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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.

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.

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.

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.

The purpose of this paper is to illustrate an application of the Six Sigma define, measure, analyze, improve, control (DMAIC) problem-solving methodology in a Portuguese industrial enterprise, aimed at reducing the rejection rate of a specific manufactured product due to defects generated in an aluminum gravity die casting operation.

Along the five-stage roadmap, a set of analytical and conceptual tools was systematically employed to better characterize the problem, define the product’s critical to quality characteristics, estimate the process baseline, determine the relevant cause-and-effect relationships, identify the root causes leading to the high rejection rate, implement an improvement plan, determine the effectiveness of the improvement actions, and put in place a control plan.

The project team discovered that the high rejection rate was caused by factors inherent to the gravity die casting operation itself and by the mixing of rejected parts made of different types of alloys from other locations within the plant. Another key finding was that the successful execution of the project was only possible due to the belief and support of top management and to the active involvement of the team members.

This case study illustrates a successful practical application of a Six Sigma project in a small-medium enterprise in Portugal, as well as the operational and financial benefits that derived from it; thus providing a good example for others to follow.

Few cases of continual improvement initiatives, including Six Sigma projects, developed at a Portuguese company are available in the literature; this paper fills such void by describing a very successful application that demonstrates the advantages that other companies can learn in terms of adopting structured methodologies to improve the quality of their products and the efficiency of their processes. Moreover, the analysis and conclusions herein presented can be of great importance for companies using gravity die casting technology.

Paper aims to an alloy development study was carried out to increase the mechanical properties of cylinder heads.

AlSi12 alloys are used to manufacture the compressor head cylinder by high-pressure casting for easy casting and superior properties. Therefore, 1.1%, 2.4% and 3.1% Mg were added to AlSi12. The microstructures of the produced samples were characterized by optical microscope, scanning electron microscopy, energy dispersive spectrometry and X-ray diffraction methods. Hardness and tensile tests as well as Charpy impact tests were performed. Wear tests were also carried out on the pin-on disc tester, and then the wear performance was examined on the tester, which simulates the actual operating condition.

AlSi12 has primary Si and eutectic Si in the Al matrix. However, alloys of Mg with AlSi12 have other intermetallics such as Mg₂Si and ß-Fe, as well as primary Si and eutectic Si. Hardness and tensile strength as well as improved wear performance with increased Mg content.

In this study, wear performance test to simulate the operation of the cylinder head produced by high pressure casting from AlSi12 alloy moreover tensile test, hardness test and impact test were performed. Therefore, in this study, the wear performance of the compressor head produced by high-pressure casting method by adding three different amounts of Mg to AlSi12 alloy was investigated.

The purpose of this technical paper is to explore the application of analytics and Six Sigma in the manufacturing processes for iron foundries. This study aims to establish a causal relationship between chemical composition and the quality of the iron casting to achieve the global benchmark quality level.

The case study-based exploratory research design is used in this study. The problem discovery is done through the literature survey and Delphi method-based expert opinions. The prediction model is built and deployed in 11 cases to validate the research hypothesis. The analytics helps in achieving the statistically significant business goals. The design includes Six Sigma DMAIC (Define – Measure – Analyze – Improve and Control) approach, benchmarking, historical data analysis, literature survey and experiments for the data collection. The data analysis is done through stratification and process capability analysis. The logistic regression-based analytics helps in prediction model building and simulations.

The application of prediction model helped in quick root cause analysis and reduction of rejection by over 99 per cent saving over INR6.6m per year. This has also enhanced the reliability of the production line and supply chain with on-time delivery of 99.78 per cent, which earlier was 80 per cent. The analytics with Six Sigma DMAIC approach can quickly and easily be applied in manufacturing domain as well.

The limitation of the present analytics model is that it provides the point estimates. The model can further be enhanced incorporating range estimates through Monte Carlo simulation.

The increasing use of prediction model in the near future is likely to enhance predictability and efficiencies of the various manufacturing process with sensors and Internet of Things.

The researchers have used design of experiments, artificial neural network and the technical simulations to optimise either chemical composition or mould properties or melt shop parameters. However, this work is based on comprehensive historical data-based analytics. It considers multiple human and temporal factors, sand and mould properties and melt shop parameters along with their relative weight, which is unique. The prediction model is useful to the practitioners for parameter simulation and quality enhancements. The researchers can use similar analytics models with structured Six Sigma DMAIC approach in other manufacturing processes for the simulation and optimisations.

The purpose of this paper is to study the effect of feeding scheme on melt flow and temperature field during the steady-state of level-pour direct-chill (DC) casting of A390 alloy hollow billet and optimize the design of feeding scheme.

Melt flow and temperature field are investigated by numerical simulation, which is based on a three-dimensional mathematical model and well verified by experiments.

The numerical results reveal that both melt flow and temperature field are obviously affected by the feeding scheme. The homogeneity of melt flow and temperature field in hollow billet with the feeding scheme of modified four inlets are better than the other feeding schemes. Experimental results show that crack can be eliminated by increasing the number of feeding inlets. The primary Si size appears unaffected while the distribution of primary Si particles is highly affected by the change of feeding scheme. Only with the feeding scheme of modified four inlets can fine and uniformly distributed primary Si particles be achieved.

The paper includes implications for the design of feeding scheme in level-pour DC casting of hollow billet for practical use.

This paper develops different feeding schemes for level-pour DC casting of hollow billet and optimizes the design of feeding scheme.

This research establishes a suitable casting model for magnesium alloy wheel. The casting of the wheel is an element that must be considered in the design of the wheel. Casting is an important basic process and technology in the field of machinery which is widely used in production, transportation, national defense, social life and other aspects. Computer numerical simulation of the casting process can shorten the product manufacturing cycle, reduce product costs, reduce casting defects and ensure product quality. The casting material in this study is AZ91 magnesium alloy used for wheel lightweight.

Lightweight research of automobile is a significant trend, and people are paying attention to the lightweight design of automobiles. Higher requirement was proposed on design and casting performance of the wheel which is an important part of lightweighting vehicle. In order to achieve better quality, the parametric studies of alloy wheel and casting are necessary. This research designs a new model of automobile wheel, to ensure energy efficiency, the wheels must be as lightweight as possible, using magnesium alloy material for lightweight.

Analysis of casting process is a very complex issue. This research based on finite element theory and actual production, designed reasonable casting model, instant filling and solidification data were obtained. Aiming at reducing casting defects, process improvement of casting riser structure was designed. On the basis of the foundation, it has important guiding significance for actual foundry production.

This research establish a suitable casting model for magnesium alloy wheel. Aiming at reducing casting defects, process improvement of casting riser structure were designed.

The introduction of new technology into the small to medium‐size company presents many problems — the most severe being the lack of skilled personnel, technologists and management. Through the use of outside consultants and a six‐step process, innovation projects can be undertaken with the maximum benefit at the minimum cost. A small fishing tackle manufacturer, in order to conform to legal and market changes, has to change its melting and casting processes. The six‐step process is employed to effect change in this company.

Increasing utilisation of the properties of hardness, corrosion resistance and electrical insulation has taken place rapidly in recent years. Still scope exists for further development of anodised aluminium as knowledge of the mechanism involved grows. In the first of this two‐part article the author deals mainly with bright and conventional architectural anodising.

To provide a suitable linkage of a computational fluid dynamics code and a shape optimization code for the augmentation of local heat transfer coefficients in forced convection channels normally used in the cooling of electronic equipment.

A parallel‐plate channel with a discrete array of heat sources embedded in one wall, while the other wall is insulated, constitutes the starting model. Using water as coolant, the objective is to optimize the shape of the channel employing a computerized design loop. The two‐part optimization problem is constrained to allow only the unheated wall to deform, while keeping the same inlet shape and observing a maximum pressure drop constraint.

First, the results for the linearly deformed unheated wall show significant decrease compared with the wall temperatures of the heated wall, with the maximum wall temperature occurring slightly upstream of the outlet. Second, when the unheated wall is allowed to deform nonlinearly, a parabolic‐like shaped wall is achieved where the maximum wall temperature is further reduced, with a corresponding intensification in the local heat transfer coefficient. The effectiveness of the computerized design loop is demonstrated in complete detail.

This paper offers a simple technique for optimizing the shapes of forced convection channels subjected to pre‐set design constraints.