An investigation of predictive control for aluminum wheel casting via a virtual process model

Maijer, Daan M.; Owen, William J.; Vetter, R.A.

doi:10.1016/j.jmatprotec.2008.04.057

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…15 The XGBoost algorithm has also been applied to material characterization and quality assessment, e.g., predicting the fatigue strength of steels, 16 optimizing steel properties by correlating chemical compositions and process parameters with tensile strength and plasticity, 17 predicting aluminum alloy ingot quality in casting, 18 porosity prediction in oilfield exploration and development, 19 and diagnosing wind turbines blade icing. 20 Additionally, other works include optimizing die casting process conditions making use of techniques such as experimental robust design, [21][22][23] model predictive control, 24 and genetic algorithm optimization. 25 For example, Guo 26 has created classifiers to identify defects in silicon wafers coming from foundry operations.…”

Section: Related Workmentioning

confidence: 99%

Industry 4.0 Foundry Data Management and Supervised Machine Learning in Low-Pressure Die Casting Quality Improvement

et al. 2022

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Low-pressure die cast (LPDC) is widely used in high performance, precision aluminum alloy automobile wheel castings, where defects such as porosity voids are not permitted. The quality of LPDC parts is highly influenced by the casting process conditions. A need exists to optimize the process variables to improve the part quality against difficult defects such as gas and shrinkage porosity. To do this, process variable measurements need to be studied against occurrence rates of defects. In this paper, industry 4.0 cloud-based systems are used to extract data. With these data, supervised machine learning classification models are proposed to identify conditions that predict defectives in a real foundry Aluminum LPDC process. The root cause analysis is difficult, because the rate of defectives in this process occurred in small percentages and against many potential process measurement variables. A model based on the XGBoost classification algorithm was used to map the complex relationship between process conditions and the creation of defective wheel rims. Data were collected from a particular LPDC machine and die mold over three shifts and six continuous days. Porosity defect occurrence rates could be predicted using 36 features from 13 process variables collected from a considerably small sample (1077 wheels) which was highly skewed (62 defectives) with 87% accuracy for good parts and 74% accuracy for parts with porosity defects. This work was helpful in assisting process parameter tuning on new product pre-series production to lower defectives.

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Section: Related Workmentioning

confidence: 99%

Industry 4.0 Foundry Data Management and Supervised Machine Learning in Low-Pressure Die Casting Quality Improvement

et al. 2022

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“…The casting process is a cost and time-effective method to produce a complex metal-based component that requires specialized configuration [2]. The wheel rim uses aluminum as the base material since it has many advantages such as lightweight, cost-effective and superior mechanical strength [3]. The wheel rim is commonly produced using sand casting and can also be produced through high/low-pressure casting [4].…”

Section: Introductionmentioning

confidence: 99%

Impact of Top Mold Slope on Defect Formation in Gravity Casting of Aluminum Alloy

Hidayat,

Rahmalina,

Rahman

2024

ACSM

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This research investigates the impact of varying the slope of the top mold in the gravity casting process of aluminum alloy (A356). The casting mold slopes were adjusted to 7°, 12°, and 17° angles, and the study integrates software analysis (Altair, 2021.2) with physical examinations to assess potential defects in the casted products. The findings from both approaches exhibit a consistent pattern, revealing notable defect porosities in molds with slopes of 7° and 12°. Radiography examinations provide insights into defect distribution within the hub wheel rim, particularly pronounced in molds with slopes of 7° and 12°. This observation suggests that disturbances in air and heat distribution impede the solidification process within the mold. Metallography profiles further highlight the presence of phases α (Al), β (Si), and the eutectic region in the casted product. Defect severity is prominently manifested in a fractured profile, particularly for products cast with a 7° slope. In contrast, a higher slope of 12° results in localized porosities and effectively mitigates defect distribution. The most favorable outcome, devoid of substantial defects, is achieved with a 17° mold slope, successfully passing stringent quality checks.

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“…In foundries, the casting operations are controlled tightly, and parameters do not change frequently. Maijer et al [9] looked into these parameters and proposed a model-based predictive control tool for low pressure die casting. There are several tests that are typically used for additional control parameters such as microstructure (secondary dendrite arm spacing (SDAS) and eutectic modification), X-ray (for porosity) or mechanical tests by sampling from the cast part.…”

Section: Introductionmentioning

confidence: 99%

Determination of Acceptable Quality Limit for Casting of A356 Aluminium Alloy: Supplier’s Quality Index (SQI)

Erzi

Gürsoy

Yüksel

et al. 2019

Metals

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Aluminium and its alloys have been widely used in the automotive industry for some time and A356 is one of the most popular aluminium alloys today in the sector. It contains approximately 7 wt.% Si and 0.3 wt.% Mg. Due to the defects that may be present in the cast parts, expected mechanical properties may not be reached and this alloy may perform in service under its potential. In a low pressure die casting, several precautions have to be taken in order to produce high quality and defect-free castings. Temperature of casting and the schemical composition of the melt is recorded continuously. Die temperature, pressure levels and their durations are optimized; degassing and fluxing operations are performed. Yet, regardless of the precautions, there could still be rejected parts. It is becoming clear that a good quality raw material is one of the most underrated points of the industry and that the starting material has a great effect on the final product and it should be taken in to account more seriously. Therefore, regarding the first rule of Campbell's 10 Rules of Casting, in this study, the quality of the starting material of supply chains for a wheel manufacturer was investigated. Chemical compositions were compared, fluidity tests, mechanical tests and bifilm indexes of these various sources and ingots were measured and a final quality index was proposed to create a reference for the quantification of quality of supplier's ingots. This new index was compared with the rejection rates.

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An investigation of predictive control for aluminum wheel casting via a virtual process model

Cited by 15 publications

References 15 publications

Industry 4.0 Foundry Data Management and Supervised Machine Learning in Low-Pressure Die Casting Quality Improvement

Industry 4.0 Foundry Data Management and Supervised Machine Learning in Low-Pressure Die Casting Quality Improvement

Impact of Top Mold Slope on Defect Formation in Gravity Casting of Aluminum Alloy

Determination of Acceptable Quality Limit for Casting of A356 Aluminium Alloy: Supplier’s Quality Index (SQI)

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