Survey of the development of petro-forge forming machines

Tobias, S. A.

doi:10.1016/0020-7357(85)90091-5

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…Similar results, concerning the evolution of the efficiency in simulation, can be found in the literature. Indeed, Tobias [4] highlights the decrease of efficiency with a decrease in the billet height, for a hammer-forging machine. Blows with small load and large billet deformation are qualified as soft blows and the associated efficiency is high, between 80 and 90%.…”

Section: Prediction Of Consecutive Blows For the Upsetting Of An Aluminium Billetmentioning

confidence: 99%

“…This implies that the instrumentation of forging machines is difficult, which is why theoretical models are used to describe dynamic machine behaviour. Tobias [4] developed a one degree of freedom model with one mass and one spring to represent hammer behaviour and Vajpayee et al [5] proposed a model comprising one spring and two masses to describe hammer-forging operations. The masses and the stiffness were obtained from the machine specifications and the machine model was coupled with the billet behaviour.…”

Section: Introductionmentioning

confidence: 99%

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A new tailored solution to predict blow efficiency and energy consumption of hammer-forging machines

Mull

Durand

Baudouin

et al. 2020

Int J Adv Manuf Technol

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Energy saving has become a real issue in process management and has led to the analysis of the energy consumption of forging machines, for the purpose of optimization. This study focuses on the amount of energy lost due to machine behaviour during the forming process. A spring-mass-damping model of the machine and tools system is associated with a billet model to describe hammer-forming operations only during the forging phase. The model parameters are identified with experimental measurements of process variables during a stroke, providing parameters specifically adapted to the machine-tools system. Then, model predictions are validated by the experimental upsetting of steel and aluminium billets. The model accurately predicts forging process variables for consecutive blows with different materials. The decrease in process efficiency and the evolution from inelastic to elastic blows after several strokes on the same billet are also predicted by the model. This methodology provides a new, tailored solution that enables forging manufacturers to predict the forging energy consumption of their own machines. The approach developed in this work concerns gravity drop hammers but is also transferable to other energy-driven machines.Jean-François Mulljeanfrancois.mull@ens am.eu

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Section: Prediction Of Consecutive Blows For the Upsetting Of An Aluminium Billetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new tailored solution to predict blow efficiency and energy consumption of hammer-forging machines

Mull

Durand

Baudouin

et al. 2020

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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“…In principle, the specification of the strain rate is important in these processes, but a single scalar value is insufficient for the description because strains and strain rates vary temporally and locally. Specifically, electrically stored energy [7], chemically stored energy [8] and mechanically stored energy [9] can be used for realizing high-speed manufacturing processes (i.e., forming, joining, and cutting operations). The industry is currently taking strong interest especially in shaping, cutting, and joining by means of electromagnetic forming (EMF) and in cutting with accelerated tools.…”

Section: High-speed Technologiesmentioning

confidence: 99%

“…Advantages compared to conventional presses are that presses for forming and cutting with accelerated tools are compact, require relatively low investment costs, do not require elaborate foundations, and enable high production rates [9]. As an alternative to special high-speed machines, Yaldiz et al developed an accelerator unit that can be used to adapt conventional presses to high-speed forming and separation processes [24].…”

Section: High-speed Technologiesmentioning

confidence: 99%

Determination of Material and Failure Characteristics for High-Speed Forming via High-Speed Testing and Inverse Numerical Simulation

Psyk

Scheffler

Tulke

et al. 2020

JMMP

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In conventional forming processes, quasi-static conditions are a good approximation and numerical process optimization is the state of the art in industrial practice. Nevertheless, there is still a substantial need for research in the field of identification of material parameters. In production technologies with high forming velocities, it is no longer acceptable to neglect the dependency of the hardening on the forming speed. Therefore, a method for determining material characteristics in processes with high forming speeds was developed by designing and implementing a test setup and an inverse parameter identification. Two acceleration concepts were realized: a pneumatically driven one and an electromagnetically driven one. The method was verified for a mild steel and an aluminum alloy proving that the identified material parameters allow numerical modeling of high-speed processes with good accuracy. The determined material parameters for steel show significant differences for different stress states. For specimen geometries with predominantly uniaxial tensile strain at forming speeds in the order of 10 4 -10 5 /s the determined yield stress was nearly twice as high compared to shear samples; an effect which does not occur under quasi-static loading. This trend suggests a triaxiality-dependent rate dependence, which might be attributed to shear band induced strain localization and adiabatic heating.Despite these advantages, the corresponding technologies, which are often referred to as high-speed forming, impulse forming or high-energy-rate-forming (HERF) [2] have not yet achieved a great industrial breakthrough. Reasons for this are lacking process knowledge and reliable process design strategies. When considering conventional forming processes, it is possible to assume a close approximation of quasi-static conditions, and numerical process design and optimization as state of the art. By contrast, the analysis and design of manufacturing processes with high forming speeds has hitherto been strongly experimental. Numerical considerations are helpful to investigate fundamental relationships, but often only qualitative interpretation is possible. One reason for this is that material parameters are hardly available at the process-specific high strain rates in the range of 10 s −1 up to 10 5 s −1 or even more. The identification of these parameters is complicated, because in addition to the influencing factors known from quasi-static conditions such as temperature and strain, the plastic flow and failure behavior of many materials strongly depends on the strain rate. Therefore, neglecting forming heat, as well as a strain rate-dependent hardening is no longer acceptable. Providing a method for determining reliable material and failure characteristics for the simulation of high-velocity forming, cutting, and joining processes will therefore contribute to making technological, economic, and ecological advantages of these processes exploitable in industrial production.

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“…This is due to energy losses caused by the dynamic behavior of the machines. That is why some dynamic models of forging machines were developed: first, models with masses and springs for gravity drop hammer [1,2] and then models with dampers in parallel with springs [3,4]. For these models, the parameters were theoretically identified based on machine specifications.…”

Section: Introductionmentioning

confidence: 99%

Parametric identification on a dynamic behavior model for a forging machine

Durand

2023

Materials Research Proceedings

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Abstract. Dynamic models using masses, dampers and springs have been developed to represent the behavior of energy piloted machines. These models were proven to estimate more accurately the required amount of energy to forge a part. But how to identify the parameters of the model to accurately represent the behavior of a specific machine? In this study, the case of a strike without billet on a screw press is investigated, different target functions are tested to identify the model parameters and a sensitivity study of the optimization is performed. Results are encouraging.

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Survey of the development of petro-forge forming machines

Cited by 12 publications

References 20 publications

A new tailored solution to predict blow efficiency and energy consumption of hammer-forging machines

A new tailored solution to predict blow efficiency and energy consumption of hammer-forging machines

Determination of Material and Failure Characteristics for High-Speed Forming via High-Speed Testing and Inverse Numerical Simulation

Parametric identification on a dynamic behavior model for a forging machine

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