Low Pressure Die Casting Research Articles

Uneven distribution of cooling rate, microstructure and mechanical properties for A356-T6 wheels fabricated by low pressure die casting

Complex aluminum alloy part prepared by low pressure die casting (LPDC) usually has unevenly distributed microstructure and mechanical properties, which is related to the cooling conditions. To investigate the uneven characteristics of cooling rate, microstructure and mechanical properties of LPDC-fabricated A356-T6 wheels, the typical positions (including hub, spoke, outer flange, rim and inner flange) were selected to calculate the cooling rate by casting simulation. Then, the microstructure, mechanical properties, X-ray result and element content of these positions were analyzed in detail. The result shows that, the secondary dendrite arms spacing (SDAS), eutectic silicon and β-Fe phase have the largest size at hub, and the eutectic silicon is plate-shaped when the cooling rate is 10 K/min, which results in poor ultimate tensile strength (UTS, 192 MPa) and elongation (2.1 %). While at the cooling rate of 100 K/min, refined microstructure, the disappearance of β-Fe phases and the elimination of defects result in good mechanical properties of the outer flange. The UTS and elongation exceed 250 MPa and 13 %, respectively. The rim is more prone to occur shrinkage defects at high cooling rate, which results in mechanical properties that do not meet expectations. Furthermore, the Si, Mg, and Fe elements also exhibit uneven distribution in different positions of LPDC-fabricated A356-T6 wheels. The maximum deviation of Si element between the actual content and the standard content is as high as 21 %.

Development of Low-Pressure Die-Cast Al-Zn-Mg-Cu Alloy Propellers Part II: Simulations for Process Optimization.

With the increasing demand for high-performance leisure boat propellers, this study explores the development of high-strength aluminum alloy propellers using the low-pressure die-casting (LPDC) process. In Part I of the study, we identified the optimal alloy compositions for Al-6Zn-2Mg-1.5Cu propellers and highlighted the challenges of hot tearing at the junction between the hub and blades. In this continuation, we developed a coupled thermal fluid stress analysis model using ProCAST software to optimize the LPDC process. By adjusting casting parameters such as the melt supply temperature, initial mold temperature, and curvature radius between the hub and blades, we minimized hot tearing and other casting defects. The results were validated through simulations and practical applications, showing significant improvements in the quality and structural integrity of the propellers. Non-destructive testing using X-ray CT confirmed the reduction in internal defects, demonstrating the effectiveness of the simulation-based approach for alloy design and process optimization.

Adaptive neuro-fuzzy inference system approach for tensile properties prediction of LPDC A357 aluminum alloy

This study is based on the desire of aluminum casting foundries to understand the influence of minor changes, within the specification limits, in the alloy chemistry. In order to ensure the casting of A357 Al alloys within the framework of the casting standards and to minimize the quality problems that may arise during casting; the estimation of ultimate tensile strength (UTS), yield strength (YS) and elongation (ε) due to very small changes among the alloying elements, although they are in the standard range, by using machine learning method (ML), were studied. The dataset of chemical composition and tensile properties of Low-Pressure Die Cast (LPDC) A357 Al alloy were experimentally established. The relationship between five input variables in the A357 alloy, namely the main alloying elements Si and Mg together with the most common impurity contents Fe, Ti and Cu were selected and three outputs (i.e UTS, YS and ε) were linked by Adaptive Neuro Fuzzy Inference System (ANFIS). The ANFIS model predicted that the most detrimental element affecting tensile properties was Fe content. According to this model, the order of the relative importance on UTS, YS and ε revealed as Si, Mg and Ti content respectively after the Fe content of the alloy.

Development of Low-Pressure Die-Cast Al-Zn-Mg-Cu Alloy Propellers-Part Ⅰ: Hot Tearing Simulations for Alloy Optimization.

Recent advances in the leisure boat industry have spurred demand for improved materials for propeller manufacturing, particularly high-strength aluminum alloys. While traditional Al-Si alloys like A356 are commonly used due to their excellent castability, they have limited mechanical properties. In contrast, 7xxx series alloys (Al-Zn-Mg-Cu based) offer superior mechanical characteristics but present significant casting challenges, including hot-tearing susceptibility (HTS). This study investigates the optimization of 7xxx series aluminum alloys for low-pressure die-casting (LPDC) processes to enhance propeller performance and durability. Using a constrained rod-casting (CRC) method and finite element simulations, we evaluated the HTS of various alloy compositions. The results indicate that increasing Zn and Cu contents generally increase HTS, while a sufficient Mg content of 2 wt.% mitigates this effect. Two optimized quaternary Al-Zn-Mg-Cu alloys with relatively low HTS were selected for LPDC propeller production. Simulation and experimental results demonstrated the effectiveness of the proposed alloy compositions, highlighting the need for further process optimization to prevent hot tearing in high Mg and Cu content alloys.

Development of the Low-Pressure Die Casting Process for an Aluminium Alloy Part.

The low-pressure die casting (LPDC) process was experimentally and numerically studied to produce AlSi7Mg0.3 components such as steering knuckles. Steering knuckles are important safety components in the context of a vehicle's suspension system, serving as the mechanical interface that facilitates the articulation of the steering to control the front wheel's orientation, while simultaneously bearing the vertical load imposed by the vehicle's weight. This work focuses on the development of a numerical model in ProCAST®, replicating the production of the aforementioned part. The model analyses parameters such as the filling dynamics, solidification process, and presence of shrinkage porosities. For the purpose of evaluating the quality of the castings, six parts were produced and characterised, both mechanically (tensile and hardness tests) and microstructurally (porosity and optical microscopy analysis). When correlating simulation results with the available experimental data, it is possible to conclude that the usage of the LPDC process is a viable alternative to the use of steels and other metals for the production of very high-quality castings while using lighter alloys such as aluminium and magnesium in more demanding applications.

Improving heat transfer and compressed air consumption in low pressure die casting of aluminum wheels

Automotive wheels made from aluminum alloys are generally manufactured using the Low-Pressure Die Casting (LPDC) method. The molten aluminum is pressurized and filled into the cavities between the molds, and it is cooled using cooling channels in the LPDC. While the cooling fluid can be either air or water in industrial applications, only air cooling is utilized within the scope of this study. The primary expenditures of the process are attributed to the cycle time and the consumption of compressed air. In industrial settings, the primary coolant supply, emanating from either one or two inlet sources, is systematically channeled through a series of nozzles. These nozzles are strategically designed to impinge air onto specific areas of the mold pockets, ensuring efficient cooling.This research focuses on enhancing the cooling channels and mold pocket regions in LPDC through a combination of experiments and Computational Fluid Dynamics (CFD) integrated with Conjugate Heat Transfer (CHT) analyses. The study involves designing cooling channels to achieve a uniform mass flow distribution at the nozzle outlets, and optimizing the mold pocket regions. This optimization resulted in a 46% reduction in compressed air flow while maintaining comparable levels of heat transfer in the optimized mold pocket design. Moreover, flow rate non-uniformity at nozzle outlets was reduced from 11.5% to 2.0% to achieve uniform cooling and temperature distribution across the wheel spokes. An experimental setup with a cooling pipe network allowed simultaneous flow rate measurements from 20 outlets using flow rate sensors.The improved cooling channel and mold pocket design were adapted to the LPDC, and 20 aluminum alloy wheels were successfully cast with a 46% lower mass flow rate. All these wheels were tested in accordance with the relevant test specifications and the results obtained were assessed as compliant according to certain limits.

Experimental Evaluation of Ceramic Coatings for Die Protection in Low-Pressure Die-Casting Process

One of the most important factors in the LPDC process is the heat transfer during the solidification of the molten alloys, which is responsible for the resulting microstructure and, thus, the quality of the cast piece. The use of foundry coatings has been lately suggested as a proper strategy to control the heat transfer while protecting bonded moulds from aluminium adhesion by providing a barrier between the surface and the liquid metal. LPDC die coating failures usually come from the loss of adherence or excessive wear originated in the successive filling processes, which requires stopping production for the reapplication of the coating. In the present work, coatings with different insulation capabilities have been evaluated, in terms of adherence and wear tests, in order to select the most promising alternative for LPDC die coating. This study confirmed that surface preparation and cleanliness are vital for an adequate adhesion of the coatings to ensure their durability. The results evinced that the use of a primer layer provided a higher adhesion of the coatings and considerably improved their perfomance. The coating that presented the best results in terms of adhesion and wear resistance under different abrasive testing conditions was coating B3.

Comparative Life Cycle Assessment of Green Sand Casting and Low Pressure Die Casting for the production of self-cleaning AlMg3-TiO2 Metal Matrix Composite

The growth in the use of novel materials, as it is the case of the Metal Matrix Composites (MMCs), is producing a positive impact in production processes, allowing to obtain final products with improved functionalities, such as an increase of the strength-to-weight ratio, or enhancement of the mechanical properties of the material, minimizing as well the environmental impacts and production costs without compromising the required technical properties. To determine and compare the environmental impact of different processes employing these materials, this paper provides a comparative analysis of the Life Cycle Assessment (LCA), under ISO 14040:2006 framework and European ILCD guidelines, of two different manufacturing technologies, Green Sand Casting (GSC) and Low Pressure Die Casting (LPDC), for the particular case of a self-cleaning doorknob, produced by an aluminium alloy reinforced with hard TiO2 nanoparticles, that confers special characteristics to the composite, such as an increase of the hardness value and tensile strength, a high wear resistance, a good chemical stability, and antibacterial properties. The results show a slight difference between both technologies in terms of kg CO2 eq. emitted, with just a 3,16 % variation, where GSC emissions are 13,098 kg, whereas 12,684 kg are released from LPDC. In addition, an economic analysis was performed, showing a 17 % cost reduction in case of LPDC. This study presents for the first time a comparative Life Cycle Assessment of GSC and LPDC, when employing new nanocomposite materials, contributing with novel datasets and meaningful insights to improve the state of the art in the field, serving as well as a support for manufacturers in decision making process involving the use of these technologies.

Towards the Prediction of Tensile Properties in Automotive Cast Parts Manufactured by LPDC with the A356.2 Alloy

Aluminum-silicon-magnesium alloys are commonly used in the automotive industry to produce structural components. Among usual quality controls of produced castings, microstructure characterization and determination of mechanical properties are the most critical aspects. However, important problems can be found when measuring mechanical properties in those areas of castings with geometrical limitations. In this investigation, a set of A356 alloys have been prepared and then used to manufacture test castings and automotive castings in a laboratory and in industrial conditions, respectively, using Low Pressure Die Casting (LPDC) technology. Test castings were used to predict secondary dendritic arm spacing (SDAS) by using thermal parameters obtained from experimental cooling curves. The results have been then compared to the ones found in the literature and improved methods for estimating SDAS from cooling curves have been developed. In a subsequent step, these methodologies have been checked with different industrial castings by using simulated cooling curves and experimentally measured SDAS values. Finally, the calculated SDAS values together with the Mg contents present in A356 alloys and the temperature and aging time data have been used to develop new models so as to predict the tensile properties in different areas of a given casting prototype. These developed models allow casters and designers predicting tensile properties in selected areas of a given prototype casting even during design and simulation steps and considering the processing variables expected in a given foundry plant. The structures of these new models have been described and experimentally validated using different processing conditions.

Industry 4.0 Foundry Data Management and Supervised Machine Learning in Low-Pressure Die Casting Quality Improvement

Low-pressure die cast (LPDC) is widely used in high performance, precision aluminum alloy automobile wheel castings, where defects such as porosity voids are not permitted. The quality of LPDC parts is highly influenced by the casting process conditions. A need exists to optimize the process variables to improve the part quality against difficult defects such as gas and shrinkage porosity. To do this, process variable measurements need to be studied against occurrence rates of defects. In this paper, industry 4.0 cloud-based systems are used to extract data. With these data, supervised machine learning classification models are proposed to identify conditions that predict defectives in a real foundry Aluminum LPDC process. The root cause analysis is difficult, because the rate of defectives in this process occurred in small percentages and against many potential process measurement variables. A model based on the XGBoost classification algorithm was used to map the complex relationship between process conditions and the creation of defective wheel rims. Data were collected from a particular LPDC machine and die mold over three shifts and six continuous days. Porosity defect occurrence rates could be predicted using 36 features from 13 process variables collected from a considerably small sample (1077 wheels) which was highly skewed (62 defectives) with 87% accuracy for good parts and 74% accuracy for parts with porosity defects. This work was helpful in assisting process parameter tuning on new product pre-series production to lower defectives.

An approach to optimize cooling channel parameters of Low pressure Die casting process for reducing shrinkage porosity in Aluminium alloy wheels

A majority of automobile wheel market is occupied by aluminium alloys because of numerous advantages, one of which includes light weight. Most of these Aluminium alloy wheels are cast using Low Pressure Die Casting process. However this process results in several defects in the wheel rims, majority of which are due to shrinkage porosity. The presence of such defects reduce the fatigue life of wheels as they act as high-stress zones. Hence, it is paramount to understand the possible defects with their location and intensity and minimize it. One possible way is to perform a multi-physics simulation of filling and solidification process and estimate the defect location and intensity. However minimizing these defects, calls for a highly non-linear space Multi-disciplinary Design Optimization (MDO) problem. Existing techniques such as full factorial analysis may not be computationally efficient when it comes to contribution from large number of parameters. Shrinkage porosity is affected by various process parameters like cooling channels flow rate, delay and duration of flow, mold pre-heat temperatures, pressure cycle of molten metal, alloy composition, etc. The current research focuses on development of an efficient approach to solve a MDO problem in LPDC process of AlSi7Mg0.3 alloy wheel rim. The work specifically deals with minimizing the shrinkage porosity by optimizing the cooling channel parameters like delay and duration. This can also be extended for other parameters like flow rates, pre-heat temperatures, pressure cycle, etc.

A study of an industrial counter pressure casting process for automotive parts

Counter pressure casting (CPC) is emerging in the automotive manufacturing industry as an alternative to low-pressure die casting (LPDC) due to its reported superior capabilities in aluminum parts production. This study presents the first comprehensive investigation of how CPC's characteristic feature (applied chamber pressure) influences the fluid flow and heat transport occurring in the process and its effect on casting quality. A large amount of high-quality data was acquired from a commercial CPC process for the production of automotive suspension control arms with two process conditions (standard production and low back-pressure condition). Analysis of the data shows that there are no significant differences between the two process pressure conditions with respect to heat transfer during solidification, as-cast microstructure nor mechanical properties. Generally, in-die measured temperatures exhibited a difference within 10 °C for the two process conditions examined and the ultimate tensile strengths (UTSs) of the samples obtained from castings were within 7% for the two process conditions. Furthermore, there was no measurable difference observed in the Secondary Dendrite Arm Spacings (SDASs) obtained under the two process conditions. However, the implementation of chamber back pressure noticeably reduces the venting rate during the filling stage, leading to 12 s delay in the filling time relative to the low back-pressure condition. A computational modelling methodology, originally developed for LPDC, was applied to simulate the CPC process. The model required only an adjustment to pressure curve to account for the delay of filling owing to the reduced venting rate observed for the higher back-pressure condition. The predicted results were found to correlate well with the measured data, demonstrating that the modelling methodology is broadly applicable to permanent die casting processes.

EFFECTS OF AIR-COOLING-HOLE GEOMETRIES ON A LOW-PRESSURE DIE-CASTING PROCESS

A significant precondition for the production of high-quality castings is keeping an optimum temperature of the respective parts of the die cavity surface. This temperature depends on the temperature of the material, the quantity of metal, the method of cooling the casting die, the thermal conductivity of the die material, and the time during which the casting remains in the die. In addition, the cooling characteristics of alloy steel dies, used in the production of aluminum-alloy wheels with the low pressure die casting (LPDC) method, have critical effects on the mechanical and metallurgical properties of the product. Ducted air coolers are widely used for the cooling of these alloy steel dies. However, the geometrical designs of the air-cooling holes are limited. In this study, we define the effects of the geometry of the cooling holes on the cooling power of the die, the efficiency of the air consumption with the Full Factorial Experimental Design method and to determine the optimum values for LPDC. Pilot production has been carried out on an industrial scale to verify the data obtained by experimental design. The experimental and real data were compared based on the values of the yield strength and the secondary dendrite arm spacing in the microstructure.

Advanced Process Simulation of Low Pressure Die Cast A356 Aluminum Automotive Wheels—Part II Modeling Methodology and Validation

This manuscript presents an advanced modeling methodology developed to accurately simulate the temperature field evolution in the die and wheel in an industrial low-pressure die casting (LPDC) machine employed in the production of A356 automotive wheels. The model was developed in the commercial casting simulation platform ProCAST for a production die operating under production conditions. Key elements in the development of the model included the definition of the resistance to heat transfer across the die/casting interfaces and die/water-cooling channel interfaces. To examine the robustness of the modeling methodology, the model was applied to simulate production and non-production process conditions for a die cooled by a combination of water and air-cooling (Die-A), and to a second die for a different wheel geometry (Die-B) utilizing only water cooling for production conditions. In each case, the model predictions with respect to in-die and in-wheel temperature evolution were compared to industrially derived thermocouple (TC) data, and were found to be in good agreement. Once tuned to the process conditions for Die-A operating under production conditions, no further tuning of the die/casting interface resistance was applied. Additionally, the model results, in terms of the prediction of pockets of solid encapsulated liquid, were used to compare to x-ray images of wheels. This comparison indicated that the model was able to predict clusters of porosity associated with encapsulated liquid with an equivalent radius of ~27 mm.

Toward the development of a thermal-stress model of an industrial counter pressure casting process

It is argued that the Counter Pressure Casting (CPC) process is superior over low-pressure die casting (LPDC) in terms of reducing defects and improving cast product performance. To date, there has been relatively little research conducted on the CPC process to provide reliable data to confirm this argument. In this work, a plant trial has been done on an industrial CPC process using a custom-designed ‘H-shaped’ die to acquire an extensive amount of quantitative process data. The data acquired includes temperatures obtained from within the die, the casting, the surrounding environment, and within specific die cooling channels. The data has been processed and analysed to support the development of a comprehensive thermal-stress model of the casting process in order to better understand and quantify the essential macro transport processes. This paper presents a methodology of a coupled thermal-stress model development on the CPC process, and the preliminary results obtained from the models. The results to-date clearly show the need for a fully coupled thermal-stress analysis for the particular casting geometry and process conditions examined. Some of the challenges associated with the current modelling approach are also identified and potential solutions presented.

Advanced Process Simulation of Low Pressure Die Cast A356 Aluminum Automotive Wheels—Part I, Process Characterization

In this work, a plant trial was conducted on an industrial low pressure die casting (LPDC) manufacturing process for the production of aluminum alloy wheels. Various types of data have been acquired, including extensive measurements of temperature at different locations (die, wheel and cooling channels), pressure in cooling channels and size/location of shrinkage porosity in the produced wheels. Moreover, two process conditions were tested in the trial—one was the standard production process condition and the other was designed to generate shrinkage porosity in wheels by altering the die temperature. The large amount of quantitative data acquired in this study helped us to understand the key transport phenomena occurring in the process, which include: (1) a thorough picture of the evolution in temperature at a large number of discrete locations in the die and the casting; (2) the dynamic and complicated heat transfer in the cooling channels both water-on and water-off stages, associated with boiling water heat transfer. This paper (Part I) presents the results and findings obtained from the process characterization. The follow-on paper (Part II) will introduce the developed modeling methodology based on the data produced from this work.

Mechanical Properties and Conductivity of Low-Pressure Die-Cast 319 Aluminum Prepared with Hot Isostatic Pressing, Thermal Treatment, or Chemical Treatment

Low-pressure die-cast (LPDC) 319 Al alloy is commonly processed with hot isostatic pressing (HIP), heat treatment, and chemical eutectic silicon modification for advanced automotive applications. Yet, the microstructure can be similarly influenced by each of these processes, which may obscure their isolated benefits. Hence, this study aimed to differentiate their effects by processing step blocks of LPDC 319 alloy with either a HIP or ambient-pressure heat treatment, each for 2 h at 500 °C, or with strontium additions up to 150 ppm. Both chemical and thermal modifications of eutectic silicon were found to effectively improve alloy conductivity. However, the potential benefits of strontium to alloy strength and hardness were offset by the decreases in density associated with the addition. In contrast, the as-cast, unmodified samples featured relatively high densities, although this limited the ability of the HIP treatment to reduce porosity. With identical temperatures and heating times, both the HIP and heat treatment promoted comparable Al2Cu dissolution and silicon spheroidization. Accordingly, both treatments generally improved hardness and strength, and increased thermal and electrical conductivities by up to 12.5%. Thus, these processing parameters can each contribute to the development of enhanced engineering components individually, thereby reducing the need for their combined application.

Tensile and wear properties of as-cast and as-extruded ZK60 magnesium alloys containing minor Nd additions

The microstructure, mechanical and wear properties of as-cast and as-extruded Mg-6Zn-0.5Zr (wt%) alloys, also denoted as ZK60, containing minor Nd additions by 0.2 and 0.5 wt% were investigated. The alloys were produced by low pressure die casting (LPDC) method and extruded at 300 °C with an extrusion ratio of 16:1 and a ram speed of 0.3 mm s−1 following homogenization at 400 °C for 24 h. Microstructural characterizations revealed that minor Nd addition gave rise to a significant grain refining effect and a formation of divorced, globular and strip-like Mg-Zn-Nd ternary phases. After homogenization, most of the second phases were dissolved in α-Mg matrix and extrusion resulted in a considerable grain refinement. Basal texture intensity of the as-extruded ZK60 alloy dramatically decreased by 0.2 wt% Nd addition. The ultimate tensile strength and elongation-to-fracture of ZK60 alloy were improved by 20% and 40%, respectively, after 0.2 wt% Nd addition in the as-cast state while only elongation-to-fracture was considerably improved by 50% after 0.2 wt% Nd addition in as-extruded state due to the significant texture softening. Wear resistance of the as-cast ZK60 alloy was improved after extrusion and gradually enhanced with increasing Nd content. The abrasion, oxidation and adhesion mechanisms were observed in the alloys.

Characterization of heat transfer and its effect on solidification in water cooled LPDC of wheels

Computational process modelling has become an important engineering tool in the casting industry to predict the solidification sequence in complex castings. Used properly, this tool can help reduce manufacturing costs. One of the challenging issues in developing casting simulations of the low pressure die casting (LPDC) process for automotive wheels is to quantify the heat transfer coefficients (HTC) within the cooling channels in a die. When water is used as the cooling media, the HTCs exhibit a complex, non-linear behaviour due to the boiling phenomena that occur making it possible to extract a significant amount of heat from the die in a short period of time and influence the solidification of a wheel. Primarily, constant heat transfer coefficients have been used to describe this heat transfer in casting models up until now, but an opportunity exists to improve the transient description of heat transfer in channels cooled with water. In this paper, HTC’s in a lab-scale physical analogue model of die cooling will be characterized as a function of initial die temperatures.

Fatigue life estimation of cast aluminium alloys considering the effect of porosity on initiation and propagation phases

Porosity is one of the main parameters in the fatigue life of cast alloys as it affects both crack initiation and propagation phases. However, most fatigue assessments present a lack of accuracy as they only consider the effect of the initial pore on the fatigue limit. Therefore, in the present study, first, the effect of the main porosity characteristics on the crack initiation and propagation phases is evaluated. Then, an alternative fatigue assessment model is proposed and implemented for Low Pressure Die Casting (LPDC) A356-T6 aluminium alloy. The model accounts for the effect of the initial pore size and distance to surface on the initiation phase as well as fractographic porosity and average pore size on the fracture surface on the propagation phase. Finally, the model was validated by means of experimental tests. Results have shown that the proposed model is able to qualitatively predict the scatter trend of samples tested at the same load. In addition, the model presents high quantitative accuracy in life estimation with an average logarithmic error below 2% and an absolute average error below 16%.