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

Abstract

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.

Survey on AI Applications for Product Quality Control and Predictive Maintenance in Industry 4.0

“…The data used in these works typically consist of numerical data recorded from sensors placed on the production lines during the manufacturing process. Examples of applications in this category include porosity detection in aluminum wheels [16] and the detection of geometrical defects in extruded tubes [17]. Several studies are specifically related to plastic injection molding [5,[18][19][20].…”

A Realistic Framework to Assess the Barriers to SCM 4.0 in the Foundry Industry

“…Vertical integration integrates the information and data into the different hierarchical levels of the organization, intending to support decision-making concerning demand variation, stock levels, production, and customer demands. Therefore, vertical and horizontal integration of technologies improves data sharing and collaboration among all stakeholders (Uyan et al, 2022).…”

“…Data collection and management in I4.0 technology, a study conducted by Uyan et al (2022) in the foundry industry mentions that machine learning algorithms are not sufficiently equipped to identify defective parts in production. However, data collection and management helped identify the root causes of the production process in foundry units.…”

A Novel Imputation Based Predictive Algorithm for Reducing Common Cause Variation from Small and Mixed Datasets with Missing Values

“…The effect of data features (factors) on metal penetration of an iron casting is studied. Three factors from 282 factors showed a significant impact on the output (Uyan et al, 2022). A cloudbased process variable measurement system is developed to extract data.…”