An Overview of Machine Learning Applications in Metal Casting Industries - Overview - Review of Applications from 2010 to 2023

Aug 4, 2026

Upload by Ezra

This paper provides an overview of machine learning techniques and algorithms applied in metal casting industries, highlighting how ML improves defect detection, process optimization, predictive maintenance, and quality control. Reviewed research papers are organized into automation in foundry and quality control, and applications covering raw material melting, material design, and defect prediction. The literature survey focuses on sand-casting from 2010 to 2023, including models for melting composition, desired properties, and defect occurrence, supported by advanced foundry technologies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Automation in foundry and quality control

Data collection and preparation for casting data

Role of cyber-physical production systems and IoT constraints

Casting process risk and need for support systems

Artificial intelligence for production parameter alteration and cost prediction

DOI: [https://doi.org/10.24425/amm.2024.151428](https://doi.org/10.24425/amm.2024.151428) Vishal B. Bhagwat1,2*, Dhanpala. KamBle1, sanDeep s. Kore1 An Overview Of MAchine LeArning AppLicAtiOns in MetAL cAsting industries this paper presents an overview of different machine learning (ml) techniques and algorithms implemented in metal casting industries. ml has made significant contributions to the field of metal casting by improving various aspects of the casting process. in this work, referred quality research papers are divided into two categories. Firstly, work reviewed for the automation in foundry and quality control. secondly, the raw material melting, material designs and defect predictions in the metal casting. the literature is extensively studied for types of ml models implemented from 2010 to 2023 for the sand-casting application area especially in the prediction of material melting compositions, desired material properties and occurrence of defects along with involvement of advanced foundry technologies. Keywords: metal casting; machine learning; artificial intelligence; quality control; defects prediction 1. introduction it should be highlighted that a completely automated foundry implementing the smart Factory idea is currently implausible. however, it should be kept in mind that many industrial processes have recently been automated, which has reduced the involvement of operators slightly. a human is still a crucial component in foundry processes, despite having access to more tools that enhance decision-making. the increased demand for metal casting from a variety of industries has contributed significantly to the growth prospects ofthe worldwide foundry industry. the foundry industry, particularly in india, has experienced explosive expansion [in recent years. as](in recent years. as) a result, india is being considered as a possible hub for multinational corporations looking to establish manufacturing bases abroad for the high-volume, low-cost manufacture of casting components. models that integrate categorization and prediction techniques enable the acquisition of new production parameters without the need for extensive experimentation. in summary, machine learning (ml) has significantly enhanced the metal casting industry by improving defect detection, process optimization, predictive maintenance, quality control, and various other aspects, leading to higher efficiency and product quality. the ongoing advancements in ml are likely to further transform the field in the [coming years. here](coming years. here)’s a review of its applications. 2. Automation in foundry and quality control ml helps in optimizing energy consumption during the casting process, reducing environmental impact. it can minimize scrap and waste to save resources and costs. it may optimize the supply chain, ensuring timely delivery of raw materials and finished products assisting the foundry automation. machine learning can predict equipment failures by monitoring sensor data, ensuring timely maintenance, and reducing downtime. it can also predict and control the quality of castings by analyzing historical data, ensuring consistency in product quality. taBle 1 represents types of algorithms used for foundry automation and quality control. the steps of data collection and preparation connected to the production process of austempered Ductile iron (aDi) cast iron were briefly described in this paper. the processes from the perspective of data mining tools were explained here like data cleaning, merging, reducing, and transforming the data. according to the authors’ experience, connecting to Cpps (Cyber-physical production systems) at later stages in relation to different casting production processes may be significantly hampered by the adoption ofiot measurement tools and procedures. the step of production data collection is crucial because it completely depends on data’s accuracy and knowledge of the frequency of collec

FAQs

How does machine learning contribute to the metal casting industry?

Machine learning enhances defect detection, process optimization, predictive maintenance, and quality control, improving efficiency and product quality.

How are the reviewed works categorized in the paper?

The paper divides reviewed research into automation in foundry and quality control, and into applications for raw material melting, material design, and defect prediction.

Why is data collection and preparation critical for building ML models in foundry processes?

The paper emphasizes that model quality depends on data accuracy and on the frequency of data collection used to create mathematical models and automate processes.

Aug 4, 2026

Upload by Ezra