Tool Wear and Life Span Variations in Cold Forming Operations and Their Implications in Microforming

Jarfors, Anders E.W.; Castagne, Sylvie; Danno, Atsushi; Zhang, Xiaodong

doi:10.3390/technologies5010003

Cited by 11 publications

(5 citation statements)

References 53 publications

(94 reference statements)

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“…The energy balance provided by the drive-wheel exceeds the energy needed to deform the material and counteract frictional losses and the energy absorbed by the press frame. In this sense, a proper selection of the forging energy for each configuration will avoid overloads and tool wear that will reduce the lifespan of the press [22]. An excessive energy could promote formation of flash, deformation of the dies or hammer, excessive bouncing of the bundle and early fracture of different components of the press.…”

Section: Resultsmentioning

confidence: 99%

Short-Cut Method to Assess a Gross Available Energy in a Medium-Load Screw Friction Press

Egea¹,

Deferrari²,

Abate³

et al. 2018

Metals

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The present study proposed a rapid method, based on a previous universal compression tests, to estimate the required load capacity to cold forge different specimen quantity in a screw press. Accordingly, experimental and theoretical approach are performed to check new adjustable drive motor of the modified forging machine to achieve a gross available energy to deform the specimens preventing damage of the forging machine. During the forging experiments, two screw friction presses (as-received and modified) are used to validate the theoretical approach. The modified press exhibits an increase of 51% of gross energy and 11% of maximum load capacity compare to the as-received press. This method is used to improve the effective of the forging process avoiding excessive loads that could promote machine failure. Therefore, a low-cost and easy to implement methodology is proposed to determine the energy and load capacity of a screw friction press to forge different specimen quantities with symmetry pattern configurations.

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Section: Resultsmentioning

confidence: 99%

Short-Cut Method to Assess a Gross Available Energy in a Medium-Load Screw Friction Press

Egea¹,

Deferrari²,

Abate³

et al. 2018

Metals

View full text Add to dashboard Buy / Rent full text

show abstract

“…By adding this parameter to the acceptable wear depth and the node threshold, a more easily comprehensible wear volume can be calculated to determine the end of tool life. Following the argumentation of Jafors et al [ 25 ], the tool life of a single die must always be considered as a single data point which is subject to strong fluctuations in the industrial manufacturing process [ 28 ]. Against this background, the calculated tool life represents a statistical mean value which can only be compared to a statistical evaluation of the lifetime over a longer period of time.…”

Section: Discussionmentioning

confidence: 99%

“…Since for each process cycle, a calculation loop is performed at each node as shown in Figure 6 , the calculation of the lifetime of forging tools, which in reality can reach typical lifetimes of around 10,000 parts [ 25 ], quickly leads to a significant increase of the calculation time. For this reason, a cycle time-scaling, which is comparable to known techniques like mass- and time-scaling [ 26 ], is implemented.…”

Section: Methodsmentioning

confidence: 99%

Multi-Layer Wear and Tool Life Calculation for Forging Applications Considering Dynamical Hardness Modeling and Nitrided Layer Degradation

et al. 2020

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As one of the oldest shaping manufacturing processes, forging and especially hot forging is characterized by extreme loads on the tool. The thermal load in particular is able to cause constant changes in the hardness of the surface layer, which in turn has a decisive influence on the numerical estimation of wear. Thus, also during numerical wear, modeling hardness changes need to be taken into account. Within the scope of this paper, a new implementation of a numerical wear model is presented, which, in addition to dynamic hardness models for the base material, can also take into account the properties of a nitride wear protection layer as a function of the wear depth. After a functional representation, the new model is applied to the wear calculation of a multi-stage industrial hot forging process. The applicability of the new implementation is validated by the evaluation of the occurring hardness, wear depths and the locally associated removal of the wear protection layer. Consecutively, a tool life calculation module based on the calculated wear depth is implemented and demonstrated. In general, a good agreement of the results is achieved, making the model suitable for detailed 2D as well as large 3D Finite Element calculations.

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“…The large scale production of forgings until today severely do suffer under unavoidable wear and structural damage due to extreme hot forging process conditions such as high cyclical mechanical as well as thermo-mechanical load applied onto active surface of forging tool. Subsequently the service life of the tools in hot forging is typically subjected to significant fluctuations [76] as depicted in Fig. 5.…”

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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Tool Wear and Life Span Variations in Cold Forming Operations and Their Implications in Microforming

Cited by 11 publications

References 53 publications

Short-Cut Method to Assess a Gross Available Energy in a Medium-Load Screw Friction Press

Short-Cut Method to Assess a Gross Available Energy in a Medium-Load Screw Friction Press

Multi-Layer Wear and Tool Life Calculation for Forging Applications Considering Dynamical Hardness Modeling and Nitrided Layer Degradation

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

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