Towards a real-time tool state detection in sheet metal forming processes validated by wear classification during blanking

Kubik, Christian; Molitor, Dirk-Alexander; Rojahn, M; Groche, Peter

doi:10.1088/1757-899x/1238/1/012067

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…The maximal punch force is defined as the cutting force 𝐹𝐹 max = 𝑙𝑙 • 𝑠𝑠 • 𝑘𝑘 𝑠𝑠 , with the cutting line 𝑙𝑙, the sheet thickness 𝑠𝑠 and the shear strength shear strength 𝑘𝑘 𝑠𝑠 , according to Lange [7]. The shear strength depends on a various number of variables such as tool parameters (clearance, tool wear, surface conditions of the active elements), material parameters (tensile strength, elongation at break, alloy composition) and other process parameters (lubrication, temperature, cutting speed) [8].…”

Section: Fig 1 Schematic Illustration Of the Interlocking Process Chainmentioning

confidence: 99%

Influence of process parameters on mechanical properties of lamination stacks produced by interlocking

Martin

2023

Materials Research Proceedings

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Abstract. Interlocking is mainly used in the manufacture of lamination stacks for the cores for electrical energy converters. The process involves the embossing of nubs, which are subsequently stacked and joined by an interference fit. For an optimal design of interlocked joints, sufficient knowledge of the relationships between various influencing parameters in the manufacture and the resulting stack properties is essential. In addition to the design parameters nub geometry, size and embossing depth, the process parameters embossing clearance, counterpunch and blankholder force influence the joint strength. In addition to these fixed parameters, continuously varying uncertainty such as wear, which can lead to a rounding of the tool edges, affects relevant stack properties like the joint strength. The mechanical loads in the area of the tool edge which are responsible for this rounding mainly act immediately before material separation and have to be considered in the embossing process. However, the influence of punch edge radii on the joint properties during the interlocking process has not yet been investigated. In order to describe the interdependencies between different influencing parameters on the achievable mechanical strength, experimental investigations are carried out. Therefore, cylindrical nubs are interlocked with varying embossing depth, clearance and edge radii. The joint strength is determined via tensile tests. Key findings are that the joint strength of nubs with increasing abrasive wear on the cutting edge of the embossing tool are compensated by a higher embossing depth. The minimum embossing depth required for stacking and the embossing depth at which the optimum strength is achieved depend on the embossing edge radius and thus on the current abrasive wear state.

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Section: Fig 1 Schematic Illustration Of the Interlocking Process Chainmentioning

confidence: 99%

Influence of process parameters on mechanical properties of lamination stacks produced by interlocking

Martin

2023

Materials Research Proceedings

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Image-based feature extraction for inline quality assurance and wear classification in high-speed blanking processes

Kubik,

Molitor,

Varchmin

et al. 2023

Int J Adv Manuf Technol

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Wear is one of the key factors that determine the efficiency of multi-stage processes that include blanking operations. Since wear in these processes not only causes unplanned downtime but also directly affects product quality, inline detection of wear and its effect on product quality is of major importance. However, current quality assurance (QA) methods are limited to manual offline inspection by operators at predefined intervals, so that 100% inspection of the product and description of the state of wear is not found in industrial practice. The aim of this work is therefore to develop an optical system that enables in-line acquisition of product images and the associated control of blanking-specific quality features up to stroke rates of 300 strokes per minute (spm). In order to make the system attractive to small- and medium-sized enterprises (SME), the system is designed to minimize integration and investment costs using commercially available components. By combining the system with a methodology for extracting blanking-specific features, so-called key performance parameters (KPPs), the condition of the blanked surface as a relevant quality parameter is derived directly from the workpiece image. To demonstrate the transferability of the methodology to industrial applications, two use cases are investigated. In the first case, the KPPs are used directly to determine the quality of the blanked workpiece and are compared with reference measurements. Here, the KPPs are quantified with a mean absolute error of 18 μm compared to a ground truth. In the second case, the KPPs are used to build a machine learning (ML) model to estimate the wear of the blanking tool. Here, an accuracy of 92% is achieved in classifying the actual wear state.

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Image based feature extraction for inline quality assurance and wear classification in high speed blanking processes

Kubik,

Molitor,

Varchmin

et al. 2023

Preprint

View full text Add to dashboard Cite

Wear is one of the key factors that determine the efficiency of multi-stage processes that include blanking operations. Since wear in these processes not only causes unplanned downtime, but also directly affects product quality, inline detection of wear and its effect on product quality is of major importance. However, current quality assurance (QA) methods are limited to manual offline inspection by operators at predefined intervals, so that 100% inspection of the product and description of the state of wear is not found in industrial practice. The aim of this work is therefore to develop an optical system that enables in-line acquisition of product images and the associated control of blanking-specific quality features up to stroke rates of 300 strokes per minute (spm). In order to make the system attractive to small and medium-sized enterprises (SME), the system is designed to minimize integration and investment costs using commercially available components. By combining the system with a methodology for extracting blanking-specific features, so-called Key Performance Parameters (KPPs), the condition of the blanked surface as a relevant quality parameter is derived directly from the workpiece image. To demonstrate the transferability of the methodology to industrial applications, two use cases are investigated. In the first case, the KPPs are used directly to determine the quality of the blanked workpiece and are compared with reference measurements. Here, the KPPs are quantified with a mean absolute error of 18 µm compared to a ground truth. In the second case, the KPPs are used to build a machine learning (ML) model to estimate the wear of the blanking tool. Here, an accuracy of 92% is achieved in classifying the actual wear state.

show abstract

Towards a real-time tool state detection in sheet metal forming processes validated by wear classification during blanking

Cited by 4 publications

References 32 publications

Influence of process parameters on mechanical properties of lamination stacks produced by interlocking

Influence of process parameters on mechanical properties of lamination stacks produced by interlocking

Image-based feature extraction for inline quality assurance and wear classification in high-speed blanking processes

Image based feature extraction for inline quality assurance and wear classification in high speed blanking processes

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