Tooling systems with integrated sensors enabling data based process optimization
More details
Hide details
IFT – Institute for Production Engineering and Photonic Technologies, TU Wien, Austria
Submission date: 2021-01-08
Final revision date: 2021-03-14
Acceptance date: 2021-03-16
Online publication date: 2021-03-29
Publication date: 2021-03-29
Journal of Machine Engineering 2021;21(1):5-21
Sensor integration into machining equipment has become an important factor for gaining deep process insights mainly driven by increasingly smaller and cheaper sensors and transmitters. Due to advances in microelectronics and communication technology, a broader field of applications in production processes and machine tools can be addressed using sensing devices and their implementation potentials. Ensuring a sensitive but robust data stream from close to the actual process allows not only reliable monitoring but also process and quality control based on sensor information. This paper provides an overview of the utilization of sensor data for the purpose of condition monitoring, model fitting and real-time control coping with stochastic effects. Examples of sensor integration in fields of injection molding, roll forming and heavy-duty milling comprise the state of the art of sensor implementation, data evaluation and possible feedback loops in the respective application scenarios.
NATH C., 2020, Integrated Tool Condition Monitoring Systems and Their Applications: A Comprehensive Review, Procedia Manufacturing, 48, 852–863.
SCHIESSLE E., 2016, Industriesensorik-Sensortechnik und Messwertaufnahme, Vogel Business Media, ISBN 978-3-8343-3341-4, 21–23.
JEMIELNIAK K., 2019, Contemporary Challenges in Tool Condition Monitoring, Journal of Machine Engineering, 2019, 19/1, 48–61.
DIMLA Sr. D.E., LISTER P.M., 1999, On-Line Metal Cutting Tool Condition Monitoring. I: Force and Vibration Analyses, International Journal of Machine Tools & Manufacture, 40, 739–768.
DWORSCHAK B., ZAISER H., 2014, Competences for Cyber-Physical Systems in Manufacturing – First Findings and Scenarios, Procedia CIRP, 25, 345–350.
YANG X., 2020, Digital Twin for Cutting Tool: Modeling, Application and Service Strategy, Journal of Manufacturing Systems, 58 Part B, 305–312.
GAO R.X., 2008, Injection Molding Process Monitoring Using a Self-Energized Dual-Parameter Sensor, CIRP Annals – Manufacturing Technology, 75, 389–393.
NAKAO M., YODA M., 2003, Locally Controlling Heat Flux for Preventing Micrometre-Order Deformation with Injection Molding of Miniature Products, CIRP, 52/1, 451–454.
MAO T., ZHANG Y., 018, Feature Learning and Process Monitoring of Injection Molding Using Convolution-Deconvolution Auto Encoders, Computers and Chemical Engineering, 118, 77–90.
MONOSTORI L., 1993, A Step Towards Intelligent Manufacturing: Modelling and Monitoring of Manufacturing Processes through Artificial Neural Networks, CIRP, 42/1, 485–488.
BHATTACHARYYA D., 1986, The Prediction of Roll Load in Cold Roll-Forming, Journal of Mechanical Working Technology, 14, 363–379.
SEDLMAIER A., 2017, Digitalization in Roll Forming Manufacturing, J. Phys., Conf. Ser. 896 012038.
LINDGREN M., 2009, 3D Roll-Forming of Hat-Profile with Variable Depth and Width, 1st International Congress of RollForming, RollFORM Paper 7.
BIDABADI B.S., 2017, Experimental and Numerical Study of Required Torque in the Cold roll Forming of Symmetrical Channel Sections, Journal of Manufacturing Processes, 27, 63–75.
JURKOVIC M., 2015, An Investigation of the Force and Torque at Profile Sheet Metal Rolling-Input Data for the Production System Reengineering, Tehnički Vjesnik, 22/4, 1029–1034.
BECKER M., GROCHE P., 2019, Towards Nonstop Availability in Roll Forming through Digitalization, Wulfsberg J.P., Hintze W., Behrens B.A. (eds), Production at the Leading Edge of Technology, Springer Vieweg, Berlin, Heidelberg,
LIU X., 2017, Investigation of Forming Parameters on Springback for Ultra High Strength Steel Considering Young’s Modulus Variation in Cold Roll Forming, Journal of Manufacturing Processes, 29, 289–297.
TSANG K.S.,2017, Validation of a Finite Element Model of the Cold Roll Forming Process on the Basis of 3D Geometric Accuracy, Procedia Engineering, 207, 1278–1283.
LUO M., LUO H., 2018, A Wireless Instrumented Milling Cutter System with Embedded PVDF Sensors, Mechanical Systems and Signal Processing, 11, 556–568.
MIN S.H., LEE T.H., 2020, Directly Printed Low-Cost Nanoparticle Sensor for Vibration Measurement During Milling Process, MDPI Journal Materials 13, doi:10.3390/ma13132920.
MÖHRING H.C., 2016, Intelligent Tools for Predictive Process Control, Procedia CIRP, 57, 539–544.
CLAUß B., MEINECKE C.R., 2020, Process Monitoring and Impulse Detection in Face Milling Using Capacitive Acceleration Sensors Based on MEMS, Procedia CIRP, 93, 1454–1459.
UEKITA M., TAKAYA Y., 2017, Tool Condition Monitoring for Form Milling of Large Parts by Combining Spindle Motor Current and Acoustic Emission Signals, International Journal of Advanced Manufacturing Technology, 89, 65–75.
SIDDHPURA A., PAUROBALLY R., 2013, A Review of Flank Wear Prediction Methods for Tool Condition Monitoring in a Turning Process, Int. J. Adv. Manuf. Technol., 65, 371–393.
MAYR J., JEDRZEJEWSKI J., 2012, Thermal Issues in Machine Tools, CIRP Annals – Manufacturing Technology, 61, 771–791.
MONOSTORI L., 2014, Cyber-Physical Production Systems: Roots, Expectations and R&D Challenges, Procedia CIRP, 17, 9–13.
TETI R., 2010, Advanced Monitoring of Machining Operations, CIRP Annals – Manufacturing Technology, 59, 717–739.
SERIN G., 2020, Review of Tool Condition Monitoring in Machining and Opportunities for Deep Learning, The International Journal of Advanced Manufacturing Technology, 109, 953–974.
DENKENA B., 2020, Statistical Approaches for Semi‑Supervised Anomaly Detection in Machining, Production Engineering, 14, 385–393.
MÖHRING H.C., 2020, Process Monitoring with a Cyber-Physical Cutting Tool, Procedia CIRP, 93, 1466–1471.
KAUFMANN T., 2020, AI-Based Framework for Deep Learning Applications in Grinding, SAMI 2020 – IEEE 18th World Symposium on Applied Machine Intelligence and Informatics – January 23–25.
CAI Y., 2017, Sensor Data and Information Fusion to Construct Digital-Twins Virtual Machine Tools for Cyber-Physical Manufacturing, Procedia Manufacturing, 10, 1031–1042.
QIAOKANG L. 2016, Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining, Sensors, 16, 1926.
ZUPERL U., 2011, Neural Control Strategy of Constant Cutting Force System in End Milling, Robotics and Computer-Integrated Manufacturing, 27, 485–493.
TOMIYAMA T., MOYEN F., 2018, Resilient Architecture for Cyber-Physical Production Systems, CIRP Annals – Manufacturing Technology, 67, 161–164.
KUNATH M., WINKLER H., 2018, Integrating the Digital Twin of the Manufacturing System into a Decision Support System for Improving the Order Management Process, Procedia CIRP, 72, 225–231.
PAUKER F., 2018, Centurio Work – Modular Secure Manufacturing Orchestration, Proceedings of the Dissertation Award and Demonstration, Industrial Track at BPM 2018,, 2018.
IGLESIAS A., 2014, Optimization of Face Milling Operations with Structural Chatter Using a Stability Model Based Process Planning Methodology, Int. J. Adv. Manuf. Technol., 70, 559–571.
MÖHRING H.C., 2018, Material Failure Detection for Intelligent Process Control in CFRP Machining, Procedia CIRP, 77, 387–390.
KAUFMANN T., 2020, AI-based Framework for Deep Learning Applications in Grinding, SAMI 2020 – IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI). doi:10.1109/sami48414.2020.9108743.
DENKENA B., BOUJNAH H., 2018, Feeling Machines for Online Detection and Compensation of Tool Deflection in Milling, CIRP Annals – Manufacturing Technology, 67, 423–426.
SÖDERBERG R., 2017, Toward a Digital Twin for Real-Time Geometry Assurance in Individualized Production, CIRP Annals – Manufacturing Technology, 66, 137–140.
TONG X., 2019, Real‑Time Machining Data Application and Service Based on IMT Digital Twin, Journal of Intelligent Manufacturing, 31, 1113–1132.
OGORODYNK O., MARTINSEN K., 2018, Monitoring and Control for Thermoplastics Injection Molding A Review, Procedia CIRP, 67, 380–385.
AGEYEVA T., HORVATH S., KOVACS J. G., 2019, In-Mold Sensors for Injection Molding: On the Way to Industry 4.0, MDPI, Sensors, 19/16, 3551.
GAO Y., WANG X., 2007, An Effective Warpage Optimization Method in Injection Molding Based on the Kriging Model, The International Journal of Advanced Manufacturing Technology, 37, 953–960.
SUDSAWAT S., SRISEUBSAI W., 2018, Warpage Reduction Through Optimized Process Parameters and Annealed Process of Injection-Molded Plastic Parts, Journal of Mechanical Science and Technology, 32, 4787–4799.
KC B., FARUK O., AGNELLI J.A.M., LEAO A.L., 2016, Sisal-Glass Fiber Hybrid Biocomposite: Optimization of Injection Molding Parameters Using Taguchi Method for Reducing Shrinkage, Composites Part A, Applied Science and Manufacturing, 83, 152–159.
BARGHASH M., ALKAABNEH F. A., 2014, Shrinkage and Warpage Detailed Analysis and Optimization for the Injection Molding Process Using Multistage Experimental Design, Quality Engineering, 26/3, 319–334.
WANG J., MAO Q., 2012, A Novel Process Control Methodology Based on the PVT Behavior of Polymer for Injection Molding, Advances in Polymer Technology 32/S1, E474–E485.
ZHANG S., DUBAY R., CHAREST M., 2015, A Principal Component Analysis Model-Based Predictive Controller for Controlling Part Warpage in Plastic Injection Molding, Expert Systems with Applications, 42, 2919–2927.
PARK H. S., KUMAR S., 2019, AI Based Injection Molding Process for Consistent Product Quality, Procedia Manufacturing, 28, 102–106.
TELLAECHE A., ARANA R., 2013, Machine Learning Algorithms for Quality Control in Plastic Molding Industry, IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).
CHEN J.Y., YANG K.J., 2018, Online Quality Monitoring of Molten Resin in Injection Molding, International Journal of Heat and Mass Transfer, 122, 681–693.
NIAN S.C., FANG Y.C., HUANG M.S., 2019, In-Mold and Machine Sensing and Feature Extraction for Optimized IC-tray Manufacturing, Polymers, 11/8, 1348.
CHEN J.Y., TSENG C.C., HUANG M.S., 2019, Quality Indexes Design for Online Monitoring Polymer Injection Molding, Advances in Polymer Technology, 419, 1–20.
KUMAR S., PARK H.S., LEE C.M., 2020, Data-Driven Smart Control of Injection Molding Process, CIRP Journal of Manufacturing Science and Technology, 31, 439–449.
PACHER G.A., BERGER G.R., 2014, In-Mold Sensor Concept to Calculate Process-Specific Rheological Properties, AIP Conference Proceedings, 1593, 179.
OGORODYNK O, MARTINSEN K., 2019, Application of Feature Selection Methods for Defining Critical Parameters in Thermoplastics Injection Molding, Procedia CIRP, 81, 110–114.
KOZJEK D., KRALJ D., 2017, A Data-Driven Holistic Approach to Fault Prognostics in a Cyclic Manufacturing Process, Procedia CIRP, 63, 664–669.
CHEN J.Y., LIU C.Y., 2019, Tie-Bar Elongation Based Filling-To-Packing Switchover Control and Prediction of Injection Molding Quality, Polymers, 11/7, 1168.
ISO 20753: Plastics – Test specimens.
YANG M., 2018, Smart Metal Forming with Digital Process and IoT, International Journal of Lightweight Materials and Manufacture, 1, 207–214.
KIM S. Y., 2017, Detection of Abnormal Behavior in Manufacturing Processes Using Bolt Type Piezo-Sensor, Proceedings of the 68th Japanese Joint Conference for the Technology of Plasticity, 181–182.
HAGINO N., 2014, Propagation Behavior of Ultrasonic Wave Around Boundary Surfaces of Workpieces and Dies, Procedia Engineering, 81, 1073–1078.
YANG M., 2003, Data Fusion of Distributed AE Sensors for the Detection of Friction Sources During Press Forming, Journal of Materials Processing Technology, 139, 368–372.
MÜLLER C., 2014, Numerische Abbildung und Validierung von Beanspruchungsgrößen in Rollprofilier-prozessen, ISBN 978-3-8440-3255-0.
TRAUB T., MIKS C., GROCHE P., 2017, Force Measurements Supporting the Set-up Process in Roll Forming, Athens: ATINER'S Conference Paper Series, No: MEC2017-2346.
LINDGREN M., 2007, Experimental Investigations of the Roll Load and Roll Torque When High Strength Steel is Roll Formed, Journal of Materials Processing Technology, 191, 44–47.
LEONHARTSBERBER M., LAMPRECHT M., BLEICHER F., 2020, Influence Parameters on Tool Deflections in Roll Forming, Proceedings of the 31st DAAAM International Symposium, DOI: 10.2507/-st.daaam.
LAMPRECHT M., 2020, Nonlinear Mechanical Model of the Shaft of a Roll Forming Mill and Parameter Identification, The International Journal of Advanced Manufacturing Technology,
TRAUB T., 2019, Measures Towards Roll Forming at the Physical Limit of Energy Consumption, The Interna-tional Journal of Advanced Manufacturing Technology,
GROCHE P., 2013, Manufacturing and Use of Novel Sensoric Fasteners for Monitoring Forming Processes, Measurement, 53, 136–144.
LINDGREN M., 2009, Experimental and Computational Investigation of the Roll Forming Process, ISBN 978-91-7439-031-5.
NAJAFABADI M.H., 2018, Effect of Forming Parameters on Edge Wrinkling in Cold Roll Forming of Wide profiles, The International Journal of Advanced Manufacturing Technology,
PARALIKAS J., 2009, Investigation of the Effects of Main Roll-Forming Process Parameters on Quality for a V-Section Profile from AHSS, International Journal of Advanced Manufacturing Technology, 44, 223–237.
ROSSI B., 2013, Numerical Simulation of the Roll Forming of Thin-Walled Sections and Evaluation of Corner Strength Enhancement, Finite Elements in Analysis and Design, 72, 13–20.
TSANG K.S., 2017, Validation of a Finite Element Model of the Cold Roll Forming Process on the Basis of 3D Geometric Accuracy, Procedia Engineering, 207, 1278–1283.
WIEBENGA J.H., 2013, Product Defect Compensation by Robust Optimization of a Cold Roll Forming Process, Journal of Materials Processing Technology, 213, 978– 986.
TOTIS G., 2010, Development of a Dynamometer for Measuring Individual Cutting Edge Forces in Face Milling, Mechanical Systems and Signal Processing, 24, 1844–1857.
DROSSEL W.-G., 2018, Performance of a New Piezoceramic Thick Film Sensor for Measurement and Control of Cutting Forces During Milling, CIRP Annals – Manufacturing Technology, 67, 45–48.
MAIER W., 2018, Tools 4.0 – Intelligence Starts on the Cutting Edge, Procedia Manufacturing, 24, 299–304.
DOMOBOVARI Z., 2018, Milling Stability for Slowly Varying Parameters, Procedia CIRP 77, 110–113.
BINXUN L., 2019, Toward Understanding of Metallurgical Behaviours in Dry Machining of Hardened Steel: Phase Transformation and Surface Oxidation, Journal of Material Research and Technology, 8/5, 3811–821.
BLEICHER F., SCHÖRGHOFER P., HABERSOHN C., 2018, In-Process Control with a Sensory Tool Holder to Avoid Chatter, Journal of Machine Engineering, 18/3, 16–27.
SCHÖRGHOFER P., 2019, Using Sensory Tool Holder Data for Optimizing Production Processes, Journal of Machine Engineering, 19/3, 43–55.
BLEICHER F., 2020, Method for Determining Edge Chipping in Milling Based on Tool Holder Vibration Measurements, CIRP, 69, 101–104.
Journals System - logo
Scroll to top