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Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.

The literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.

The authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.

This study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

Due to intrinsic limitations, fused deposition modelling (FDM) products suffer from the bad surface finish and inaccurate dimensional accuracies restricting its usage in many applications. Hence, there is a need for processing polymer patterns before, during and after their productions. This paper aims to highlight the importance of pre- and post-processing treatments on the FDM-based acrylonitrile butadiene styrene patterns improving its surface quality so, that it can be used in rapid investment casting process for making medical implants and other high precision components.

As a part of pre-processing treatment, the machine parameters affecting the surface quality were identified and optimised using design of experiments. The patterns developed after the first stage of optimisation were given different post-processing treatments, which included vapour smoothening, chemical treatment and sand paper polishing. The results were compared and the best ones were used for making patterns for making medical implants via rapid investment casting technique. The surface quality was checked while the dimensional changes happening during the stages of this hybrid technique were recorded using a three-dimensional optical scanner.

The surface roughness of the FDM based ABS patterns reduced from 21.63 to 14.40 µm with pre-processing treatments. Chemical treatment (post-processing treatment) turned to be the most suitable technique for reducing the surface roughness further down to 0.30 µm. Medical implants that used these pre- and post-processing treatments gave an average surface roughness of 0.68 µm. Cost and lead time comparisons showed that rapid investment casting technique can be a better method for low volume, customised and with specific requirements.

FDM parts/medical implants produced by rapid investment casting technique suffer from the inferior surface finish and inaccurate dimensional accuracies limiting its applications. A systematic approach to overcome this issue is presented in this research paper. This will directly help the end users and the manufacturers of medical implants, wherein, better surface finish and dimensionally accurate components are expected.

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.

This paper seeks to review the industrial applications of state‐of‐the‐art additive manufacturing (AM) techniques in metal casting technology. An extensive survey of concepts, techniques, approaches and suitability of various commercialised rapid casting (RC) solutions with traditional casting methods is presented.

The tooling required for producing metal casting such as fabrication of patterns, cores and moulds with RC directly by using different approaches are presented and evaluated. Relevant case studies and examples explaining the suitability and problems of using RC solutions by various manufacturers and researchers are also presented.

Latest research to optimize the current RC solutions, and new inventions in processing techniques and materials in RC performed by researchers worldwide are also discussed. The discussion regarding the benefits of RC solutions to foundrymen, and challenges to produce accurate and cost‐effective RC amongst AM manufacturers concludes this paper.

The research related to this survey is limited to the applicability of RC solutions to sand casting and investment casting processes. There is practically no implication in industrial application of RC technology.

This review presents the information regarding potential AM application – RC, which facilitates the fabrication of patterns, cores and moulds directly using the computer‐aided design data. The information available in this paper serves the purpose of researchers and academicians to explore the new options in the field of RC and especially users, manufacturers and service industries to produce casting in relatively much shorter time and at low cost and even to cast complex design components which otherwise was impossible by using traditional casting processes and CNC technology.

This paper aims to review the industrial and biomedical applications of state-of-the-art fused deposition modelling (FDM)-assisted investment casting (FDMAIC). Brief literature survey of methodologies, ideas, techniques and approaches used by various researchers is highlighted and use of hybrid feedstock filament-based pattern to produce metal matrix composite is duly discussed.

Pattern replica required for investment casting (IC) of biomedical implant, machine parts, dentistry and other industrial components can be directly produced by using FDM process is presented. Relevant studies and examples explaining the suitability of FDMAIC for various applications are also presented.

Researches to optimize the conventional IC with FDM solutions and develop new hybrid feedstock filament of FDM done by researchers worldwide are also discussed. The review highlights the benefit of FDMAIC to surgeons, engineers and manufacturing organizations.

The research related to this survey is limited to the suitability and applicability of FDMAIC.

This review presents the information regarding potential IC application, which facilitates the society, engineers and manufacturing organizations by providing variety of components for assisting FDM. The information reported in this paper will serve doctors, researchers, organizations and academicians to explore the new options in the field of FDMAIC.

A pine/co-PES composite (PCPES composite) was proposed as the feedstock for powder bed fusion (laser sintering, LS). This paper aims to provide some necessary experimental data and the theoretical foundation for LS of pine/co-PES, especially for the application of using the laser-sintered pine/co-PES parts as complex structural patterns in investment casting.

The PCPES composites with different pine loadings were mixed mechanically. The composite’s preheating temperature and processing temperature during LS were determined experimentally based on the material’s thermal behavior. The effects of pine powder on the binding mechanism of PCPES composites were discussed through analyzing the microstructure of the laser-sintered parts’. Mechanical properties and dimensional precision of laser-sintered PCPES parts in different pine loadings were tested, and the parts’ mechanical properties were strengthened by wax-infiltration post-processing. The influence extents of process parameters on the mechanical properties of laser-sintered 20 Wt.% pine/co-PES parts were investigated using a 1/2 fractional factorials experiment.

20 Wt.% pine/co-PES is considered to be a promising wood-plastic composite for laser sintering. The relationship between mechanical strength of its laser-sintered parts and process parameters was built up using mathematical formulas. Experimental results show density, tensile strength, flexural strength and surface roughness of laser-sintered 20 Wt.% pine/co-PES parts are improved by 72.7-75.0%, 21.9-111.3%, 26.8-86.2%, 27.0-29.1% after post-process infiltration with a wax. A promising application of the wax-infiltrated laser-sintered parts is for investment casting cores and patterns.

The proper process parameters and forming properties of laser-sintered parts are limited to the results of laser sintering experiments carried on using AFS 360 rapid prototyping device.

This investigation not only provides a new feedstock for laser sintering with the advantages of low cost and fabricability but also uses an advanced technique to produce personalized wood-plastic parts efficiently. Mathematical models between mechanical properties of laser-sintered PCPES parts and LS process parameters will guide the further LS experiments using the 20 Wt.% pine/co-PES composite. Besides, the laser-sintered PCPES parts after wax-infiltration post-processing are promising as complex structural patterns for use in investment casting.

The purpose of this paper is to verify the feasibility and evaluate the dimensional accuracy of two rapid casting (RC) solutions based on 3D printing technology: investment casting starting from 3D‐printed starch patterns and the ZCast process for the production of cavities for light‐alloys castings.

Starting from the identification and design of a benchmark, technological prototypes were produced with the two RC processes. Measurements on a coordinate measuring machine allowed calculating the dimensional tolerances of the proposed technological chains. The predictive performances of computer aided engineering (CAE) software were verified when applied to the ZCast process modelling.

The research proved that both the investigated RC solutions are effective in obtaining cast technological prototypes in short times and with low costs, with dimensional tolerances that are completely consistent with metal casting processes.

The research assessed the feasibility and dimensional performances of two RC solutions, providing data that are extremely useful for the industrial application of the considered technologies.

The paper deals with experimental work on innovative techniques on which data are still lacking in literature. In particular, an original contribution to the determination of dimensional tolerances and the investigation on the predictive performances of commercial CAE software is provided.

THE use of investment castings for aerospace applications dates back to the Second World War when components made by this process were utilized in the construction of superchargers for piston aero engines and nozzle components for turbojet engines. The technique has developed enormously since that time so that today investment castings are used widely for aircraft and missile applications—particularly on the power plant side for blading.

Conventional investment casting of turbine blades is a time consuming and expensive process due to the complications in wax injection steps and the complex shape of airfoil surfaces. By using rapid investment casting, a substantial improvement in the gas turbine blade manufacturing process can be expected. However, this process needs to be able to compete with conventional investment casting from a dimensional accuracy view of point. The purpose of this paper is to investigate the manufacture of gas turbine blades via two indirect rapid tooling (RT) technologies, namely epoxy (EP) resin tooling and silicon rubber molding.

The second stage blade of a Ruston TA 1750 gas turbine (rated at 1.3 MW) was digitized by a coordinate measuring machine. The aluminum‐filled EP resin and silicon rubber molds were fabricated using StereoLithography master models. Several wax patterns were made by injection in the EP resin and silicone rubber molds. These wax patterns were utilized for ceramic shell fabrication and blade casting.

Dimensional inspection of cast blades showed that silicone rubber molding was not a suitable approach for production of blade wax patterns. The maximum deviation for the final cast blade made using the silicone rubber mold was +0.402 mm. The maximum deviation for the final cast blade made using the EP resin mold was lower at −0.282 mm. This showed that EP resin tooling could enable new cost‐effective solutions for small batch production of gas turbine blades.

The research results presented will give efficient industrial approach and scientific insight of the gas turbine blade manufacturing by use of rapid technologies.

There are some general research works related to utilization of rapid technologies for manufacturing of gas turbine blade. However, this paper presents a unique procedure of integrated reverse engineering and RT technologies for rapid investment casting of gas turbine blade through presenting comprehensive comparison between two techniques from dimensional accuracy view of point.

The purpose of this paper is to develop a novel process of integral ceramic molds for investment casting of hollow turbine blades.

At first, a resin pattern of a hollow turbine blade prototype is fabricated by stereolithography (SL). And then aqueous gelcasting process is utilized to fill the resin pattern with ceramic slurry of low viscosity and low shrinkage, through in situ polymerization of the slurry a ceramic mold is formed. At last, the ceramic mold for investment casting of hollow turbine blade is obtained by vacuum drying, pyrolyzing and sintering.

An integral ceramic mold is successfully fabricated by combining SL and gelcasting process, cores and shell are connected with each other and thus high relative position accuracy is guaranteed. Properties of integral ceramic mold at room temperature and high temperature satisfy the requirements of directional casting for complex‐shaped thin‐walled blades.

Because the integral ceramic mold is a close body, it is very difficult to directly measure its inner dimensions and the relative position accuracy of cores and shell, and the further research is needed.

This method enhanced the versatility of using SL prototype in the fabrication of integral ceramic mold for investment castings. Although this paper took a hollow turbine blade as an example, this method is also capable of fabricating integral ceramic molds for other complex investment castings.