Optical inline monitoring of the burnish surface in the punching process

Lorenz, Maximilian; Menzl, Matthias; Donhauser, Christian; Layh, Michael; Pinzer, B

doi:10.1007/s00170-021-07922-6

Cited by 6 publications

(3 citation statements)

References 23 publications

(26 reference statements)

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“…Hamrol et al, 2020 Planning and optimization of quality inspections within a multistage manufacturing process based on quality cost and the value added to the production process by inspections Zimmermann et al, 2021 Manufacturing scheduling optimization that combines a predictive schedule with a proactive multicriteria decisionmaking method based on smart batches and their quality prediction capability. Lorenz et al, 2021 An optical line monitoring system that integrates a combined imaging and triangulation sensor as well as subsequent image processing. Verna et al, 2021 The effectiveness and cost of inspection strategies assessment.…”

Section: Duffuaa and El Gaaly 2017mentioning

confidence: 99%

Quality and Risk Management in Industrial Production Systems: A Literature Review

Baltag

Morariu

2022

Bulletin of the Polytechnic Institute of Iași. Machine Constructions Section

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Nowadays quality management and specific tools can represent a challenge in SMEs (small and medium enterprises). To assure the expected quality of the products, SPC (statistical process control) combined with industry 4.0 specifics offer instantaneous responses and triggers for needed actions. Therefore, risk analyses are used as enablers for quality-oriented behaviour, and risk mitigation and identification, together with collected data, represent a comprehensive system that leads enterprises to achieve the expected performance level needed to remain competitive in the market. In addition, risk management and quality management cannot be treated without looking at the maintenance activities and policies which can have a considerable impact on those two. This paper provides a comprehensive literature review of the papers from the quality management sector and articles where risk management and maintenance are seen as facilitators for quality improvement with an impact on the performance level. The main contribution of each paper is listed in the table which simplifies the reader’s access to the work done in the field and the overview presented enables easy access to trends in this field and offers a starting point for future research by presenting the gaps.

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Section: Duffuaa and El Gaaly 2017mentioning

confidence: 99%

Quality and Risk Management in Industrial Production Systems: A Literature Review

Baltag

Morariu

2022

Bulletin of the Polytechnic Institute of Iași. Machine Constructions Section

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“…Modern presses are equipped with four force sensors, one for each of the press columns [25], but four signals might not be enough to achieve effective decomposition and feature recognition. For this reason, multiple sensors are used to achieve a richer supply of data, such as acoustic sensors embedded in the tool holders [26][27][28], thin film local load sensors [29] or sensors to measure in-process the geometry [30], the quality [31] or the temperature [32] of the stamped parts. Another problem connected to data-driven approaches is that in real industrial cases, signals may shift for reasons that are often unknown and lead to conditions that are "false positives", i.e., conditions that apparently indicate an anomaly but that eventually correspond to regular production with good quality and no evidently worn or failed tools.…”

Section: Introductionmentioning

confidence: 99%

A Data-Based Tool Failure Prevention Approach in Progressive Die Stamping

Farioli

Kaya

Fumagalli³

et al. 2023

JMMP

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The research on methods for monitoring sheet metal stamping is benefiting from the increased availability of enabling technologies such as sensors, data mining software, cloud computing, and artificial intelligence. The predictive maintenance policies of tools (punches and dies) can be targeted at monitoring progressive wear or at the detection of sudden failures or anomalies. Early detection of tool failure is the method preferred by the recent literature on data management in sheet metal stamping. However, the stamping of small parts poses challenges due to multiple tools and signals and limited visibility of die wear, requiring management of multiple sensors and data sources. This paper proposes a failure prevention approach for progressive die stamping using global and local force sensors with upper bounds for maximum values to indicate unhealthy conditions. The methodology was tested on millions of small washers made of carbon steel. The stamping process was implemented using a servo-press with a high rate. The press was equipped with eight in-process sensors, including strain gauges, thin foil force sensors, and acoustic sensors. The data of material properties, maintenance reports, statistical process control data, and in-process sensors were collected and stored in a data lake. By combining the in-process sensor acquisition with the corresponding log events and maintenance data in the same time span, it is possible to look for correlations among the variables and build an effective tool health prevention policy.

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“…Since there is no direct extraction of KPP to determine the product quality of the blanked workpiece, and the image acquisition is done offline, the presented work is not suitable for inline QA of the blanked surface. Another promising approach for the direct, inline quantification of the quality of blanked workpieces in the form of the blanked surface is presented by Lorenz et al [29]. The authors developed a system consisting of a camera with a CMOS sensor and a laser as a light source to measure the blanked surface on a sheet metal strip.…”

Section: Introductionmentioning

confidence: 99%

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

Kubik,

Molitor,

Varchmin

et al. 2023

Preprint

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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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Optical inline monitoring of the burnish surface in the punching process

Cited by 6 publications

References 23 publications

Quality and Risk Management in Industrial Production Systems: A Literature Review

Quality and Risk Management in Industrial Production Systems: A Literature Review

A Data-Based Tool Failure Prevention Approach in Progressive Die Stamping

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

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