Casting Process Research Articles

Fuzzy System for fault detection in electric motors for aluminum casting

This study presents the development of a fuzzy logic-based system for predictive fault detection in electric motors used in aluminum casting processes. The addressed problem concerns the need to optimize predictive maintenance in a competitive industrial environment, minimizing unexpected downtimes and costs associated with corrective maintenance. The main objective was to create a fuzzy algorithm for real-time monitoring of critical variables such as temperature, pressure, and electric current. The methodology involved simulations of operational scenarios validated through experimental tests in a controlled environment. Results indicate that the proposed fuzzy system accurately identifies anomalies and issues preventive alerts, contributing to extending motor lifespan and improving operational efficiency. It is concluded that the developed solution can be integrated into industrial supervisory systems, enhancing reliability and productivity.

Investigation of Mo microalloying-induced intragranular ferrite and grain refinement

In this work, the Mo microalloying-induced intragranular ferrite and grain refinement are studied through macroscopic experimental research and microstructural simulation. The microstructure and precipitates in high-strength low-alloy steel (DH36 steel) were characterised by an optical microscope and transmission electron microscope to investigate the effect of Mo on fine crystal strengthening and intragranular ferrite induction. It is demonstrated through first-principle calculations that the surface energy of the MoC(100) plane is the lowest and the structural stability is the best, and molecular dynamic simulations confirmed that the phenomena observed in TEM experiments: the α-Fe (100) surface is the optimal orientation surface of the MoC(100) surface. The solidification structure of DH36 steel during the continuous casting process was simulated based on the cellular automaton-finite element (CAFE) model. Based on these observations, solidification structure simulations confirmed the phenomena observed in metallographic experiments. Meanwhile, it is proposed that the grain refinement and microstructure optimisation effect can be controlled by Mo microalloy content, so as to provide important theoretical data for practical shipbuilding steel DH36 production and improvement of the continuous casting quality.

Effect of rPET Content and Preform Heating/Cooling Conditions in the Stretch Blow Molding Process on Microcavitation and Solid-State Post-Condensation of vPET-rPET Blend: Part II—Statistical Analysis and Interpretation of Tests

This research explores how varying proportions of virgin polyethylene terephthalate (vPET) and recycled polyethylene terephthalate (rPET) in vPET-rPET blends, combined with preform thermal conditions during the stretch blow molding (SBM) process, influence PET bottles’ microscopic characteristics. Key metrics such as viscosity, density, crystallinity, amorphous phase relaxation, and microcavitation were assessed using response surface methodology (RSM). Statistical analysis, including Analysis of variance (ANOVA) and its power, supported the interpretation of results. The first part of the work details the experimental design and statistical methods. Positron annihilation lifetime spectroscopy (PALS) and amorphous phase density analysis revealed reduced free volume size, a substantial increase in free volume quantity, and a transformation toward ellipsoidal geometries, highlighting significant structural changes in the material. At the same time, the intrinsic viscosity (IV) and PALS studies indicate that the solid-state post-condensation effect (SSPC) is linked with microcavitation through post-condensation product diffusion. The conclusions, which resulted from the microstructure analysis, affected the material’s mechanical strength and were validated by pressure resistance tests of the bottles.

Theoretical Possibilities of Controlling the Cooling Rate in the Heat Treatment of Cast Iron with Water Mist

The goal of the cooling procedure after austenitizing in the austempering heat treatment process applied to ausferritic ductile iron (ADI) castings is to rapidly reduce the temperature throughout the casting volume to the temperature range of the process window in which the proper metal matrix structure is formed. The lower limit of this range must be higher than the temperature at which the martensitic transformation starts. The upper limit is selected to eliminate the possibility of diffusion decomposition of austenite while holding castings at austempering temperature. Most often in heat treatment practice, salt baths are used for this purpose. Such a solution makes it possible to realize in one device both the procedure of cooling down to a given temperature and the procedure of isothermal heat treatment at this temperature. Unfortunately, the presence of molten salt in the process equipment has a very unfavorable impact on working conditions and ecology. The purpose of this research is to analyze the possibility of replacing the salt bath cooling procedure in the manufacturing process of ADI castings with a water mist spray cooling procedure on the surfaces of the heat-treated casting. If such cooling is effective, subsequent procedures of heat treatment operations can be carried out with traditional furnace equipment without the use of molten salt, which will clearly improve the environmental performance of the enterprise. The article, using analytical and numerical methods, analyzes the possibility of cooling objects made of cast iron with the help of a high-efficiency heat receiver such as water mist spraying. The limiting conditions for carrying out the process for the assumed cooling curve were considered. It was proposed to use a PID controller realizing the function of controlling the intensity of water mist spray, which corresponds to the intensity of the heat flux received from the heat-treated casting. The theoretical analysis carried out allows us to conclude that water mist cooling according to the assumed cooling curve is practically feasible.

Evaluation of novel 3D printed foam patterns for rapid investment casting based on fused filament fabrication

Purpose This study aims to evaluate the effectiveness of three-dimensional (3D) printed foam polylactic acid (PLA) patterns in reducing ceramic shell stresses and cracking during burnout in the rapid investment casting (RIC) process to improve casting yield and dimensional tolerances. Design/methodology/approach Cylindrical and step-wedge patterns were 3D printed using foam PLA feedstock and compared with patterns from plain PLA and Polyvinyl Butyral (PVB). The patterns were shelled using ceramic slurry and investment cast in A356.1 aluminum alloy. Shell cracking and dimensional tolerances of resulting castings were assessed. Additionally, a complex component was 3D printed, laser-scanned, then cast and rescanned to evaluate dimensional accuracy. Finite Element Analysis (FEA) was conducted on cylindrical geometries to analyze internal mold pressure because of thermal stresses during burnout. Findings The foam PLA for all patterns produced no shell cracking during both ramp and flash burnouts. Castings made from foam PLA patterns showed improved dimensional tolerances and a narrower error distribution in GD&T analysis compared to those made from PLA and PVB. FEA results indicated that the thermomechanical properties of foam PLA reduce internal mold pressure by over 90%, which decreased internal shell stresses. Originality/value This research introduces a novel application of 3D printed foam PLA feedstock in the RIC process as a pattern material. This study demonstrates that foam PLA patterns effectively eliminate shell cracking during burnout and enhance dimensional accuracy. The findings of this study offer a new approach for improving dimensional tolerances and casting yield in RIC, which has not been previously explored.

Wear Study on Various Conditions of AA7075 Metal Matrix Composite with Silicon Carbide (SiC), Boron Carbide (B4C) as Reinforcement for Aerospace Applications

This study aims to investigate the wear behavior of AA7075 alloy with the reinforcement of Silicon carbide (SiC) and Boron carbide (B4C) particles. Process parameters are crucial for component quality improvement, particularly in metal matrix composites (MMCs), a unique class of materials used in a variety of technical applications, such as but not limited to automobiles, marine, and aeronautics.These are frequently utilized in challenging applications due to their significantly better strength to weight ratios, stiffness, and then standard materials. However, it has numerous disadvantages, including high weight ratios, high deformation and stresses, poor fatigue life cycle, early wear and friction, and so on. Up till now, numerous reinforcements have been employed to address these crucial problems. Due to its superior properties, aluminum matrix composites (AMCs) have been used in many different applications. This work attempts to examine the wear behavior of AA7075 alloy reinforced with silicon (SiC) and boron (B4C) particles utilizing the stir casting process AA 7075-(SiC)-(B4C) composites were produced by employing AA 7075 as the matrix material with silicon carbide (SiC) and boron carbide (B4C) particles as reinforcement in various percentages of weight (5%, 10%, and 15%). Parameters of the composites, including wear behavior, coefficient of friction, frictional force, and pin temperature were assessed through graphical representation.

Implementing multi criteria decision making methods for computing complexity involved in industrial investment castings

Investment casting is admired for its ability to produce industrial castings with remarkable precision, exceptional exterior finish and complex designs among diverse industrial application. Traditionally, the complexity of these castings is generally assessed qualitatively while quantitative measurement of this complexity remains largely unexplored. To identify the various parameters that affects the complexity of industrial investment casting, an in-person industrial survey was carried out in one of the major investment casting clusters that accounts for nearly 25% of India’s investment casting foundries. Through this survey it was found that complexity of investment casting is determined by three factors related to geometry, features and manufacturability. These three factors are further driven by 19 elements and 52 attributes. These 52 attributes are further characterised by 212 meta-attributes. This research focuses on applying multi-criteria decision-making methods to quantify the complexity affects in manufacturing industrial investment castings. Numerous methodologies within the domain of Multi-Criteria Decision-Making (MCDM) have been explored to determine the appropriate weightage for the factors, elements, attributes and meta-attributes involved. It was observed that for the specific problem mentioned, the Weighted Criteria Approach (WCA) and Analytical Hierarchy Process (AHP) were identified as suitable choices, aligning well with the required level of accuracy. The result obtained through these methods were used to compute the complexity of industrial castings. The proposed complexity index was validated using various industrial castings and proved to be a valuable tool for designers in adopting investment casting process for producing complex castings.

Assessment of evolutionary multi-objective optimisation algorithms for thermoplastic injection moulding in aluminium moulds

In order for a plastic part manufactured by the injection moulding process to be of the desired quality, it is necessary to maintain control of the process parameters. To increase productivity and reduce defects in parts, various studies have been carried out to propose and evaluate methods for determining injection parameters, which are based on the use of experimental planning techniques as well as the application of optimisation algorithms. This work proposes an experimental design with 6 injection parameters (mould temperature, melt temperature, injection time, percentage of volume filled, packing time and packing pressure) to determine the initial search space for the NSGA-II and MOEA/D optimisation algorithms in relation to the use of aluminium moulds. This procedure was used via a computer code described in the PYTHON programming language to determine the appropriate injection parameters for minimising part warpage and injection pressure. The results showed that an increase in compaction pressure is associated with a reduction in warpage and vice versa. It was observed that the NSGA-II algorithm showed a greater diversity of points on the approximate Pareto frontier compared to the MOEA/D algorithm. In the case of the proposed study, MOEA/D presented a more viable solution, determining a lower packing pressure, less warping of the part and a shorter cycle time. The NSGA-II algorithm required around 4 hours more computational time than MOEA/D.

Process Optimization Using Cuckoo Search Algorithm for the Manufacturing of Resin Transfer Moulded Composite Parts

An in-house coded cuckoo search (CS) optimization algorithm integrated with a finite element simulation model was developed for resin transfer moulding (RTM) process optimization. At first, the mould filling and curing phases, the vital stages of the RTM process were modelled in the COMSOL multi-physics simulator for RTM6 resin – carbon fibre reinforced composite part. Then, the model was imported to the CS optimization algorithm scripted in MATLAB using the COMSOL live link for MATLAB. The CS algorithm was developed for the minimization of dry spot content by finding the optimal positions of gates and vents during the mould-filling stage and the minimization of the thermal gradient by finding the optimal mould temperature during the curing stage. From the optimization results, there was a decrease in dry spot content and thermal gradient with an increase in generations. With the generations, converged-stable-optimal solutions were obtained. A dry spot content of 3% for one gate and one vent and 0.56% for two gates and one vent was obtained for two-dimensional and three-dimensional composite plates, respectively. From the curing phase optimization results, a minimum thermal gradient of 0.86 K with 98.1% degree of cure was obtained for the optimal mould temperature of 433 K. The developed CS algorithm predicted the better-automated mould-fill and cure phase optimal solutions for the studied composite part.

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.

X-ray computed tomography (XCT) study on the porosity structure of slag film in the steel continuous casting

X-ray computed tomography (XCT) has been utilised to investigate the porosity structure of slag films in the steel continuous casting. This study explores the efficacy of the XCT technique in characterising and identifying porosity within slag films, including comparisons between vertical XCT slices and images from traditional 2D methods. The results of 3D visualisation reveal that higher porosity on the mould side is not consistent across all slag films, thereby challenging existing beliefs. It has also been observed that the distributions of pore locations vary across different slag films, correlating with their porosity levels. In slag films with low porosity, pores predominantly appear on the sides, whereas, in those with higher porosity, they are distributed throughout. Analysis of pore sizes across various slag films indicates that, although smaller pores are numerous, larger pores significantly contribute to the overall porosity. This research in-depth explored the internal pore structure in slag film via XCT technique, offering insights for optimising slag film design and improving steel casting processes.

Application of neural network in steelmaking and continuous casting: A review

With the improvement of computer computing power and the development of big data technology, neural networks have rapidly developed and been effectively applied in multiple fields. Steelmaking and continuous casting are key stages in steel production, involving complex physical changes and chemical reactions under high-temperature conditions. Neural networks, with the advantage of intelligently mining patterns from massive data, have been extensively researched and applied in the control of steelmaking and continuous casting processes. This paper uses a systematic literature review method to summarise the applications of neural networks in converter steelmaking, electric arc furnace (EAF) steelmaking, secondary refining and continuous casting over the past two decades. The main areas include the prediction of endpoint temperature and composition in converter and EAF smelting, optimisation of EAF power supply parameters, prediction of breakout in continuous casting and predictive control of billet and slab defects in continuous casting. It summarises and analyses the advantages and effects of different types of neural networks applied in steelmaking and continuous casting. Finally, the paper highlights the potential and challenges of neural networks in the application of steelmaking and continuous casting and points out future development directions.

Scheduling of steelmaking-continuous casting process by integrating deep neural networks with mixed integer programming

This study addresses the scheduling problem in the steelmaking-continuous casting (SCC) process. The SCC process is a hybrid flow shop with three stages, and we focus on job dispatching in the second stage, the refining stage. Our primary aim is to develop an algorithm applicable to real-world scenarios, mirroring field engineers’ decision-making and handling the process’s complex features. We propose a deep neural network (DNN)-based approach, trained on engineers' past decisions, achieving up to 97% accuracy. However, DNN alone falls short of outperforming engineers in scheduling objectives, specifically minimizing the total completion time in the refining stage. Hence, we introduce a novel approach combining DNN with mixed integer programming (MIP). In the integrated approach, the DNN initially makes decisions, but when confidence in the accuracy of a DNN-based decision is lacking, as determined by a developed reliability measure, it is supplemented with a decision derived using MIP. Experiments demonstrate that this integration improves scheduling objectives, surpassing engineers' performance. Furthermore, filtering inaccurate decisions enhances the accuracy of the DNN-based decisions. The proposed approach has been successfully implemented in one of South Korea's largest steelmaking companies.

Influence of temperature regime of the combined process of casting and rolling of strips from high-alloy aluminium alloys

Influence of temperature regime of the combined process of casting and rolling of strips from high-alloy aluminium alloys

Change in Alloy Composition and Manufacturing Technology of Bronze Vessels Excavated from Archaeological Site

The purpose of this study is to analyze the components and observe the microstructure of 98 bronze vessels excavated from the Korean Peninsula, where such vessels were widely used, to identify the alloy composition and manufacturing process of bronze artifacts by period. In addition, we attempted to confirm the changing processes of bronze manufacturing technology by reviewing previous studies. The analysis results showed that the bronzeware of the Unified Silla period was mainly produced using a Cu-Sn binary alloy containing 20-26 wt% Sn, which was cast and slowly cooled. Also, most bronze vessels from the Goryeo dynasty were made of a Cu-Sn binary alloy, with an Sn content of 20–24 wt%. In the microstructure, a twinned α phase and β(M) phase were observed, indicating that the container was manufactured using the casting–hot forging–quenching process. Bronze vessels from the Joseon dynasty were made using a binary alloy of Cu-Sn and a ternary alloy of Cu-Sn-Pb. In order to confirm the changes in bronze manufacturing technology over time, we investigated the alloy composition and microstructure of a total of 295 vessels, including previous studies, and were able to confirm the dominant production technology for each period. During the Unified Silla period, the shape of the container was made by casting using a Cu-Sn binary alloy, and the process was completed through slow cooling or quenching. During the Goryeo Dynasty, vessels were made using a Cu-Sn binary alloy through a process of casting, hot forging, and quenching, or by casting a Cu-Sn-Pb ternary alloy into a vessel shape and then slowly cooling it. And in the Joseon Dynasty, the use of ternary alloys of Cu-Sn-Pb increased.

Effect of depressions on the three-dimensional propagation of longitudinal surface crack in continuous casting strands of hypo-peritectic steel

Surface depressions are common defects in the continuous casting process of hypo-peritectic steel, categorised into transverse and longitudinal depressions. While the formation processes of these depressions are well understood, their influence on the propagation of longitudinal surface cracks remains unclear. This study developed a new method for large-area surface depth measurement of the strand based on optical focusing. Utilising this method, we investigated the impact of surface depressions on the three-dimensional propagation of longitudinal surface crack in terms of width, depth and casting direction through macrostructure characteristics and finite element static analysis. The results indicated that transverse and longitudinal depressions affect crack propagation differently. Specifically, the depth of longitudinal depressions increases crack width, while transverse depressions can inhibit it to some extent. Macrostructure analysis of the cross-section revealed a continuous segregation line at the bottom of the depression, closely related to heat transfer, thermal stress, and the flow of solute-enriched liquid steel. Statistical analysis indicated that the depth of longitudinal depressions significantly affects the length of the segregation line, which in turn influences crack depth. However, no surface cracks were found at the bottom of transverse depressions adjacent to longitudinal depressions. This may be due to the banded grains at the bottom of transverse depressions resisting tensile thermal stress parallel to the strand's width direction. Along the casting direction, crack propagation primarily occurs along the bottom of longitudinal depressions due to continuous thermal disturbances at the meniscus. Meanwhile, the transverse depression can restrict the propagation of the existing crack while promoting the formation of the new crack at the other site. Finally, the study proposed mechanisms by which depressions influence the propagation of the longitudinal surface crack. This research further elucidates the impact patterns of surface depressions on macrostructure and stress distribution and how these factors subsequently influence crack propagation in three dimensions. It underscores the importance of controlling surface depressions to mitigate the longitudinal surface crack.

Fabrication of Porous TiO2 with Aligned Pores Using Tert-Butyl Alcohol Based Freeze Casting

The effect of freezing conditions on the pore structure of porous TiO2 fabricated using a freeze casting process with tert-butyl alcohol slurry was investigated. The raw powder was heat treated to manufacture a porous body with stable shape by suppressing volume change induced by the phase transformation of TiO2 powder. The XRD analysis result indicated that the anatase phase fraction in the raw powder could be converted to 98.6% rutile phase by heating it at 800°C for 30 min. Porous TiO2 ceramics with unidirectionally aligned pore channels were prepared by freezing tert-butyl alcohol slurry containing 10 vol% TiO2 powder under different freezing temperatures of -20, -30 and -40°C, respectively. After removing the frozen tert-butyl alcohol by sublimation in vacuum, the green samples were sintered at 1100°C for 4 h in air. The porous body contains directional pores with a size of approximately 30 µm, and a hexagonal pore shape corresponding to the crystal structure of tert-butyl alcohol was observed in the cross-section of the pores. Small pores were observed at the bottom part of the porous body near the Cu plate where the solidification heat was released, but relatively large pores were present at the upper part. Both microstructure observation and pore size distribution indicated that the pore size and the strut thickness increased significantly with increasing freezing temperature. The change in pore characteristics was explained as being mainly due to the difference in the solidification behavior of the slurry and the rearrangement of the solid powder.

Temperature fields prediction for the casting process by a conditional diffusion model

Temperature fields prediction for the casting process by a conditional diffusion model

Numerical simulation on metallurgical transport phenomena during DC casting of large-size Mg-Gd-Y alloy billet under various magnetic fields

The manufacture of large-size rare-earth Magnesium alloy billets by direct-chill (DC) casting process is now facing great technical challenges, mainly due to the high cost of casting experiments, and such casting defects like severe cracking, macrosegregation, and non-homogenized solidified structure. Based on the low-cost and high-efficiency numerical simulation method, the present study systematically explores the effects of single pulsed and alternating magnetic fields (PMF and AMF), differential-phase pulsed and alternating magnetic fields (DPMF and DAMF) on DC casting metallurgical transport phenomena of Φ750mm large-size Mg-Gd-Y magnesium alloy billet. The electromagnetic characteristics, flow behavior, and temperature distribution during casting process are clearly visualized. The results demonstrate that traditional DC casting process is optimized by applying magnetic fields. For one thing, pulsed magnetic fields (PMF and DPMF) generate larger and faster changing instantaneous Lorentz forces, which induce huge transient impact and oscillation effects on the melt. For another, alternating magnetic fields (AMF and DAMF) have greater time-average Lorentz forces, which can force the melt to flow continuously and steadily. Additionally, differential-phase magnetic fields (DPMF and DAMF) enhance the melt’s axial flow strength while reducing the small vortex generated at the edges, resulting in improved heat transfer efficiency and uniform temperature field. Among those magnetic fields, DPMF shows dual advantages of transient oscillation and homogenized temperature distribution. Based on the present simulated research, the Φ750mm large-sized rare earth Mg-Gd-Y alloy billet with high-quality and non-cracking has been manufactured during DC casting process under DPMF.

Effect of modified investment and casting system on the recasting properties of high-gold alloy

Background/purposeHigh gold (Au) alloys have many advantages, such as good mechanical properties and stable chemical properties for dental restoration. The purpose of this investigation was to investigate the effect of zirconia (ZrO2)-magnesia (MgO)-based investment combined with an argon arc vacuum pressure (Ar-arc VP) casting process on the recasting of high Au alloys. Materials and methodsThe recasting Au alloys were compared between the control group of conventional SiO2-based investment/horizontal centrifugal (HC) casting and the experimental group of ZrO2–MgO-based investment/Ar-arc VP die casting. The first-generation castings were defined as being made from purchased Au alloys and the second-generation castings were made from 50 wt% of the original Au alloys before casting, plus the balance of the remaining 50 wt% from the previous castings. The third-generation was made from all old surplus from the previous castings. The ingots were measured for the marginal gap, surface roughness, interface oxidation, hardness, and phase identification. ResultsThe results showed that the recasting success rate reached 100%. The ZrO2–MgO-based investment/Ar-arc VP group had better edge precision, smaller oxide layer thickness, and lower hardness than the SiO2-based investment/HC group. However, surface roughness analysis indicated little difference between the two groups. Phase analysis showed that the recasting alloys of the second and third-generation groups contained higher Au contents than those of the first-generation. ConclusionOverall, the Au alloy can be better recasted and retain good mechanical properties under the clinically used 5 wt% ZrO2–MgO-based investment/Ar-arc VP casting method.