A cost effective approach to enhance surface integrity and fatigue life of precision milled forming and forging dies

Previously held under moratorium from 8 August 2019 until 19 January 2022The machining process determines the overall quality of produced forming and forging dies, including surface integrity. Previous research found that surface integrity has a significant influence on the fatigue life of the dies. This thesis aims to establish a cost-effective approach for precision milling to obtain forming and forging dies with good surface integrity and long fatigue life. It combined experimental study accompanied by Finite Element Modelling and Artificial Intelligence soft modelling to predict and enhance forming and forging die life. Four machining parameters, namely Surface Speed, Depth of cut, Feed Rate and Tool Lead Angle, each with five levels, were investigated experimentally using Design of Experiment. An ANOVA analysis was carried out to identify the key factor for every Surface Integrity (SI) parameter and the interaction of every factor. It was found that the cutting force was mostly influenced by the tool lead angle. The residual stress and microhardness were both significantly influenced by the surface speed. However, on the surface roughness it was found that the feed rate had the most influence. After the machining experiments, four-point bending fatigue tests were carried out to evaluate the fatigue life of precision milled parts at an elevated temperature in a low cycle fatigue set-up imitated for the forming and forging production. It was found that surface roughness and hardness were the most influential factors for fatigue life. A 3D-FE-Modelling framework including a new material model subroutine was developed; this led to a more comprehensive material model. A fractional factorial simulation with over 180 simulations was carried out and validated with the machining experiment. Based on the experimental and simulation results, a soft prediction model for surface integrity was established by using Artificial Neural Networks (ANN) approach. These predictions for SI were then used in a Genetic Algorithm model to optimise the SI. The confirmation tests showed that the machining strategy was successfully optimised and the average fatigue duration was increased by at least a factor of two. It was found that a surface speed of 270 m/min, a feed rate of 0.0589 mm/tooth, a depth of cut of 0.39 mm and a tool lead angle of 16.045° provided the good surface integrity and increased fatigue performance. Overall, these findings conclude that the fundamentals and methodology utilised have developed a further understanding between machining and forming/forging process, resulting in a good foundation for a framework to generate FE and soft prediction models which can be used to in optimisation of precision milling strategy for different materials.The machining process determines the overall quality of produced forming and forging dies, including surface integrity. Previous research found that surface integrity has a significant influence on the fatigue life of the dies. This thesis aims to establish a cost-effective approach for precision milling to obtain forming and forging dies with good surface integrity and long fatigue life. It combined experimental study accompanied by Finite Element Modelling and Artificial Intelligence soft modelling to predict and enhance forming and forging die life. Four machining parameters, namely Surface Speed, Depth of cut, Feed Rate and Tool Lead Angle, each with five levels, were investigated experimentally using Design of Experiment. An ANOVA analysis was carried out to identify the key factor for every Surface Integrity (SI) parameter and the interaction of every factor. It was found that the cutting force was mostly influenced by the tool lead angle. The residual stress and microhardness were both significantly influenced by the surface speed. However, on the surface roughness it was found that the feed rate had the most influence. After the machining experiments, four-point bending fatigue tests were carried out to evaluate the fatigue life of precision milled parts at an elevated temperature in a low cycle fatigue set-up imitated for the forming and forging production. It was found that surface roughness and hardness were the most influential factors for fatigue life. A 3D-FE-Modelling framework including a new material model subroutine was developed; this led to a more comprehensive material model. A fractional factorial simulation with over 180 simulations was carried out and validated with the machining experiment. Based on the experimental and simulation results, a soft prediction model for surface integrity was established by using Artificial Neural Networks (ANN) approach. These predictions for SI were then used in a Genetic Algorithm model to optimise the SI. The confirmation tests showed that the machining strategy was successfully optimised and the average fatigue duration was increased by at least a factor of two. It was found that a surface speed of 270 m/min, a feed rate of 0.0589 mm/tooth, a depth of cut of 0.39 mm and a tool lead angle of 16.045° provided the good surface integrity and increased fatigue performance. Overall, these findings conclude that the fundamentals and methodology utilised have developed a further understanding between machining and forming/forging process, resulting in a good foundation for a framework to generate FE and soft prediction models which can be used to in optimisation of precision milling strategy for different materials

Efficiency in contamination-free machining using microfluidic structures

The plastic deformation of the material in the chip formation and the friction when the chip slides on the rake face of the insert generate heat. The heat generation is responsible for a temperature rise of the chip, of the insert and of the newly created surface on the workpiece. Adhesion and diffusion between the chip and the insert are thus facilitated with detrimental effects on the tool wear. A cooling system based on microfluidic structures internal to the insert is considered in this study as a means of controlling the temperature at the chip–insert interface. The coolant and the part never enter in contact. Hence contamination of the part by coolant molecules is prevented. The aim of this study is to identify and to quantify the effect of the cutting parameters on the effectiveness of the internal cooling system. To measure this effectiveness an efficiency ratio r is defined as the percentage of the mechanical power actually needed at the tool to remove material that is thermally dissipated by the internal flow of the coolant. Similarly, a specific efficiency ratio r′ is also defined by considering the mechanical power per volume flow rate of the material removed and the dissipated thermal power per volume flow rate of the coolant. Both r and r′ are then analysed in a 33 factorial experiment within the space of the technological variables depth of cut, feed rate and cutting speed. The cutting trials were conducted in turning operations of AA6082-T6 aluminium alloy. Linear mixed-effects models were fitted to the experimental results using the maximum likelihood method. The main finding was that the efficiency ratio r depends only on the feed rate and the cutting speed but not on the depth of cut. An interaction effect of the feed rate and the cutting speed on the efficiency was also found significant. Higher efficiency is attainable by decreasing cutting speed and feed rate. The maximum efficiency predicted in the technological region investigated was 10.96 %. The specific efficiency once log-transformed was found linearly increasing with the depth of cut and the feed rate, whereas being insensitive to the cutting speed

A statistics based Digital Twin for the combined consideration of heat treatment and machining for predicting distortion

This paper introduces a novel concept of Digital Twinning of heat treatment and machining for predicting distortion. A set of physical experiments were conducted, and statistical models based on these trials were created. The experiments involved heat-treating AA7075 billets with multiple input conditions and measuring distortion during machining trials. This trained a Gaussian Process machining model to reproduce the real-life behaviour of a part, and to predict distortions. These predictions matched the shape and magnitude of data points of the trials. The paper suggests further refinements of the model. The developed statistical tool enables distortion prediction to produce right-first-time parts

Conference on Thermal Issues in Machine Tools: Proceedings

Inhomogeneous and changing temperature distributions in machine tools lead to sometimes considerable quality problems in the manufacturing process. In addition, the switching on and off of aggregates, for example, leads to further fluctuations in the temperature field of machine tools. More than 100 specialists discussed these and other topics from the field of thermal research at the 1st Conference on Termal Issues in Machine Tools in Dresden from 22 to 23 March.:Efficient modelling and computation of structure-variable thermal behavior of machine tools S. Schroeder, A. Galant, B. Kauschinger, M. Beitelschmidt Parameter identification software for various thermal model types B. Hensel, S. Schroeder, K. Kabitzsch Minimising thermal error issues on turning centre M. Mareš, O. Horejš, J. Hornych The methods for controlled thermal deformations in machine tools A. P. Kuznetsov, H.-J. Koriath, A.O. Dorozhko Efficient FE-modelling of the thermo-elastic behaviour of a machine tool slide in lightweight design C. Peukert, J. Müller, M. Merx, A. Galant, A. Fickert, B. Zhou, S. Städtler, S. Ihlenfeldt, M. Beitelschmidt Development of a dynamic model for simulation of a thermoelectric self-cooling system for linear direct drives in machine tools E. Uhlmann, L. Prasol, S.Thom, S. Salein, R. Wiese System modelling and control concepts of different cooling system structures for machine tools J. Popken, L. Shabi, J. Weber, J. Weber The electric drive as a thermo-energetic black box S. Winkler, R. Werner Thermal error compensation on linear direct drive based on latent heat storage I. Voigt, S. Winkler, R. Werner, A. Bucht, W.-G. Drossel Industrial relevance and causes of thermal issues in machine tools M. Putz, C. Richter, J. Regel, M. Bräunig Clustering by optimal subsets to describe environment interdependencies J. Glänzel, R. Unger, S. Ihlenfeldt Using meta models for enclosures in machine tools F. Pavliček, D. P. Pamies, J. Mayr, S. Züst, P. Blaser, P. Hernández-Becerro, K. Wegener Model order reduction of thermal models of machine tools with varying boundary conditions P. Hernández-Becerro, J. Mayr, P. Blaser, F. Pavliček, K. Wegener Effectiveness of modelling the thermal behaviour of the ball screw unit with moving heat sources taken into account J. Jedrzejewski, Z. Kowal, W. Kwasny, Z. Winiarski Analyzing and optimizing the fluidic tempering of machine tool frames A. Hellmich, J. Glänzel, A. Pierer Thermo-mechanical interactions in hot stamping L. Penter, N. Pierschel Experimental analysis of the heat flux into the grinding tool in creep feed grinding with CBN abrasives C. Wrobel, D. Trauth, P. Mattfeld, F. Klocke Development of multidimensional characteristic diagrams for the real-time correction of thermally caused TCP-displacements in precise machining M. Putz, C. Oppermann, M. Bräunig Measurement of near cutting edge temperatures in the single point diamond turning process E. Uhlmann, D. Oberschmidt, S. Frenzel, J. Polte Investigation of heat flows during the milling processes through infrared thermography and inverse modelling T. Helmig, T. Augspurger, Y. Frekers, B. Döbbeler, F. Klocke, R. Kneer Thermally induced displacements of machine tool structure, tool and workpiece due to cutting processes O. Horejš, M. Mareš, J. Hornych A new calibration approach for a grey-box model for thermal error compensation of a C-Axis C. Brecher, R. Spierling, M. Fey Investigation of passive torque of oil-air lubricated angular contact ball bearing and its modelling J. Kekula, M. Sulitka, P. Kolář, P. Kohút, J. Shim, C. H. Park, J. Hwang Cooling strategy for motorized spindle based on energy and power criterion to reduce thermal errors S. Grama, A. N. Badhe, A. Mathur Cooling potential of heat pipes and heat exchangers within a machine tool spindleo B. Denkena, B. Bergman, H. Klemme, D. Dahlmann Structure model based correction of machine tools X. Thiem, B. Kauschinger, S. Ihlenfeldt Optimal temperature probe location for the compensation of transient thermal errors G. Aguirre, J. Cilla, J. Otaegi, H. Urreta Adaptive learning control for thermal error compensation on 5-axis machine tools with sudden boundary condition changes P. Blaser, J. Mayr, F. Pavliček, P. Hernández-Becerro, K. Wegener Hybrid correction of thermal errors using temperature and deformation sensors C. Naumann, C. Brecher, C. Baum, F. Tzanetos, S. Ihlenfeldt, M. Putz Optimal sensor placement based on model order reduction P. Benner, R. Herzog, N. Lang, I. Riedel, J. Saak Workpiece temperature measurement and stabilization prior to dimensional measurement N. S. Mian, S. Fletcher, A. P. Longstaff Measurement of test pieces for thermal induced displacements on milling machines H. Höfer, H. Wiemer Model reduction for thermally induced deformation compensation of metrology frames J. v. d. Boom Local heat transfer measurement A. Kuntze, S. Odenbach, W. Uffrecht Thermal error compensation of 5-axis machine tools using a staggered modelling approach J. Mayr, T. Tiberini. P. Blaser, K. Wegener Design of a Photogrammetric Measurement System for Displacement and Deformation on Machine Tools M. Riedel, J. Deutsch, J. Müller. S. Ihlenfeldt Thermography on Machine Tools M. Riedel, J. Deutsch, J. Müller, S. Ihlenfeldt Test piece for thermal investigations of 5-axis machine tolls by on-machine measurement M. Wiesener. P. Blaser, S. Böhl, J. Mayr, K. Wegene

Surface integrity evaluation and the effect of machining-induced surface integrity characteristics on part's performance

Surface integrity (SI) is the integrated surface behavior and condition of a material after being modified by a manufacturing process; it describes the influence of surface properties and characteristics upon material functional performance. As the leading-edge field of manufacturing research, SI finishing/machining and the consequent machining-induced complex combination of surface roughness, residual stress, work-hardening, macro and microstructure transformation, strongly affect the fatigue and stress behavior of machined parts. This kind of influence is particularly sensitive and pronounced in the difficult-to-machine materials, which are typically chosen for the most critical applications in the automobile, aerospace and nuclear industry. Thus, well-designed SI processing requirement and accurate SI evaluation model are essential to control and ensure the surface quality and functional performance for these key parts. In this thesis, an SI descriptive model for quantitative characterization and evaluation of surface integrity is proposed based on five principal SI characteristics. Considering the nature of surface integrity, a conceptual framework of an SI model for machined parts is established, in which the SI model is constructed based on the correlations between SI manufacturing processes, SI characteristics and final functionality. This model offers a theoretical basis and guideline for controlling SI characteristics and improving fatigue properties for machined parts. An empirical model for estimating the SI-characteristics-caused effective stress concentration factor (SCF) is established with fatigue life as the evaluating indicator. For a typical difficult-to-machine material, GH4169 superalloy, usually used in internal combustion engines, its grindability and the influence of processing parameters on the five principal SI characteristics are investigated in detail. The correlations between the processing parameters and the SI characteristics, between the processing parameters and the fatigue properties, and between the SI characteristics and the fatigue properties, are analyzed based on an orthogonally-designed grinding experiment and corresponding rotary bending fatigue testing for GH4169 samples within the selective range of grinding processing parameters. The feasibility and effectiveness of the proposed model for estimating the SI effective SCF are also validated by the experimental results, and this has actually offered an equivalent and convenient means for evaluation of SI and fatigue properties. Finally, the conclusions and contribution of the research are discussed, and potential future work to build on this research is identified