Adhesive tool wear in the cold roll forming process

Galakhar, Alexander S.; Gates, J.D.; Daniel, William J.T.; Meehan, Paul A.

doi:10.1016/j.wear.2011.05.047

Cited by 15 publications

(8 citation statements)

References 32 publications

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“…Qualitative visual assessment is often used to determine the severity of wear on tooling and parts, and remains the most ubiquitous and effective method for characterising and identifying the severity of galling wear. The effectiveness of visual assessment has led to its use in numerous wear studies, often in addition to other quantitative measures [7,8,[10][11][12][13][14][15][16][17][18][19][20][21]. Due to the difficulty of assessing tooling during forming operations visual assessment of formed parts is a primary method used in industrial applications for determining if tool maintenance is required [22].…”

Section: Galling Wear Measurementmentioning

confidence: 99%

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A new methodology for measuring galling wear severity in high strength steels

Voss

Pereira

Rolfe

et al. 2017

Wear

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A new methodology for measuring galling wear severity in high strength steels, Wear, http://dx. AbstractWith the increased usage of Advanced High Strength Steels, galling wear has become a significant challenge for sheet metal stamping industries. Galling, in particular, can have a large economic impact due to the high costs and lost productivity associated with manual monitoring, refinishing/resurfacing damaged tooling and formed parts, and the need to apply expensive treatments or coatings to tool surfaces. This has led to a push for automated galling wear detection systems. However, developing such systems requires an accurate measurement of galling wear severity that can be easily implemented in industrial situations. Parameters used for measuring galling wear are often difficult to collect in large industrial style trials, and can be inaccurate as they are not targeted towards characterising the localised features associated with galling wear damage. In this study, a new galling wear characterisation and measurement methodology is introduced that accurately measures galling wear severity by targeting the localised features on sheet metal parts. This methodology involves calculating Discrete Wavelet Transform detail coefficients of 2D surface profiles. A case study on a series of deep drawn channel parts demonstrates the accuracy of the Discrete Wavelet Transform methodology when compared to visual assessment of galling wear severity. Based on comparison to visual assessment the presented Discrete Wavelet Transform galling wear measurement methodology outperforms other commonly used wear measures. The methodology provides a targeted, repeatable and non-subjective measure of galling wear severity. The specific outcome of this work provides an important tool for research into galling wear monitoring and detection systems in sheet metal forming, and the study of galling wear and its prevention in general.

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Section: Galling Wear Measurementmentioning

confidence: 99%

“…Mass and volume loss measurements of tooling are common methods for quantifying wear [11,14,19,28,29]. These methods are convenient for the purposes of modelling given the role of wear volume the in Archard wear equation [30], which has seen extensive usage in tool wear related studies.…”

Section: Galling Wear Measurementmentioning

confidence: 99%

A new methodology for measuring galling wear severity in high strength steels

Voss

Pereira

Rolfe

et al. 2017

Wear

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“…This forces companies to process high-strength materials using only low amounts of lubricant, which especially accelerates abrasive wear phenomena [4,5]. Due to this trend, wear is identified as the major cause for unplanned machine downtimes [6], required removal of tools [7] or the failure of the whole system [8]. In order to achieve an inline estimations about the actual wear state within a sheet metal forming process and to plan maintenance properly, data driven techniques like ML help to generate this process knowledge by identifying highly complex and non-linear patterns in given data sets [9].…”

Section: Introductionmentioning

confidence: 99%

Towards a systematical approach for wear detection in sheet metal forming using machine learning

Kubik

Becker

Molitor

et al. 2022

Prod. Eng. Res. Devel.

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Wear is one of the decisive factors for the economic efficiency of sheet metal forming processes. Thereby, progressive wear phenome lead on the one hand to a poor workpiece quality and on the other hand to tool failure resulting in high machine downtimes. This trend is intensified by processing high-strength materials and the reduction of lubricant up to dry forming. In this context, data-driven monitoring methods such as machine learning (ML) provide the potential of detecting wear at an early stage to overcome manual and cost-intensive process inspections. The presented study aims to provide a ML based inline quantification of wear states within sheet metal forming processes. The development of this monitoring approach is based on a procedure model the Knowledge Discovery in Time series and image data in Engineering Epplications (KDT-EA) which is validated on two forming processes, blanking and roll forming, that strongly differ in their physical process behavior and their acquired process data. The presented inline quantification allows an estimation of wear states with a deviation of less than 0.83% for the blanking process and 2.21% for the roll forming process from the actual wear state. Furthermore, it is shown that combining different feature extraction methods as well as a compensation of unbalanced data using data augmentation techniques are able to improve the performance of the investigated ML models.

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“…Luo et al [7] investigated the wear characteristics of the die in a process of the hot forging turbine blade, the influences of the temperature on the wear evolution and distribution were discussed. Galakhar et al [8] studied adhesive wear between a hardened tool steel sliding against a mild steel wheel, and investigated wear mechanisms of material transfer in a cold roll forming processes. Zhang et al [9] studied the influences of the processing parameters on the mold wear and optimized the parameters in cross wedge rolling process, the optimized parameters could prolong the mold life greatly.…”

Section: Introductionmentioning

confidence: 99%

Investigating Wear and Predicting Lifetime of the Roller Cavities for the Net-shape Blade Rolling

Jin¹,

Wang²,

Jiang³

et al. 2021

Preprint

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The die undergoing severe loads which induces inevitably wear in the pressure forming process, and the wear of die arouses obsessions about the die’s service lifetime. In order to obtain the geometrical shape transformation caused by wear and predict the service lifetime for a pair of rollers in net-shape blade rolling process, this paper quantified the distributions and evolutions of the local wear over roller cavities based on the local contact load responses, and predicted the lifetime which related to wear by a mathematical models. Firstly, the net-shape blade rolling process and the local contact load responses were summarized. Then, an improved wear model was provided based on the Archard formula, and the impact factors of the model was standardized by a regression analysis experiment. The transient wear distributions and evolutions over the roller cavities were enumerated, and the wear distribution for one rolling cycle was calculated based on wear accumulation effect. Finally, a lifetime prediction model was proposed to predict the service lifetime of the rollers according to the wear accumulation effect, and an experimental verification was carried out to validate the model. The results showed that the wear model and lifetime prediction model can be used for investigating the wear and lifetime prediction of the roller cavities for the net-shape blade rolling process.

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Adhesive tool wear in the cold roll forming process

Cited by 15 publications

References 32 publications

A new methodology for measuring galling wear severity in high strength steels

A new methodology for measuring galling wear severity in high strength steels

Towards a systematical approach for wear detection in sheet metal forming using machine learning

Investigating Wear and Predicting Lifetime of the Roller Cavities for the Net-shape Blade Rolling

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