Potential of Tool Clamping Surfaces in Forming Machines for Cognitive Production
 
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1
Department Machine Tool, Fraunhofer Institute for Machine Tools and Forming Technology IWU, Germany
2
Production systems and factory automation, Fraunhofer Institute for Machine Tools and Forming Technology IWU, Germany
3
Chair of Machine Tools Development and Adaptive Controls, Dresden University of Technology TUD, Germany
CORRESPONDING AUTHOR
Mohaned Alaluss   

Department Machine Tool, Fraunhofer Institute for Machine Tools and Forming Technology IWU, Reichenhainer Strasse 88, 09126, Chemnitz, Germany
Submission date: 2022-02-15
Final revision date: 2022-04-21
Acceptance date: 2022-04-24
Online publication date: 2022-05-04
 
Journal of Machine Engineering 2022;22(3)
 
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ABSTRACT
High reproducibility of forming processes along with high quality expectations of the resulting formed parts demand cognitive production systems. The prerequisite is process transparency, which can be improved by increased knowledge of interdependencies between forming tool and forming machine that affects the tool clamping interface behavior. Due to the arrangement as surfaces transmitting process forces, their closeness to the forming process, and yet machine inherent, tool clamping interface provide greater potential for intelligent monitoring. This paper presents a holistic analysis of the interdependencies at the tool clamping interface. Here, the elastic deflection behavior of the press table and slide with their related clamping surfaces, the frictional slip behavior between the interacting machine components and the used clamping devices are described on qualitative level and verified by simulative analysis. Based on the results, available sensor systems are assessed regarding the capability to monitor the identified phenomena inline.
 
REFERENCES (19)
1.
BEHRENS B.A., JAVADI M., 2009, Exakte und Kostengünstige Qualitätskontrolle an Pressen in der Blechverarbeitungsindustrie, UTF Science, Bamberg.
 
2.
DOEGE E., SCHOMAKER K.-H., BRENDEL T., 1992, Sensors and Diagnostic Systems in Forming Machines, CIRP Annals 41/1, 323–336.
 
3.
ZORN W., HAMM L., ELSNER R., DROSSEL W.-G., 2019, Potential of the Force Distribution Measurement in Deep Drawing Processes for Increasing the Process Quality, Int. J. Mech. Eng. Robot. Res. 8/3, 449–453.
 
4.
IHLENFELDT S., RIEDEL M., WABNER M., TEHEL R., TISZTL M., FISCHER J., 2014, Novel Maintenance Support System for Frame Components of Forming Presses, European Congress & Expo on Maintenance and Asset Management (EuroMaintenance), Helsinki, 471–476.
 
5.
PIERER A., WIENER T., GJAKOVA L., KOZIOREK J., 2021, Zero-Error-Production Through Inline-Quality Control of Presshardened Automotive Parts by Multi-Camera Systems, IOP Conf. Ser., Mater. Sci. Eng. 1157 012074.
 
6.
BIEHL S., RUMPOSCH C., PAETSCH N., BRÄUER G., WEISE D., SCHOLZ P., LANDGREBE D., 2016, Multifunctional Thin Film Sensor System as Monitoring System in Production, Microsyst. Technol., 22, 1757–1765.
 
7.
PILTHAMMAR J., SIGVANT M., HANSSON M., PALSSON E., RUTGERSSON W., 2017, Characterizing the Elastic Behaviour of a Press Table Through Topology Optimization, IOP Conf. Series, Journal of Physics, 896, 012068.
 
8.
KURTH R., BERGMANN M., TEHEL R., DIX M., PUTZ M., 2021, Cognitive Clamping Geometries for Monitoring Elastic Deformation in Forming Machines and Processes, CIRP Annals Manufacturing Technology, 70/1, 235–238.
 
9.
TEHEL R., PÄßLER T., MIHM M., 2019, Modeling Elastic Behavior of Forming Machine Components to Reduce Tool Manufacturing Time, Procedia Manufacturing, 27, 177–184.
 
10.
TEHEL R., PÄßLER T., BERGMANN M., 2020, Effective FE Models for Simulating the Elastomechanical Characteristics of Forming Machines, Int. J. Adv. Manuf. Technol., 58/106, 3505–3514.
 
11.
PILTHAMMAR J., SIGVANT M., KAO-WALTER S., 2018, Introduction of Elastic Die Deformations in Sheet Metal Forming Simulations, International Journal of Solids and Structures, 151, 76–90.
 
12.
GROCHE P., HOPPE F., SINZ J., 2017, Stiffness of Multipoint Servo Presses: Mechanics vs. Control, CIRP Annals, 66/1, 373–376.
 
13.
KURTH R., TEHEL R., PÄßLER T., PUTZ M., WEHMEYER K., KRAFT C., SCHWARZE H., 2019, Forming 4.0: Smart Machine Components Applied as a Hybrid Plain Bearing and a Tool Clamping System, Procedia Manufacturing, 27, 65–71.
 
14.
KUBIK C., KNAUER S.M., GROCHE P., 2021, Smart Sheet Metal Forming: Importance of Data Acquisition, Preprocessing and Transformation on the Performance of a Multiclass Support Vector Machine for Predicting Wear States During Blanking, Journal of Intelligent Manufacturing, 23/5, 1489–1513.
 
15.
GESELLSCHAFT FÜR OPTISCHE MESSTECHNIK, 2010, Application Note: 3D Motion Analysis: Optical Measuring Technology for Dynamic Analysis of Press Machines, https://www.gom.com/fileadmin/... industries/forming_machine_EN.pdf, Accessed on: 01 Nov. 21.
 
16.
PILTHAMMAR J., SKARE T., GALDOS L., FRÖJDH K., 2021, New Press Deflection Measuring Methods for the Creation of Substitutive Models for Efficient Die Cambering, IOP Conf. Ser., Mater. Sci. Eng. 1157 012076.
 
17.
STRUCK R., 2010, Bestimmung der minimal notwendigen Pressenkraft zur Herstellung von Karosseriestruktur bauteilen im Automobilbau, PhD thesis, Leibnitz University, Hannover.
 
18.
DROSSEL W.-G., ZORN W., HAMM L., 2019, Modular System to Measure and Control the Force Distribution in Deep Drawing Processes to Ensure Part Quality and Process Reliability, CIRP Annals Manufacturing Technology, 68/1, 309–312.
 
19.
TESSARI K., 2016, messQUADER mQ 5013.01. Der Allrounder zur Prozesskontrolle, https://www.unidor.info/ assets/messquader-mq-5013.01.pdf, Accessed on: 08 Dec. 21.
 
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