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International Journal of Quality & Reliability Management

ISSN : 0265-671X

Article publication date: 26 October 2021

Issue publication date: 17 January 2023

Casting is one of the well-known manufacturing processes to make durable parts of goods and machinery. However, the quality of the casting parts depends on the proper choice of process variables related to properties of the materials used in making a mold and the product itself; hence, variables related to product/process designs are taken into consideration. Understanding casting techniques considering significant process variables is critical to achieving better quality castings and helps to improve the productivity of the casting processes. This study aims to understand the computational models developed for achieving better quality castings using various casting techniques.

Design/methodology/approach

A systematic literature review is conducted in the field of casting considering the period 2000–2020. The keyword co-occurrence network and word cloud from the bibliometric analysis and text mining of the articles reveal that optimization and simulation models are extensively developed for various casting techniques, including sand casting, investment casting, die casting and squeeze casting, to improve quality aspects of the casting's product. This study further investigates the optimization and simulation models and has identified various process variables involved in each casting technique that are significantly affecting the outcomes of the processes in terms of defects, mechanical properties, yield, dimensional accuracy and emissions.

This study has drawn out the need for developing smart casting environments with data-driven modeling that will enable dynamic fine-tuning of the casting processes and help in achieving desired outcomes in today's competitive markets. This study highlights the possible technology interventions across the metal casting processes, which can further enhance the quality of the metal casting products and productivity of the casting processes, which show the future scope of this field.

Research limitations/implications

This paper investigates the body of literature on the contributions of various researchers in producing high-quality casting parts and performs bibliometric analysis on the articles. However, research articles from high-quality journals are considered for the literature analysis in identifying the critical parameters influencing quality of metal castings.

Originality/value

The systematic literature review reveals the analytical models developed using simulation and optimization techniques and the important quality characteristics of the casting products. Further, the study also explores critical influencing parameters involved in every casting process that significantly affects the quality characteristics of the metal castings.

• Optimization
• Bibliometric analysis

Suthar, J. , Persis, J. and Gupta, R. (2023), "Critical parameters influencing the quality of metal castings: a systematic literature review", International Journal of Quality & Reliability Management , Vol. 40 No. 1, pp. 53-82. https://doi.org/10.1108/IJQRM-11-2020-0368

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Casting and Casting Processes

39 Pages Posted: 12 Jul 2017

D. G. Mahto

Green Hills Engineering College

Date Written: March 10, 2015

The casting process was discovered probably around 3500 BC in Mesopotamia. Casting is unique manufacturing processes for a variety of reasons. Perhaps the most obvious is the array of molding and casting processes available that are capable of producing complex components in any metal, ranging in weight from less than an ounce to single parts weighing several hundred tons. Foundry processes are available and in use that are economically viable for producing a single prototype part, while others achieve their economies in creating millions of the same part. Virtually any metal that can be melted can and is being cast. Many parts and components are made by casting, including automotive components such as carburettors, engine blocks, crankshafts, agricultural and rail road equipments, pipe and pumping fixtures, power tools, gun barrels and large components of hydraulic turbines etc. Since 1950, partially automated casting processes have been developed for production lines.It is estimated that castings are used in 90% or more of all manufactured goods and in all capital goods machinery used in manufacturing. The diversity in the end use of metal castings is a direct result of the many functional advantages and economic benefits that castings offer compared to other metal forming methods. The beneficial characteristics of a cast component are directly attributable to the inherent versatility of the casting process.

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Dalgobind Mahto (Contact Author)

Green hills engineering college ( email ).

SP-43, RIICO Industrial Area Kukas Jaipur, Rajasthan 302028 India 8058799995 (Phone) 8058799995 (Fax)

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Continuous casting preparation process of helical fiber-reinforced metal matrix composites.

1. Introduction

2. experimental section, 2.1. design and manufacturing of experimental equipment, 2.2. material, 2.3. process parameters, 2.4. testing, 2.4.1. the shape of helical carbon fiber in composites, 2.4.2. interface and mechanical property of helical carbon fiber-reinforced aluminum composites, 3.1. coordinate system, 3.2. basic assumptions, 3.3. the formation processing of helical fiber, 3.3.1. initial state, 3.3.2. transition state, 3.3.3. stable state, 3.4. the angle θ a b difference between point a and point b, 4. results and discussion, 4.1. shape stability of helical carbon fiber in lead matrix, 4.2. influence of process parameters on the shape of helical carbon fiber in lead matrix, 4.2.1. melting temperature, 4.2.2. cooling intensity, 4.2.3. carbon fiber rotation speed, 4.3. prediction of the helical fiber shape, 4.4. helical carbon fiber-reinforced aluminum matrix composites, 4.4.1. the shape of helical carbon fiber, 4.4.2. interface of carbon fiber and aluminum matrix, 4.4.3. mechanical properties, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Yang, H.; Chang, M.; Wu, C. Continuous Casting Preparation Process of Helical Fiber-Reinforced Metal Matrix Composites. Metals 2024 , 14 , 832. https://doi.org/10.3390/met14070832

Yang H, Chang M, Wu C. Continuous Casting Preparation Process of Helical Fiber-Reinforced Metal Matrix Composites. Metals . 2024; 14(7):832. https://doi.org/10.3390/met14070832

Yang, Hui, Ming Chang, and Chunjing Wu. 2024. "Continuous Casting Preparation Process of Helical Fiber-Reinforced Metal Matrix Composites" Metals 14, no. 7: 832. https://doi.org/10.3390/met14070832

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A Critical Review on Casting Types and Defects

Casting is the oldest manufacturing method and well known metallurgical process. Casting process basically involves introduction of molten metal into a mold cavity and subsequently the molten metal takes the shape of mold cavity. Very simple and high end complicated shapes and designs can be made from any metal that can be melted. Casting is an integrated process which is considered as an experienced artful work with high end quality aspects. Even though these high quality aspects are considered, defects are very much inherent in casting process. The main objective of the current review is to explain casting types and discuss the possible defects during the process of casting. The scope of the review also includes causes and remedies of casting defects.

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Now a day's casting plays a key role for manufacturing industries so its performance should be highly effective in terms of production with minimum number of rejections. So casting yield should be high which can be achieved in highly controlled environment where defects can be minimised so as to minimise the rejections. The challenges of casting defects are to be identified and minimised for effective castings. This paper aims to analyse the causes of different types of defects and provides the remedial measures which will be helpful in improving the quality of product along with increase the productivity. This paper enlists various defects in casting and provides the causes of their occurrence, which will help to analyse the undesirable defects in casting.

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Many industry aims to improve quality as well as productivity of manufacturing product. So need to number of process parameter to must controlled while casting process, so there are no of uncertainty and defects are face by organizations. In casting process industries are need to technical solution to minimize the uncertainty and defects. In this review paper to represent various casting defects and root causes for engine parts while casting process. Also provide preventive action to improve quality as well as productivity an industrial level.

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Global buyers demand defect-free castings and strict delivery schedule, which foundries are finding it verydifficult to meet. Metal casting industry suffers from poor quality and productivity due to the large number of process parameters, combined with lower penetration of manufacturing automation. Casting process involves complex interactions among various parameters and operations related to metal composition, methods design, molding, melting, pouring, shake-out, fettling and machining. For example, if shrinkage porosity is identified as gas porosity, and the pouring temperature is lowered to reduce the same, it may lead to another defect, namely cold shut. Casting defects result in increased unit cost and lower morale of shop floor personnel.Casting defect analysis has been carried out using techniques like cause-effect diagrams, design of experiments, if-then rules (expert systems), and artificial neural networks. Most of the previous work is focused on finding process-related causes for individual defects, and optimizing the parameter values to reduce the defects.The defects are classified in terms of their appearance, size, location, consistency, discovery stage and inspection method. This helps in correct identification of the defects. For defect analysis, the possible causes are grouped into design, material and process parameters. The effect of suspected cause parameters on casting quality is ascertained through simulation. Based on the results and their interpretation, the optimal values of the parameters are determined to eliminate the defects. An integrated understanding of heat transfer during solidification, friction/lubrication at solid–liquid interface, high temperature properties of the solidifying shell etc. is necessary to control the casting process.The influence of steel chemistry on solidification dynamics, particularly with respect to mode of solidification and its consequence on strength and ductility of the solidifying shell, has been dealt with in detail. The application of these basic principles for casting of stainless steel slabs and processing to obtain good quality products has been covered. Castings can unfortunately also sometimes contain other types of defects, such as inclusions of slag or moulding sand, but these are not classified as solidification defects.

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More reliable and durable parts with high structural integrity are required to meet the increasing advancements in science and technology. This paper reviews five (5) different casting techniques: squeeze casting, sand casting, investment casting, die casting, and continuous casting. Their respective cast products were examined, and their various mechanical properties were discussed. However, these different casting techniques involve a similar fundamental procedure: melting metal, pouring it into a mold, and allowing it to solidify. However, they vary in their physical and mechanical properties, durability, and surface finishing, making one technique more desirable than the other in their application areas. Some techniques were found to be more advantageous and effective than the other, which will aid foundrymen in making the best decision in choosing a technique, considering parameters such as environmental friendliness and cost implications. The appropriate implementation of thes...

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An attempt has been made to describe the casting metallic mold in brief and review the major casting process based on a set of criteria such as step involved, process conceptualization, advantages, disadvantages, and their applications. In addition, the most defects of the casting process are also presented in this study. Based on this review, it can be observed that numerous casting methods are founded and the selection of a process is depend on several factors such as the quality of the casting surface, dimension accuracy, rate production, shape complexity and cost .etc.

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Casting process is the most widely used process in manufacturing industries. Production of casting involves various processes like pattern making, molding, and core making and melting. It is very difficult to produce defect free castings. Systematic analysis and identification of sources of product defects are essential for successful manufacturing. Since the quality of casting parts are mostly influenced by process conditions, how to determine the optimum process condition becomes the key to improving part quality. The industry generally tries to eliminate the defects by trial and error method. This paper describes the identification and analysis of the casting defects. Filling related defects, Shape related defects and Thermal related defects of casting products are discussed in this paper. Defects occurred by various gating system parameters are also be identified. So good gating system reduces the defects.

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Influence of Casting Materials on the Microstructure and Mechanical Properties of Gray Cast Iron for Cylinder Liners

• Technical Paper
• Published: 24 July 2024

Cite this article

• Shouquan Du   ORCID: orcid.org/0009-0004-1551-9507 1 ,
• Chaoyang Chen 2 ,
• Ruirun Chen 1 , 2 ,
• Qi Wang 2 ,
• Xiangyin Cui 1 &
• Qiang Song 1  

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In this paper, four different casting materials were used to get gray cast iron samples, the effects of different cooling rates caused by different casting materials on graphite distribution, matrix structure and mechanical properties were investigated. The experimental results show that as the cooling rate increases, the graphite form of gray cast iron changed from coarse flake A-type graphite to rosette shaped B-type graphite, graphite increased in quantity and was more evenly distributed. The interlayer spacing of pearlite in matrix decreased with the increase of cooling rate, four different mold casting of cast iron material sample of pearlite lamellar spacing is CO 2 sodium silicate bonded sand mold, 340 nm, oxide ceramic mold, 275 nm, cast iron mold, 141 nm, graphite casting mold, 135 nm, respectively. The reduction of the interlayer spacing of pearlite also significantly improves the tensile strength, compressive strength and hardness. The tensile strength of cast iron specimens cast in graphite casting molds is the highest, at 421 MPa, while the tensile strength of cast iron specimens cast in CO 2 sodium silicate bonded sand molds is the lowest, at 346 MPa. The graphite cast iron sample has the highest compressive strength of 2165 MPa, and the oxide ceramic cast iron sample has the lowest compressive strength of 1115 MPa. The Brinell hardness of the samples cast in cast iron molds is the highest, at 409 HB, while the samples cast in CO 2 sodium silicate bonded sand molds have the lowest Brinell hardness, at 255 HB. In addition, increasing the cooling rate inhibited the diffusion of elements in the melt, reduced the final solidification interval and also reduced the shrinkage porosity and other defects. Fracture analysis shows that cleavage fracture is the main fracture mode of castings. The higher the cooling rate, the smoother the fracture morphology.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 52425401 and 52374384), Foundation of National Key Laboratory for Precision Hot Processing of Metals (JCKYS2021603C001).

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Du, S., Chen, C., Chen, R. et al. Influence of Casting Materials on the Microstructure and Mechanical Properties of Gray Cast Iron for Cylinder Liners. Inter Metalcast (2024). https://doi.org/10.1007/s40962-024-01413-6

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Effect of roll diameter on temperature of aluminum alloy strip during casting using a vertical type high speed twin roll caster, effect of roll material on strip solidification between the rolls of a vertical-type high-speed twin-roll caster, modeling effect of cooling conditions on solidification process during thermal cycle of rollers in twin-roll strip casting, the effect of the thermal deformation of casting roll on strip thickness in the strip casting process, effect of cast roll sleeve material on temperature field of sandwich composite plate solid-liquid-solid twin-roll casting process, research methods and influencing factors of interfacial heat transfer during sub-rapid solidification process of strip casting, formation of deposited oxide film during the sub-rapid solidification of silicon steel droplet and its effect on interfacial heat transfer behavior, air gap measurement during steel-ingot casting and its effect on interfacial heat transfer, two-phase viscoplastic model for the simulation of twin roll casting, near net shape casting: is it possible to cast too thin, related papers.

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Machine learning unlocks secrets to advanced alloys

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The concept of short-range order (SRO) — the arrangement of atoms over small distances — in metallic alloys has been underexplored in materials science and engineering. But the past decade has seen renewed interest in quantifying it, since decoding SRO is a crucial step toward developing tailored high-performing alloys, such as stronger or heat-resistant materials.

Understanding how atoms arrange themselves is no easy task and must be verified using intensive lab experiments or computer simulations based on imperfect models. These hurdles have made it difficult to fully explore SRO in metallic alloys.

But Killian Sheriff and Yifan Cao, graduate students in MIT’s Department of Materials Science and Engineering (DMSE), are using machine learning to quantify, atom-by-atom, the complex chemical arrangements that make up SRO. Under the supervision of Assistant Professor Rodrigo Freitas, and with the help of Assistant Professor Tess Smidt in the Department of Electrical Engineering and Computer Science, their work was recently published in The Proceedings of the National Academy of Sciences .

Interest in understanding SRO is linked to the excitement around advanced materials called high-entropy alloys, whose complex compositions give them superior properties.

Typically, materials scientists develop alloys by using one element as a base and adding small quantities of other elements to enhance specific properties. The addition of chromium to nickel, for example, makes the resulting metal more resistant to corrosion.

Unlike most traditional alloys, high-entropy alloys have several elements, from three up to 20, in nearly equal proportions. This offers a vast design space. “It’s like you’re making a recipe with a lot more ingredients,” says Cao.

The goal is to use SRO as a “knob” to tailor material properties by mixing chemical elements in high-entropy alloys in unique ways. This approach has potential applications in industries such as aerospace, biomedicine, and electronics, driving the need to explore permutations and combinations of elements, Cao says.

Capturing short-range order

Short-range order refers to the tendency of atoms to form chemical arrangements with specific neighboring atoms. While a superficial look at an alloy’s elemental distribution might indicate that its constituent elements are randomly arranged, it is often not so. “Atoms have a preference for having specific neighboring atoms arranged in particular patterns,” Freitas says. “How often these patterns arise and how they are distributed in space is what defines SRO.”

Understanding SRO unlocks the keys to the kingdom of high-entropy materials. Unfortunately, not much is known about SRO in high-entropy alloys. “It’s like we’re trying to build a huge Lego model without knowing what’s the smallest piece of Lego that you can have,” says Sheriff.

Traditional methods for understanding SRO involve small computational models, or simulations with a limited number of atoms, providing an incomplete picture of complex material systems. “High-entropy materials are chemically complex — you can’t simulate them well with just a few atoms; you really need to go a few length scales above that to capture the material accurately,” Sheriff says. “Otherwise, it’s like trying to understand your family tree without knowing one of the parents.”

SRO has also been calculated by using basic mathematics, counting immediate neighbors for a few atoms and computing what that distribution might look like on average. Despite its popularity, the approach has limitations, as it offers an incomplete picture of SRO.

Fortunately, researchers are leveraging machine learning to overcome the shortcomings of traditional approaches for capturing and quantifying SRO.

Hyunseok Oh , assistant professor in the Department of Materials Science and Engineering at the University of Wisconsin at Madison and a former DMSE postdoc, is excited about investigating SRO more fully. Oh, who was not involved in this study, explores how to leverage alloy composition, processing methods, and their relationship to SRO to design better alloys. “The physics of alloys and the atomistic origin of their properties depend on short-range ordering, but the accurate calculation of short-range ordering has been almost impossible,” says Oh. 

A two-pronged machine learning solution

To study SRO using machine learning, it helps to picture the crystal structure in high-entropy alloys as a connect-the-dots game in an coloring book, Cao says.

“You need to know the rules for connecting the dots to see the pattern.” And you need to capture the atomic interactions with a simulation that is big enough to fit the entire pattern. 

First, understanding the rules meant reproducing the chemical bonds in high-entropy alloys. “There are small energy differences in chemical patterns that lead to differences in short-range order, and we didn’t have a good model to do that,” Freitas says. The model the team developed is the first building block in accurately quantifying SRO.

The second part of the challenge, ensuring that researchers get the whole picture, was more complex. High-entropy alloys can exhibit billions of chemical “motifs,” combinations of arrangements of atoms. Identifying these motifs from simulation data is difficult because they can appear in symmetrically equivalent forms — rotated, mirrored, or inverted. At first glance, they may look different but still contain the same chemical bonds.

The team solved this problem by employing 3D Euclidean neural networks . These advanced computational models allowed the researchers to identify chemical motifs from simulations of high-entropy materials with unprecedented detail, examining them atom-by-atom.

The final task was to quantify the SRO. Freitas used machine learning to evaluate the different chemical motifs and tag each with a number. When researchers want to quantify the SRO for a new material, they run it by the model, which sorts it in its database and spits out an answer.

The team also invested additional effort in making their motif identification framework more accessible. “We have this sheet of all possible permutations of [SRO] already set up, and we know what number each of them got through this machine learning process,” Freitas says. “So later, as we run into simulations, we can sort them out to tell us what that new SRO will look like.” The neural network easily recognizes symmetry operations and tags equivalent structures with the same number.

“If you had to compile all the symmetries yourself, it’s a lot of work. Machine learning organized this for us really quickly and in a way that was cheap enough that we could apply it in practice,” Freitas says.

Enter the world’s fastest supercomputer

This summer, Cao and Sheriff and team will have a chance to explore how SRO can change under routine metal processing conditions, like casting and cold-rolling, through the U.S. Department of Energy’s INCITE program , which allows access to Frontier , the world’s fastest supercomputer.

“If you want to know how short-range order changes during the actual manufacturing of metals, you need to have a very good model and a very large simulation,” Freitas says. The team already has a strong model; it will now leverage INCITE’s computing facilities for the robust simulations required.

“With that we expect to uncover the sort of mechanisms that metallurgists could employ to engineer alloys with pre-determined SRO,” Freitas adds.

Sheriff is excited about the research’s many promises. One is the 3D information that can be obtained about chemical SRO. Whereas traditional transmission electron microscopes and other methods are limited to two-dimensional data, physical simulations can fill in the dots and give full access to 3D information, Sheriff says.

“We have introduced a framework to start talking about chemical complexity,” Sheriff explains. “Now that we can understand this, there’s a whole body of materials science on classical alloys to develop predictive tools for high-entropy materials.”

That could lead to the purposeful design of new classes of materials instead of simply shooting in the dark.

The research was funded by the MathWorks Ignition Fund, MathWorks Engineering Fellowship Fund, and the Portuguese Foundation for International Cooperation in Science, Technology and Higher Education in the MIT–Portugal Program.

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