International Journal of Metalcasting最新文献

A conventional cast-on (CCO) method requires costly procedures for steel insert and molten metal processing in order to produce bimetal sand castings of acceptable bonding quality. We explored the feasibility of using ultrasound-assisted cast-on methods for producing bimetal sand castings using as-received bare steel inserts and an un-degassed molten A354 alloy. The ultrasound-assisted methods included a direct ultrasound-assisted cast-on (DUACO) method and an indirect ultrasound-assisted cast-on (IUACO) method. Mathematical calculations were carried out to understand the conditions under which metallurgical bonding could occur using CCO in a sand casting process. Results of this study showed that the CCO method was unable to metallurgically bond as-received bare steel inserts to A354 alloy under the conditions of this study. The IUACO method was capable of producing metallurgical bonding between as-received inserts and un-degassed A354 alloy with bonding quality identical to that obtainable using coated inserts following stringent procedures for insert and molten metal processing. The DUACO method was much more effective than the IUACO method in producing high-quality metallurgical bonding between bare steel insert and un-degassed A354 alloy. Microstructural analysis indicated that a continuous layer of intermetallic phases was formed at the steel/aluminum interface in castings produced by DUACO method. Push-out test revealed that the shear strength of the bond produced by the DUACO method approached around 80 MPa. Mechanisms under which defect-free metallurgical bonding was formed were discussed.

Cast aluminum alloys have been increasingly used in internal combustion engine cylinder head applications because of their lightweight and high thermal conductivity. Increasing demand for fuel economy and high power density has posed a significant challenge on existing cast aluminum alloys for high temperature performance. This paper reports a study of evaluating two new high temperature cast aluminum alloys Al–Q (AlSiCuMg) and ACMZ (AlCuMnZr) using semi-permanent mold (SPM) cast cylinder heads. While the new cast aluminum alloys, especially the ACMZ alloy, show a significant improvement in high temperature tensile properties, they exhibit similar fatigue performance as the commonly used cylinder head aluminum alloy A356+0.5%Cu. This is due to the presence of casting defects which is limited by existing casting processes for cylinder heads.

Measurement of secondary dendrite arm spacing (SDAS) is one of the key metrics for quantifying the microstructural evolution and cooling rate of solidified cast metallic alloys. This measurement provides a practical way to evaluate the effects of microstructure on material properties. In the current work, the spacing transform was used to measure the SDAS of a cast high-strength low-alloy (HSLA) steel wedge, and the results were compared with those from both solidification modeling and traditional line intercept methods. It was shown that the results obtained from the three methods closely matched. The results provide quantitative agreement as well as a spatial distribution of spacing from more rapidly cooled sections to slower cooled areas. This further opens more opportunity for the spacing transform to be used in generating very robust and statistically reliable dataset needed for advanced microstructure quantification within a short time that would otherwise be impossible with the manual method.

Over the past few years, a variety of melting casting projects have been performed, in collaboration with industries and universities. Concentrated alloys and nonmetallic compounds were studied including high-Si cast iron, high-Mn steel, Al-added stainless steel, high-entropy alloy, Permalloy 80 Ni alloy, Stellite Co alloy, steelmaking slag, and lithium iron phosphate (LFP). A melt synthesis of LFP was developed. Cooling curves were measured using the thermal analysis cup method and differential thermal analysis. In addition, the phase diagrams of alloys were calculated using Thermo-Calc. This paper draws on some examples of the measured cooling curves to illustrate the thermal behaviors of melts, along with a quick review of each project.

Ensuring the precise grading of discontinuities is imperative to guarantee the quality of castings and enhance profitability in casting production. Recent grading methods leveraging computer vision are advanced by performing a single-label image classification or regression, which loses the intrinsically ordinal relationship. Motivated by this observation, we propose a label smoothing technology for ordinal variables to convert the level of each defect instance into a discrete probability distribution, aiming to model the noise label and ordinal relationship. Furthermore, we design a convolutional neural network framework based on multi-task learning. This framework, by simultaneously learning the level label distribution and regressing the level directly, outperforms a single-task network in terms of overall performance. Finally, we construct a casting gas porosity defect grading dataset. Experimental results on this dataset highlight the significant advantages of our proposed method compared to traditional single-label image classification or regression algorithms.

This paper focuses on casting self-healing metal matrix composites with aluminum alloy as a matrix, and Nitinol fiber as reinforcement. The shape memory effect of Nitinol fibers can cause them to shrink and narrow or close the crack in cast matrix material and the bend test restores the shape. Self-healing metal matrix composite samples were cast using (a) semi solid squeeze casting after Rapid Slurry Formation and (b) gravity die casting. Casting procedures, microstructures, and mechanical properties are reported. The cast aluminum-Nitinol fiber composite samples prepared by both techniques were cracked under tensile and bending, both types of samples exhibit a reduction in the width of the crack in the samples upon heating the sample above the transformation temperature of Nitinol. Under certain conditions, the cracks in permanent mold cast alloys reinforced with Nitinol have been completely sealed leading to self-healing.

The constant evolution of computer-aided engineering techniques continues to enhance our understanding of the liquid metal cooling process. Conventional methodologies often rely on fixed heat exchange coefficients for computing the thermal interaction between casting modules and the liquid coolant. This paper introduces a novel coupled numerical scheme integrating computational fluid dynamics (CFD) and solidification analysis. Within this framework, the commercial software ANSYS Fluent® addresses the CFD aspect of the coolant, while the ProCAST® handles the solidification simulation. The solidification process is simulated through solving heat conduction equation in which the heat flux boundary condition on the external surface of the cast module is determined according to the convective coolant flow. The CFD simulation based on the Navier–Stokes equations furnishes the heat flux at the module-coolant interface, taking the temperature field of the cast module as input. The coupled method is validated first with a test case in which a module is immersed inside coolant, followed by a multiphase flow simulation, wherein a casting module is pulled into the liquid tin bath from a gas-phase position. Both simulations reveal temperature variations in the coolant. Comparison with conventional heat exchange coefficient approaches confirms the influence of these variations on cooling curves. The coupled model is further used to investigate the effect of withdrawal rates on the solidification process, exhibiting qualitative agreement with experiments.

High-energy ultrasound has been proposed to ameliorate the uniform dispersion the reinforcement and used widely fabricating in high strength metal matrix composites. In this work, 7085 aluminum (Al) metal matrix composites with varying contents of reinforcement carbon nanotubes (CNTs) (0–1.2 wt.%) were fabricated by surface modification treatment and ultrasound-assisted casting technique. The wear resistance and mechanical properties of the composites were investigated. The addition of reinforcement CNTs particles led to an improvement in the mechanical properties of the composite from 126.4 to 187.2 MPa and the hardness from 128 to 169.4 HV. The wear resistance results showed that with the addition of the 1.0 wt.% CNTs, the wear rate of the reinforcement composite was 1.42×10⁻³mm³/Nm, and the depth and width of the abrasion marks reached the optimal value. At the same time, the formation of Al₄C₃ will be beneficial to further enhance the mechanical properties of the alloy. However, excess CNTs tends to agglomerate, which will deteriorate the properties of the composites. This also indicates that the synergistic fabrication of multiple processes could offer CNTs/7085 composite a great promising candidate for engineering applications.

Data-centric near-real-time intelligent process control for smart manufacturing in an Industry 4.0 era is of tremendous value. Design and manufacturing of high-performance ductile iron sand castings is a multi-variant complex process with much uncertainty involved. As a result, in spite of a well-controlled operation and an experienced workforce, iron foundries in a production environment do face sporadic shrinkage and lots with nonconforming property requirements, resulting in scrap or rework. A framework and methodology consisting of AI (artificial intelligence) and ML (machine learning) tools, coupled with ICME (integrated computational materials engineering) and process simulation tools, will be presented to quantify uncertainty (UQ). Metamodels, both predictive and prescriptive in near real time were developed using such AI/ML techniques using historical production and selective design of experiments (DOE)-generated additional data. The data will be presented including details on successful corrective action production trials. The proposed framework and approach is applicable to solve such complex problems encountered in the foundry and machining operations where there is uncertainty.

The latest generation of high-performance cylinder blocks are produced with the Precision Sand Castings Process which sometimes uses a monolithic cast iron chill that promotes enhanced solidification conditions that improve both tensile and fatigue performance. However, monolithic chills tend to be re-used quite quickly and thus can be significantly above room temperature during the next subsequent casting. To deter the potential deleterious effects, this may cause most casting practitioners to enforce a strict room temperature condition of the chill prior to use. However, this may necessitate a higher number of chills in the system or the implementation of chill cooling stations, both of which involve higher capital investment. The authors will address the most optimal method to address thermal management of chills by documenting the mechanical and microstructural property impact and its compliance in the bulkhead (next to chill) and head bolt columns (60 mm away from the chill) to the chill temperature prior to casting. The Quality Index will reflect the chill temperature impacts based on the mechanical properties results for this manuscript.