S., CAO J., PESHKIN M., GAO R.X, WANG H-T., 2009, Tooling-integrated sensing systems for stamping process monitoring, International Journal of Machine Tools & Manufacture, 49/7–8, 634–644,
MAHAYOTSANUN N., CAO J., PESHKIN M., SAH S., GAO R., WANG C.T, 2007, Integrated Sensing System for Stamping Monitoring Control, Sensors, 2007 IEEE , Atlanta, GA, USA, 1376–1379, 2007.4388668.
BOOKSTEIN F.L, 1989, Principal Warps: Thin-Plate Splines and the Decomposition of Deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11/6, 567–585,
MIKLOS T., ZSOLT L., GAL G., 2008, Integrated Process Simulation and Die-Design in Sheet Metal Forming. International Journal of Material Forming, 1, 185–188,
CSER L., GEIGER M., LANGE K., KALS J.A.G., HÄNSEL M.. 1993, Tool Life and Tool Quality in Bulk Metal Forming, Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture, 207/4, 223–239,
ALBERTI N., CANNIZZARO L., MICARI F., 1991, Knowledge-Based Systems and FE Simulations in Metal-Forming Processes Design An Integrated Approach, CIRP Annals, 40, 295–298,
ANDERSSON A., 2001, Information Exchange within the Area of Tool Design and Sheet-Metal-Forming Simulations, Journal of Engineering Design, 12/4, 283–291,
HARTLEY P., PILLINGER I.D., 2001, Evelopments in Computational Modeling Techniques for Industrial Metal Forming Processes, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 215/7, 903–914,
VISHAL O., 2010, AI Applications to Metal Stamping Die Design – A Review, World Academy of Science, Engineering and Technology, International Journal of Industrial and Manufacturing Engineering, 4/8, 721–727.
SUYOG J., CHRIST P., NEELESH J., 2013, Causes of Failure and Repairing Options for Dies and Molds: A Review, Engineering Failure Analysis, 34,
CHAN W.L., F.U., M.W., 2008, An Integrated FEM and ANN Methodology for Metal-Formed Product Design, Eng. Appl. of AI, 21, 1170–1181,
LIHUI W., GAO R., 2006, Condition Monitoring and Control for Intelligent Manufacturing, Springer Series in Advanced Manufacturing,
DEQUAN Y., RUI Z., JUN C., et al., 2006, Research of Knowledge-Based System for Stamping Process Planning, Int. J. Adv. Manuf. Technol., 29, 663–669,
DUBAR M., DUBAR L., DUBOIS A., VERLEENE A., OUDIN J., 2001, Wear Criteria of Cold Forging Tools, Surface Engineering – Surf. Eng., 17, 119–122,
ESHEL G., BARASH M., JOHNSON W., 1986, Rule Based Modeling for Planning Axisymmetrical Deep-Drawing, Journal of Mechanical Working Technology, 14/1, 1–115, ISSN 0378-3804,
LINDVALL F.W., 2011, On Tool Steel, Surface Preparation, Contact Geometry and Wear in Sheet Metal Forming, Karlstads universitet, ISBN: 978917063403, Series: Karlstad University Studies, 1403–8099, 2011, 64.
GAARD A., 2008, Wear in Sheet Metal Forming, Karlstad University Studies, 10, ISSN 1403-8099, ISBN 978-91-7063-168-9.
QATTAWI A., MAYYAS A., DONGRI S., OMAR M., 2014, Knowledge-Based Systems in Sheet Metal Stamping: a Survey, International Journal of Computer Integrated Manufacturing, 27/8, 707–718, .2013.834463.
HONG H., LING W., 2013, The Rapid Design System of Stamping Process Based on KBE, Applied Mechanics and Materials, 347–350, 3460–3464,
RAMANA K.V., RAO P.V.M., 2004, Data and Knowledge Modeling for Design-Process Planning Integration of Sheet Metal Components, Journal of Intelligent Manufacturing, 15, 607–623,
PFEIFER T., IMKAMP D., GLOMBITZA M., 2000, Freeform Surface Measurement for Tool Correction, Proceedings, XVI IMEKO World Congress, IMEKO, International Measurement Confederation, Austrian Society for Measurement and Automation, 8/14, Measurement of Geometrical Quantities, Topic 25, Quality management /ed. MN Durakbasa, 237–242.
LI M-Z., CAI Z-Y., LIU Ch-G., 2007, Flexible Manufacturing of Sheet Metal Parts Based on Digitized-Die, Robotics and Computer-Integrated Manufacturing, 23/1, 107–115,
SITARAMAN S.K., KINZEL G.L., ALTAN T., 1991, A Knowledge-Based System for Process-Sequence Design in Axisymmetric Sheet-Metal Forming, Journal of Materials Processing Technology, 25/3, 247–271,
MOULTRIE J., CLARKSON P.J., PROBERT D.R., 2006, Development of a Product Audit Tool, Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture, 220/7, 1157–1174,
TEKKAYA A.E., 2000, State-of-the-art of Simulation of Sheet Metal Forming, Journal of Materials Processing Technology, 103, 14–22,
MIKLOS T., ZSOLT L., 2013, Computer Aided Process Planning and Die Design in Simulation Environment in Sheet Metal Forming, AIP Conference Proceedings, 16 December, 1567/1, 1002–1007,
TISZA M., 2005, Numerical Modeling and Simulation in Sheet Metal Forming Academic and Industrial Perspectives, MSF, 473–474, 407–14,
TSAI Y-L., YOU Ch-F., LIN J-Y., LIU K-Y., 2010, Knowledge-Based Engineering for Process Planning and Die Design for Automotive Panels, Computer-Aided Design and Applications, 7/1, 75–87, .
PILANI R., NARASIMHAN K., MAITI S.K., SINGH U.P., DATE P.P., 2000, A Hybrid Intelligent Systems Approach for Die Design in Sheet Metal Forming, International Journal of Advanced Manufacturing Technology, 16, 370–375,
WANG H., LI G., 2010, Sheet Forming Optimization Based on Least Square Support Vector Regression and Intelligent Sampling Approach, Int. J. Mater. Form., 3/1, 9–12,
CAO J., BRINKSMEIER E., FU M., GAO R.X., LIANG B., MERKLEIN M., et al., 2019, Manufacturing of Advanced Smart Tooling For Metal Forming, CIRP Annals, 68/2, 605–628,
GROCHE P., BRENNEIS M., 2014, Manufacturing and Use of Novel Sensory Fasteners for Monitoring Forming Processes, Measurement, 53, 136–144,
AMOON M., ALTAMEEM T., ALTAMEEM A., 2020, Internet of Things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System, Measurement, 161, 107861 ,
MOLE N., CAFUTA G., STOCK B., 2014, A 3D Forming Tool Optimization Method Considering Springback and Thinning Compensation, Journal of Materials Processing Technology, 214, 1673–1685, jmatprotec.2014.03.017.
CHEN Ch., WANG Y., OU H., HE Y., TANG X., 2014, A Review on Remanufacture of Dies and Molds, Journal of Cleaner Production, 64, 13–23, jclepro.2013.09.014.
ZHANG G., LEE Ch., ZHOU H., WAGNER T., 2018, Punching Process Monitoring Using Wavelet Transform Based Feature Extraction and Semi-Supervised Clustering, Procedia Manufacturing, 26, 1204–1212,
ADDONA D.D., TETI R., 2011, Multi-Agent Tool Management in the Manufacturing of Aircraft Engines, Proc. IMechE, 225 B, J. Engineering Manufacture, 62–73,
GAO R.X., SAH S., MAHAYOTSANUN N., 2010, On-Line Measurement of Contact Pressure Distribution at Tool–Workpiece Interfaces in Manufacturing Operations, CIRP Annals - Manufacturing Technology, 59, 399–402,
ALLWOOD J.M., DUNCAN S.R., CAO J., GROCHE P., HIRT G., KINSEY B., KUBOKI T., LIEWALD M., STERZING A., TEKKAYA A.E., 2016, Closed-Loop Control of Product Properties in Metal Forming, CIRP Annals – Manufacturing Technology, 65, 573–596,
LIU H., SAKSHAM D., SHEN M., CHEN K., WU V., WANG L., 2022, Industry 4.0 in Metal Forming Industry Towards Automotive Applications: A Review, International Journal of Automotive Manufacturing and Materials, 1/1, 2,
MILUTINOVIC M., MILOSEVIC M., ILIC J., MOVRIN D., KRAISNIK M., RANĐELOVIC S., LUKIC D., 2021, Industry 4.0 and New Paradigms in the Field of Metal Forming, Technical Journal, 15, 2250–2257, https://
RALPH B.J., STOCKINGER M., 2020, Digitalization and Digital Transformation in Metal Forming: Key Technologies, Challenges and Current Developments of Industry 4.0 Applications, XXXIX. Verformungskundliches Kolloquium: Zauchensee, 13–23, Montanuniversität Leoben, Lehrstuhl für Umformtechnik.
YANG D.Y., BAMBACH M., CAO J., DUFLOU J.R., GROCHE P., KUBOKI T., STERZING A., TEKKAYA A.E., LEE C.W., 2018, Flexibility in Metal Forming, CIRP Annals, 67/2, 743–765,
BAI Ch., DALLASEGA P., ORZES G., SARKIS J., 2019, Industry 4.0 technologies assessment: A sustainability perspective, Int. J. Production Economics, 229, 107776,
YANG M., 2019, Sensing Technologies for Metal Forming, Sensors and Materials, 31/10, 3121–3128,
HAGENACH H., SCHULTE R., VOGEL M., HERMANN J., SCHARRERA H., LECHNERA M., MERKLEIN M., 2019, 4.0 in Metal Forming – Questions And Challenges, Procedia CIRP, 79, 649–654,
RALPH B.J., SCHWARZ A., STOCKINGER M., 2020, An Implementation Approach for an Academic Learning Factory for the Metal Forming Industry with Special Focus he Digital Twins and Finite Element Analysis, Procedia Manufacturing, 45, 253–258,
YANG M., 2018, Smart Metal Forming with Digital Process and IoT, International Journal of Lightweight Materials and Manufacture, 1, 207–214,
THAMES L., SCHAEFER D., 2016, Software-Defined Cloud Manufacturing for Industry 4.0, Procedia CIRP, 52, 12–17,
FAHLE S., PRINZ C., KUHLENKÖTTER B., 2020, Systematic Review on Machine Learning (ML) Methods for Manufacturing Processes – Identifying Artificial Intelligence (AI) Methods for Field Application, Proceeding CIRP, 93, 413–418,
WU D., ROSEN D.W., WANG L., SCHAEFER D., 2015, Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation, Comput. Aided Des. 59/1–14,
BÄUME T., ZORN W., DROSSEL W.G., RUPP G., 2016, Iterative Process Control and Sensor Evaluation for Deep Drawing Tools with Integrated Piezoelectric Actuators, Manufacturing Rev., 3/3.
SACHSENMEIER P., 2016, Industry 5.0—The Relevance and Implications of Bionics and Synthetic Biology, Engineering, 2, 225–229,
LI G., LIANG Z., 2020, Intelligent Design Method and System of Trimming Block for Stamping Dies of Complex Automotive Panels. Int. J. Adv. Manuf. Technol., 109, 2855–2879,
HAWRYLUK M., MRZYGŁOD B., 2017, A Durability Analysis of Forging Tools for Different Operating Conditions with Application of a Decision Support System Based on Artificial Neural Networks (ANN), Maintenance and Reliability, 19/3, 338–348,
BAPURAO M L., 2016, A Review Paper on FEA Application for Sheet Metal Forming Analysis. Corpus ID: 212483948.
JONSSON C.J., STOLT R., ELGH F., 2020, Stamping Tools for Sheet Metal Forming: Current State and Future Research Directions, Research Article, 281-290,
SON Y.H., PARK K.T., LEE D., JEON S.W., NOH S.D., 2021, Digital Twin–Based Cyber-Physical System For Automotive Body Production Lines, The International Journal of Advanced Manufacturing Technology, 115, 291–310,
TAO F., ZHANG H., LIU A., NEE A.Y.C., 2013, Digital Twin in Industry: State-of-the-Art, IEEE Transactions on Industrial Informatics, 15/4, 2405–2415, April 2019,
TATIPALA S., WALL J., JOHANSSON C.M., SIGVANT M., 2018, Data-Driven Modeling in the era of Industry 4.0: A case study of friction modeling in sheet metal forming simulations. Journal of Physics: Conference Series, 1063/1. IOP Publishing.
TATIPALA S., WALL J., JOHANSSON Ch., LARSSON T., 2020, A Hybrid Data-Based and Model-Based Approach to Process Monitoring and Control in Sheet Metal Forming, Processes, 8/1, 89,
GRZESIK W., RUSZAJ A., 2021, Hybrid Manufacturing Processes, Springer Series in Advanced Manufacturing,
HAFIZ A.A.M., HUSSEIN H.M.A., GEMEAL A.M.B., NARANJE V., 2019, Topology Optimization of Sheet Metal Combination Die, International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, 191–196,
KAO Y-Ch., LIU Y-P., WEI C-L., HSIEH S-H., YU Ch-Y., 2018, Application of a Cyber-Physical System and Machine-to-Machine Communication for Metal Processes. Conference: IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 1–6,
SCHUH G., BERGWEILER G., FIEDLER F., BICKENDORF P., COLAG C., 2019, A Review on Flexible Forming of Sheet Metal Parts, IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, 1221–1225,
ZHOU D., YUAN X., GAO H., WANG A., LIU J., EL FAKIR O., POLITIS D.J., WANG L., LIN J., 2016, Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes. J. Vis. Exp., 118, e53957,
WANG A., EL FAKIR O., LIU J., ZHANG Q., ZHENG Y., WANG L., 2019, Multi-objective Finite Element Simulations of a Sheet Metal-Forming Process via a Cloud-Based Platform. The International Journal of Advanced Manufacturing Technology, 100,
OPRITESCU D., VOLK W., 2015, Automated Driving for Individualized Sheet Metal Part Production—A Neural Network Approach, Robotics and Computer-Integrated Manufacturing, 35, 144–150, .
HELO P., PHUONG D., HAO Y., 2019, Cloud Manufacturing – Scheduling as a Service for Sheet Metal Manufacturing, Comput. Op. Res., 110, C, 208–219,
Design Guide: Sheet Metal Fabrication, Version 2.2, 2023,
ZHANG Q., LIU Y., ZHANG Z., 2016, A New Optimization Method for Sheet Metal Forming Processes Based on an Iterative Learning Control Model. Int. J. Adv. Manuf. Technol., 85, 1063–1075.
REZAEIANJOUYBARI B., SHANG Y., 2020, Deep learning for prognostics and health management: State of the art, challenges, and opportunities, Measurement, 163, 107929,
TRAN M-Q., DOAN H-P., VU V.Q., VU L.T., 2023, Machine Learning and IoT-Based Approach For Tool Condition Monitoring: A Review and Future Prospects, Measurement, 207, 112351,
Liu S., Xia Y., Liu Y., Shi Z., Hui Yu, Li Z., J., 2022, Tool path planning of consecutive free-form sheet metal stamping with deep learning, Journal of Materials Processing Tech., 303, 117530,
SANTHI A.R., MUTHUSWAMY P., 2023, Industry 5.0 or Industry 4.0S? Introduction to Industry 4.0 and a Peek into the Prospective Industry 5.0 Technologies, International Journal on Interactive Design and Manufacturing (IJIDeM), 17, 947–979,
CAO J., BANU M., 2020, Opportunities and Challenges in Metal Forming for Lightweighting: Review and Future Work, Journal of Manufacturing Science and Engineering, 142,
AWASTHI A, SAXENA K.K., ARUN V., 2021, Sustainable and Smart Metal Forming Manufacturing Process, MaterialsToday Proceedings, 44, 2069–2079,
ZHOU H., XU Q., NIE Z., LI N., 2022, A Study on Using Image-Based Machine Learning Methods to Develop Surrogate Models of Stamp Forming Simulations, ASME, J. Manuf. Sci. Eng. February; 144/2, 021012.
SORGER M., RALPH B.J., HARTL K., WOSCHANK M., STOCKINGER M., 2021, Big Data in the Metal Processing Value Chain: A Systematic Digitalization Approach under Special Consideration of Standardization and SMEs. Appl. Sci., 13, 9021,
CHEN T., SAMPATH V., MAY M.C., SHAN S., JORG O.J., AGUILAR MARTIN J.J., STAMER F., FANTONI G., TOSELLO G., CALAON M., 2023, Machine Learning in Manufacturing towards Industry 4.0: from for now to Four-Know. Appl. Sci. 13, 1903,
STJEPANDIC J., SOMMER M., DENKENA B., 2021, DigiTwin: An Approach for Production Process Optimization in a Built Environment, Springer Series in Advanced Manufacturing,
AL-GAMAL A., ABLAT M.A., RAMINENI L., ALI M., ALMOTARI A., ALAFAGHANI A., SUN J., QATTAWI A., 2022, Cradle-to-Gate Life Cycle Analysis of Origami-Based Sheet Metal for Automobile Parts, Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition. 2B, Advanced Manufacturing, Columbus, Ohio, USA, V02BT02A065, ASME,
LI W., LIANG Y., WANG S., 2021, Data Driven Smart Manufacturing Technologies and Applications. Springer Series in Advanced Manufacturing,
MOHANED A., KURTH R., TEHEL R., WAGNER M., WAGNER N., IHLENFELDT S., 2022, Potential of Tool Clamping Surfaces in Forming Machines for Cognitive Production, Journal of Machine Engineering, 22/3 116–131,
NEE A.Y.C., SONG B., ONG S-K., 2013, Re-engineering Manufacturing for Sustainability, Proceedings of the 20th CIRP International Conference on Life Cycle Engineering, Singapore 17–19 Springer Singapore,
NIEMIETZ P., KORNELY M.J.K., TRAUTH D., et al., 2022, Relating Wear Stages in Sheet Metal Forming Based on short- and long-term force signal variations, J. Intell. Manuf., 33, 2143–2155,
KUBIK C., BECKER M., MOLITOR D.A., et al., 2023, Towards a Systematic Approach for Wear Detection in Sheet Metal Forming Using Machine Learning, Prod. Eng. Res. Devel., 17, 21–36,
NIEMIETZ P., FENCL M., BERGS T., 2023, Study on Learning Efficient Stroke Representations in Clocked Sheet Metal Processing: theoretical and Practical Evaluation. Prod. Eng. Res. Devel., 17, 279–289,
BARDA A., TEVET G., SCHULZ A., BERMANO A.H., 2023, Generative Design of Sheet Metal Structures, ACM Trans. Graph., 42/4, Article 116, 1–13,
TRZEPIECINSKI T., 2023, Approaches for Preventing Tool Wear in Sheet Metal Forming Processes, Machines, 11, 616,
HOU Y., MYUNG D., PARK J.K., MIN J., LEE H.-R., EL-ATY A.A., LEE M.-G., 2023, A Review of Characterization and Modeling Approaches for Sheet Metal Forming of Lightweight Metallic Materials, Materials, 16/2,
TRZEPIECINSKI T., 2020, Recent Developments and Trends in Sheet Metal Forming, Metals, 10/6.
MERAYO D., RODRIGUEZ-PRIETO A., CAMACHO A.M., 2021, Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning, Metals, 11, 1289,
CRUZ D.J., BARBOSA M.R., SANTOS A.D., MIRANDA S.S., AMARAL R.L., 2021, Application of Machine Learning to Bending Processes and Material Identification, Metals, 11, 1418,
VUKADINOVIC V., MAJSTOROVIC V., ZIVKOVIC J., STOJADINOVIC S., DJURDJANOVIC D., 2021, Digital Manufacturing as a basis for the development of the Industry 4.0 model, Procedia CIRP, 104, 1867–1872,
KRCO S., et al, 2023, IoT Project in XYZ Company, In progress, Gornji Milanovac.
MOURTZIS D., et al., 2022, Operator 5.0: A Survey on Enabling Technologies and a Framework for Digital Manufacturing Based on Extended Reality, Journal of Machine Engineering, 22/1, 43–69,
MOURTZIS D., 2021, Towards the 5th Industrial Revolution: a Literature Review and a Framework for Process Optimization Based on Big Data Analytics and Semantics, Journal of Machine Engineering, 21/3, 5–39,