Comparative Evaluation of Marking Methods on Cast Parts of Al–Si Alloy with Image Processing

Deng, Fangtian; Li, Rui; Klan, Steffen; Volk, Wolfram

doi:10.1007/s40962-021-00661-0

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Part-specific traceability is the key for modern quality management (Kopper & Apelian, 2022) and thus intelligent manufacturing. There is a vast body of literature on tracing problems in general; for the special application of tracing cast parts, a very recent overview by Deng et al (2021) compares different marking methods. Their review article presents a good overview and discussion of published marking methods, which they classify into three categories "simple marking methods", "innovative marking methods", and "advanced marking methods", according to their complexity of application.…”

Section: Related Workmentioning

confidence: 99%

A novel tracking system for the iron foundry field based on deep convolutional neural networks

et al. 2022

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In modern manufacturing the ability of retracing produced components is crucial for quality management and process optimization. Tracking is essential, especially for analyzing the influence of the production parameters on the final quality of the castings. In the iron foundry industry, common marking methods, such as a datamatrix code, cannot be used due to harsh environmental conditions and the rough surface of the cast parts. This work presents a new coding and reading system that guarantees unique marking in the casting process.The coding is built up over several beveled pins and is read out using an optical 2D handheld scanner. With a deep convolutional neural network approach of object detection and classification, a stable image processing algorithm is presented. With a first prototype a reading accuracy of 99.86% for each pin was achieved with an average scanning time of 0.43 s. The presented code is compatible with existing foundry processes, while the handheld scanner is intuitive and reliable. This allows immediate benefits for process optimization.

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Section: Related Workmentioning

confidence: 99%

A novel tracking system for the iron foundry field based on deep convolutional neural networks

et al. 2022

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Data-Driven Process Analysis for Iron Foundries with Automatic Sand Molding Process

Baitiang,

Weiß,

Krüger

et al. 2023

Inter Metalcast

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This paper proposes a methodological framework to develop a data-driven process control using pure industrial production data from a cast iron foundry, despite the limitation of complete casting traceability. The aim is to help sand foundries to produce good castings. A reference foundry, which produces mainly automotive and oven parts with automatic sand molding and pouring machines, was selected. Past data, where only good castings were produced, were extracted from the database to determine parameter control limits (upper and lower control limits) with the aid of statistical approach. To identify critical process parameters associated with casting defects, process data from the zero and high scrap production batches were systematically compared. This method clearly identified unstable parameters without exact synchronization between inline and part quality data. Molding sand moisture, temperature and compactability, liquidus temperature of the melt, phosphorus content, carbon equivalent and pouring temperature were found to be the critical parameters to be stabilized. Finally, a regression model for predicting and controlling of molding sand moisture and liquidus temperature of the melt was created. The determined boundaries and the models were helpful for the foundry in assisting ongoing production control and correction of process inputs to achieve target casting quality.

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Traceability System of Sand Core in Casting Production with a Digital-Twin Core Rack

Deng,

Li,

Klan

2023

Inter Metalcast

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In recent development of casting traceability, it has been noticed that many casting manufacturing processes that involve the use of sand cores often lack proper traceability back to the cores used in their production and the supporting production information. Most marking symbols applied to sand cores using existing marking methods not only exhibit unstable code readability but also pose critical risks to the surface quality of sand cores. In response, we developed and tested a digital-twin core rack aimed at transferring digital codes to sand cores without contact with their surfaces. By utilizing infrared sensors for real-time object detection, the core rack can transfer digital identical codes from the core-making machine to the core rack, and subsequently to the casting machine. This digital code encompasses all relevant process data of a sand core, from core making to de-coring. Moreover, the various sensors embedded in the core rack were designed to monitor air humidity, temperature, and vibration events during the transport and storage of the cores. These parameters are especially critical for maintaining the quality of inorganic sand cores. The study demonstrates the successful application of each component within this conceptual framework. Looking ahead, it is imperative to update the hardware of this concept to ensure its adaptation to the demanding conditions of an industrial environment.

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Comparative Evaluation of Marking Methods on Cast Parts of Al–Si Alloy with Image Processing

Cited by 3 publications

References 17 publications

A novel tracking system for the iron foundry field based on deep convolutional neural networks

A novel tracking system for the iron foundry field based on deep convolutional neural networks

Data-Driven Process Analysis for Iron Foundries with Automatic Sand Molding Process

Traceability System of Sand Core in Casting Production with a Digital-Twin Core Rack

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