A Combined Numerical and Experimental Investigation on Deterministic Deviations in Hot Forging Processes

Behrens, Bernd‐Arno; Volk, Wolfram; Maier, Daniel; Scandola, Lorenzo; Ott, Michael; Brunotte, Kai; Büdenbender, Christoph; Till, Michael

doi:10.1016/j.promfg.2020.04.231

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…As input data for the obtained geometry, CU RR k part in Fig. 1b, simulation results from the software Simufact Forging [35] are employed. In order to evaluate the outcome of the compensation, different scalar criteria representative of the deviations of the entire part are employed.…”

Section: Compensation Results and Methods Verificationmentioning

confidence: 99%

“…After successful verification of the compensation algorithm, the method is validated by conducting real tests and analysing experimental data [35]. The forging operations are carried out on a mechanical screw press "Lasco -SPR 500" in open-die forging configuration, the burr originating from the flange zone.…”

Section: Validation and Comparison Of Simulations And Experimental Rementioning

confidence: 99%

“…The forming temperature is 1250 • C and the tools are subjected to a pre-heating stage to achieve a starting temperature of 250 • C. An homogeneous temperature distribution in the billets is obtained by heating them for 15 minutes in a chamber furnace. Additional information about the performed tests and the forming simulations are available in [35]. The initial tools are produced from the effective contours of the nominal part.…”

Section: Validation and Comparison Of Simulations And Experimental Rementioning

confidence: 99%

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Development of a numerical compensation framework for geometrical deviations in bulk metal forming exploiting a surrogate model and computed compatible stresses

et al. 2021

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The optimal design of the tools in bulk metal forming is a crucial task in the early design phase and greatly affects the final accuracy of the parts. The process of tool geometry assessment is resource- and time-consuming, as it consists of experience-based procedures. In this paper, a compensation method is developed with the aim to reduce geometrical deviations in hot forged parts. In order to simplify the transition process between the discrete finite-element (FE) mesh and the computer-aided-design (CAD) geometry, a strategy featuring an equivalent surrogate model is proposed. The deviations are evaluated on a reduced set of reference points on the nominal geometry and transferred to the FE nodes. The compensation approach represents a modification of the displacement-compatible spring-forward method (DC-SF), which consists of two elastic FE analyses. The compatible stress originating the deviations is estimated and subsequently applied to the original nominal geometry. After stress relaxation, an updated nominal geometry of the part is obtained, whose surfaces represent the compensated tools. The compensation method is verified by means of finite element simulations and the robustness of the algorithm is demonstrated with an additional test geometry. Finally, the compensation strategy is validated experimentally.

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Section: Compensation Results and Methods Verificationmentioning

confidence: 99%

Section: Validation and Comparison Of Simulations And Experimental Rementioning

confidence: 99%

Section: Validation and Comparison Of Simulations And Experimental Rementioning

confidence: 99%

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Development of a numerical compensation framework for geometrical deviations in bulk metal forming exploiting a surrogate model and computed compatible stresses

et al. 2021

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“…Gained results finally were processed into hardness evolution curves (H = f(T,σ,n)), so that a realistic changes of hardness values can be estimated over the course of the forging operation. Process planners in industry do need such a function considering calculated process peak temperature T and comparative stress σ in conjunction with a specified number of forging cycles n. This function was successfully implemented into common FE codes by means of self-developed user subroutines and now can be directly applied to asses wear in more detail after a typical process calculation of a hot forging process [82].…”

Section: Tool Life Prediction In Hot Forging Determined By Digital Pr...mentioning

confidence: 99%

Perspectives on data-driven models and its potentials in metal forming and blanking technologies

Liewald

Bergs

Groche

et al. 2022

Prod. Eng. Res. Devel.

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Today, design and operation of manufacturing processes heavily rely on the use of models, some analytical, empirical or numerical i.e. finite element simulations. Models do reflect reality as best as their design and structure may appear, but in many cases, they are based on simplifying assumptions and abstractions. Reality in production, i.e. reflected by measures such as forces, deflections, travels, vibrations etc. during the process execution, is tremendously characterised by noise and fluctuations revealing a stochastic nature. In metal forming such kind of impact on produced product today in detail is neither explainable nor supported by the aforementioned models. In industrial manufacturing the game to deal with process data changed completely and engineers learned to value the high significance of information included in such digital signals. It should be acknowledged that process data gained from real process environments in many cases contain plenty of technological information, which may lead to increase efficiency of production, to reduce downtime or to avoid scrap. For this reason, authors started to focus on process data gained from numerous metal forming technologies and sheet metal blanking in order to use them for process design objectives. The supporting idea was found in a potential combination of conventional process design strategies with new models purely based on digital signals captured by sensors, actuators and production equipment in general. To utilise established models combined with process data, the following obstacles have to be addressed: (1) acquired process data is biased by sensor artifacts and often lacks data quality requirements; (2) mathematical models such as neural networks heavily rely on high quantities of training data with good quality and sufficient context, but such quantities often are not available or impossible to gain; (3) data-driven black-box models often lack interpretability of containing results, further opposing difficulties to assess their plausibility and extract new knowledge. In this paper, an insight on usage of available data science methods like feature-engineering and clustering on metal forming and blanking process data is presented. Therefore, the paper is complemented with recent approaches of data-driven models and methods for capturing, revealing and explaining previously invisible process interactions. In addition, authors follow with descriptions about recent findings and current challenges of four practical use cases taken from different domains in metal forming and blanking. Finally, authors present and discuss a structure for data-driven process modelling as an approach to extent existing data-driven models and derive process knowledge from process data objecting a robust metal forming system design. The paper also aims to figure out future demands in research in this challenging field of increasing robustness for such kind of manufacturing processes.

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“…During stretching of shaft forgings, geometry of the formed workpieces deviates from the desired target geometry, due to complex interactions between tools and billets which result in inhomogeneous temperature and stress fields. Therefore, the development of forging tools requires an iterative adaptation process through a large number of revisions in the tool geometry, which escalates the resulting costs [4]. For example, end-face dents could appear, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Research on the End Surface Dent of the Main Shaft Forging

Niu

Qian³

et al. 2021

Teh. vjesn.

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In the process of the stretching of the shaft forgings, if the process parameters are not properly selected, the end-face dent will take place. The end-face dent affects the performance of large forgings and leads to much material wasting. Finite element method was employed to perform numerical simulation of the stretching of a main shaft with an upper flat anvil and a lower V-shaped anvil. The orthogonal test table was designed by selecting the anvil width, the Reduction ratio and the feed as influencing factors. Accordingly, simulations were carried out to solve the end-face dent values under different parameter combinations. The analysis showed that the optimal parameter combination gave an anvil width ratio of 0.75, a Reduction ratio of 0.2, and an initial feed of 300 mm. Through extremum difference analysis, it was found that among the three factors are the feed, the reduction ratio, and the anvil width ratio in the decreasing order of the influence on the end-face dent. Comprehensive analysis showed that when the anvil is relatively narrow, increasing the relative feed can reduce the end-surface dent remarkably. It is advisable that during the stretching of shaft forgings with a flat upper anvil and a V-shaped lower anvil, the combination of the anvil width ratio of 0.75, the reduction ratio of 0.2, and increasing the feed can reduce the end-face dent, thereby reducing the end cutting and saving material costs.

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A Combined Numerical and Experimental Investigation on Deterministic Deviations in Hot Forging Processes

Cited by 7 publications

References 7 publications

Development of a numerical compensation framework for geometrical deviations in bulk metal forming exploiting a surrogate model and computed compatible stresses

Development of a numerical compensation framework for geometrical deviations in bulk metal forming exploiting a surrogate model and computed compatible stresses

Perspectives on data-driven models and its potentials in metal forming and blanking technologies

Research on the End Surface Dent of the Main Shaft Forging

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