Low Pressure Die Casting Process Research Articles

Reduction of rejects by combining data from the casting process and automatic X-ray inspection

Automatic inspection of castings with X-rays (radiographic and computed tomography) is widespread for parts that are relevant for safety or have high quality requirements. Examples in the automotive sector are aluminum wheels, chassis parts and new parts within the electric power train. Those parts are automatically inspected, which means that both the image acquisition and the evaluation of the images is done fully automatically. Today, in most industrial implementations, the generated data with a size up to several gigabytes per part is summarized to a simple good or bad decision, according to specification. All other data is dismissed, although this information can be valuable to optimize production processes and thus minimize rejects. This contribution gives an overview about the results of the project Cast Control, which is a collaboration of Fraunhofer Development Center for X-ray Technology EZRT, Fraunhofer Center for Applied Research on Supply Chain Services SCS and industry partner RONAL GROUP. RONAL GROUP is a major aluminum wheel manufacturer, mainly for the OEM market. Within the project we combined serial production data from the low pressure die casting process from a foundry of the RONAL GROUP with the data generated in the automatic X- ray inspection. After collecting a large base of sample data, we were able train a neural network for the prediction of error metrics obtained by X-ray inspection. We apply a combination of layer-wise relevance propagation and dimensionality reduction to find correlations between data of the casting machines (process and sensor) and the characteristics of anomalies detected during X-ray inspection. With this information, it is possible to adjust the casting process in an early stage – even before rejects are produced. This enables the foundry to reduce their rejects rate, which saves costs and energy and results in a better competitiveness.

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.

A multimodal data-driven design of low pressure die casting gating system for aluminum alloy cabin

The design of gating systems for large and complex thin-wall castings requires the application of empirical equations and a substantial number of iterative experiments. In this study, a novel machine learning-based design strategy is proposed for the gating system of large and complex thin-walled castings. The gating system of castings was estimated using a multi-input multi-output neural network model, which incorporated visual, textual, and numerical multimodal features. The R2 value of the model accuracy reached 0.981, and the predicted parameters for the gating system were a slot runner width of 27 mm, a slot runner length of 48 mm, a riser runner diameter of 60 mm, and the number of riser runners of 8. The EasyCast and Procast simulation software were utilized for comparison and verification, resulting in a smooth filling and the achievement of sequential solidification. Finally, the mechanism of casting defects is discussed using the solidification phase-field model of EasyPhase system, elucidating how the alloy system can serve as a distinguishing feature in machine learning models that impact gating system parameters. This strategy is anticipated to enhance the efficiency of low pressure die casting process design, thereby serving as a valuable reference for intelligent process design in low pressure die casting.

Reduction of rejects by combining data from the casting process and automatic X-ray inspection

Automatic inspection of castings with X-rays (radiographic and computed tomography) is widespread for parts that are relevant for safety or have high quality requirements. Examples in the automotive sector are aluminum wheels, chassis parts and new parts within the electric power train. Those parts are automatically inspected, which means that both the image acquisition and the evaluation of the images is done fully automatically. Today, in most industrial implementations, the generated data with a size up to several gigabytes per part is summarized to a simple good or bad decision, according to specification. All other data is dismissed, although this information can be valuable to optimize production processes and thus minimize rejects. This contribution gives an overview about the results of the project Cast Control, which is a collaboration of Fraunhofer Development Center for X-ray Technology EZRT, Fraunhofer Center for Applied Research on Supply Chain Services SCS and industry partner RONAL GROUP. RONAL GROUP is a major aluminum wheel manufacturer, mainly for the OEM market. Within the project we combined serial production data from the low pressure die casting process from a foundry of the RONAL GROUP with the data generated in the automatic X-ray inspection. After collecting a large base of sample data, we were able train a neural network for the prediction of error metrics obtained by X-ray inspection. We apply a combination of layer-wise relevance propagation and dimensionality reduction to find correlations between data of the casting machines (process and sensor) and the characteristics of anomalies detected during X-ray inspection. With this information, it is possible to adjust the casting process in an early stage – even before rejects are produced. This enables the foundry to reduce their rejects rate, which saves costs and energy and results in a better competitivenessin a better competitiveness.

Process optimization of A356 aluminum alloy wheel hub fabricated by low-pressure die casting with simulation and experimental coupling methods

The aluminum alloy wheel hub is mainly produced by low-pressure die casting process (LPDC), in which casting quality is closely related to the process parameters. In this paper, the LPDC process of the A356 alloy wheel hub was numerically simulated by ProCAST software, and the forming quality of the wheel hub under different cooling processes has been studied. The result indicated that the liquid aluminum exhibited different solidification characteristics under three cooling processes of 300 L/h for 150 s, 400 L/h for 140 s and 500 L/h for 130 s. A sequential solidification state was obtained under the condition of 400 L/h for 140 s. By comparing the tensile properties of the rim under different cooling processes, it was found that the wheel hub casted under 400 L/h for 140 s had higher tensile strength and elongation, which were 178.9 MPa and 6.6%, respectively. The reduction of casting defects, microstructure refinement, and uniform distribution of eutectic Si particles in the wheel hub are the main reasons for the improvement of mechanical properties.

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.

Engineering Challenges in Alloy Wheel Rim for Safety Simulations

<div class="section abstract"><div class="htmlview paragraph">Aluminum alloy wheels are being widely used in the automotive industry since the last decade due to its superior styling and performance. Alloy wheel rim is one of the critical components and plays an important role in a frontal crash scenario. The wheel rim failure prediction in safety simulation is essential to ensure robust safety performance. Determining failure characteristics of an alloy wheel poses many difficulties considering its brittle nature, porosity and inhomogeneity in material properties across different regions of wheel rim due to mold design, cooling rate and other process parameters of the low-pressure die casting process.</div><div class="htmlview paragraph">This paper describes the modelling and simulation methodology developed to predict accurate wheel behavior. The methodology addresses two distinct areas of challenges such as alloy wheel rim failure prediction and associated tire blow out.</div><div class="htmlview paragraph">Alloy wheel rim material is characterized considering the inhomogeneity of material properties at different regions such as the rim, spokes and hub. The material model is developed, considering strain rate sensitivity, anisotropy due to the state of loading, ductile normal and shear fracture aspects. Scatter function is developed to simulate material property variations.</div><div class="htmlview paragraph">Radial tire characterization is carried out with detailed modelling of constituents such as rubber, steel beads, body plies and steel belts. Various physical tests and digital correlation studies are carried out at coupon and tire assembly level to represent the stiffness and deformation of the tire accurately. A novel approach was developed using sensors in digital models to detect the alloy wheel rim failure in real-time. This triggers rapid air leakage in the tire to simulate the blowout scenario during the crash event. The methodology has been validated at the subsystem and full vehicle level by carrying out multiple physical tests. This approach can be used for digital development and validation of alloy wheel rim design to ensure robust crash safety performance.</div></div>

Formation and evolution of the interfacial structure in al/steel compound castings during solidification and heat treatment

In this work, Al7SiMg/steel compound castings were produced through a low-pressure die casting process. All steel inserts were galvanized, where half of them were flux-coated to further improve the wettability and remove interfacial oxide layers during casting. The reaction layer formed in the Al7SiMg/steel interface was examined using Optical Microscopy (OM), Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS). In addition, Vickers Micro-hardness was measured across the interface. Results show that successful metallurgical bonding can be achieved between aluminum and galvanized steel, both with and without additional flux coating. A large fraction of intermetallic particles formed at the reaction layer, where ternary Al4·5FeSi particles were the dominating phase. The influence of T6 heat treatment (solution treatment at 540 °C, followed by artificial ageing) on the interfacial microstructure was also studied. After heat-treatment, the thickness of the interfacial layer increased significantly, due to the growth of β-Al4.5FeSi and Al–Fe binary particles into the bulk of steel. Consequently, cracks formed and propagated through the inner binary intermetallic layer. Formation mechanisms of various intermetallic phases at the interface during solidification and heat treatment have been discussed.

Interfacial Microstructure Formation in Al7SiMg/Cu Compound Castings

Compound casting is an attractive approach to create multi-material components and thus reduce the overall weight, while maintaining both the functional and mechanical properties. In this work, Al7SiMg alloy/copper compound castings were produced by a low-pressure die casting process. A flux coating was applied on copper pipes to reduce the oxide layer present in the interface between Al and Cu. The interface layer formed between the two alloys was investigated using optical microscopy, scanning electron microscopy and energy-dispersive X-ray spectroscopy. Vickers micro-hardness was also measured across the interface. Results showed that a continuous metallurgical bond formed between copper and aluminum without use of surface treatment. In the bond layer, various Al–Cu intermetallic phases were detected, as well as primary silicon particles and the quaternary phase Al5Cu2Mg8Si6. Flux coating prevented formation of any metallic bond between copper and aluminum. Instead, high concentrations of potassium, magnesium and fluorine, indicative of formation of KMgF3 and MgF2, were detected in the interface. The mechanism for the formation of the intermetallic phases and the strength of the interface layer have been discussed.

Microstructure and Properties of A356 Alloy Wheels Fabricated by Low-Pressure Die Casting with Local Squeeze

The low-pressure die-casting process (LPDC) is widely used in fabricating aluminum alloy automotive wheels. With good castability and mechanical properties, A356 alloys are important and common materials for LPDC wheels. However, in practical casting process, defects often appear, especially porosities in the wheel thick-walled parts (i.e., spoke) due to inadequate interdendritic feeding, which greatly decreases the mechanical properties. In this investigation, to deal with this problem, local squeeze (LS) was added in conventional LPDC to enhance the interdendritic feeding. The practical A356 alloy wheel castings were fabricated with different LS velocities and times. The quantitative characteristics of α-Al cells, Si particles, and porosities with and without the LS analyzed by optical microscope, scanning electron microscope, and 3D x-ray computed tomography were compared. Their effects on the tensile and impact properties were discussed. The results have shown that the LS can increase the cooling rate and feed shrinkage of spoke parts, hence resulting in finer grains and denser microstructure. In result, the properties of spoke parts were improved with the LS.

Compute-Aided Design of Low Pressure Die-Casting Process of A356 Aluminum Wheels

The lightweight design of aluminum automobile wheel can easily bring about all kinds of defects during die-casting, which often causes wheel frame deformation and creep damages in the future use and in turn affects traffic safety. To understand the evolution of mold flow, temperature field, and solidification, the low pressure die-casting processes of A356 aluminum wheel were simulated by Anycasting software package. Various casting parameters combined with the designs of flow channel and overflows were adopted to reduce the defects occurred in wheel products. In addition, we adopted the retained melt modulus (RMM) to predict the position of defects to be formed as well as their distribution so as to eliminate the shrinkage voids and porosity defects during die-casting. The research findings showed that the setting up of overflow tank could effectively prevent the formation of shrinkage void and porosity of die-castings and significantly promote the quality and productivity of die-casting wheel products.

Effect of strain rate on the defect susceptibility of tensile properties to porosity variation

The contribution of the strain rate to the dependence of tensile properties on the microporosity variation in A356 casting alloy was investigated in terms of the defect susceptibility of tensile properties to the microporosity variation with variations of the strain rate as well as the relative contribution of microporosity and strain rate to overall tensile properties. The test samples were prepared using a low-pressure die-casting process and subsequent T4 treatment (12-h at 540°C), and the tensile test was carried out at room temperature for strain rates varying in the 1.4×10−4∼1.4×10−1s−1 range. The overall dependence of tensile properties on the strain rate is not described precisely owing to remarkable deviations in the data that mainly arose from the variation of the fractographic porosity; these deviations can be clearly attributed to the variability in the defect susceptibility coefficient of the tensile properties to the microporosity variation. The defect susceptibility coefficient of ultimate tensile strength (UTS) to microporosity variation increases with the strain rate, whereas the defect susceptibility coefficient of elongation decreases. Although the UTS for the sample with a low microporosity level increases with increasing strain rates, the UTS above a certain porosity level is affected adversely and decreases with the increasing strain rates. However, the nominal level of tensile elongation on the variation of the strain rate clearly decreases with the increase of the microporosity. The fractographic porosity practically decreases with the increase of the strain rate, and the overall fracture path between the micro-voids depends practically upon a certain transition of the fracture mode of Si particles accompanying the variation of the strain rate. Additionally, the damage evolution of eutectic Si particles is transited from a mixed mode of cracking and debonding failure to a failure mode which the debonding failure dominates as the strain rate increases.

Wastage Reduction through Lean Manufacturing in Head Lamp Casing Manufacturing Process

In Case Industry motorcycles models Head lamp casing is made up of Aluminium and it is manufactured by gravity pressure die casting (GDC) process. The rejections rate and cycle time are very high. These results in frequent line stoppage due to the shortage of Head lamp casing. Hence we have decided to proceed with some other process for the manufacturing of head lamp casing, since the existing GDC process has some limitations it cannot be improved further. To move with other process, we have studied sheet metal, in which our head lamp casing design is so complicated and process would be more tedious. Moreover aesthetics look of the head lamp casing will be affected. So we progressed with other die casting process. We have studied pressure die casting (PDC) process first, but in PDC, head lamp casing cannot be manufactured two halves, due to the size of our head lamp casing. Hence we proceeded with low pressure die casting (LPDC) process, in which sand cores are made and thru which Head lamp casing are manufactured. To move with LPDC process, the present supplier base is not feasible. Hence we decided to move with an alternate source for LPDC process. Three LPDC manufacturing suppliers were shortlisted. Among the three suppliers, based on the quality, cost and delivery time, one supplier is approved. Approved supplier is permitted for die making and manufacturing head lamp casing after the product and process audit. Once die manufacturing is completed, samples were taken and tried out at our end. The new LPDC head lamp casing is found ok and hence rejections at supplier end had minimised and rework at our end had eliminated. These results in eliminating the dispatch constraints and thus our motto of avoiding line stoppage due to the shortage of head lamp casing are eliminated.

Full through Process Simulation for Low Pressure Die Casting – From Casting to Design

To achieve a required product quality during Low Pressure Die Casting (LPDC) process, it is necessary to identify and also control the main input parameters affecting the casting defects to arrive at the desired output quality. In industrial scale LPDC, where the issues of part quality, cost, and cast speed are the main driving forces for the industries, the cast process optimization to have minimum defects is quite essential. The LPDC process of light weight metals is defined as a casting process where the die is filled relatively slowly at low pressure. The melt flow regime has low turbulence, and filling process can be defined as relatively smooth filling. In recent years, in order to limit the component defects, the through process simulation has widely been used. In an interactive simulation environment, a full multi-phase casting process simulation (thermal-fluid simulation using multi-physical domain) along with its material and mechanical simulations are carried out in a single environment. One of the main contributions of this paper is to show the advantages of using full through process simulation of LPDC to limit the defect in the component. An experimental program along with a comprehensive process simulation has been setup to optimize the casting process and the results were presented for real world case studies.

Optimization of low-pressure die casting process parameters for reduction of shrinkage porosity in ZL205A alloy casting using Taguchi method

In order to reduce serious tendency of the shrinkage porosity in ZL205A alloy, the design of experiment technique, Taguchi method is introduced to optimize the process parameters of cylindrical shell ZL205A alloy parts produced by low-pressure die casting in this article. The effect of pouring temperature, filling time and packing pressure on the shrinkage porosity in ZL205A castings is investigated using the orthogonal array and Advanced Porosity Model in ProCAST to calculate the volume of shrinkage porosity. The effectiveness of selected casting parameters in reducing the shrinkage porosity in cylindrical shell ZL205A casting is verified through analysis of signal-to-noise ratio and analysis of variance. Numerical results indicate that the optimized process parameters, including a pouring temperature of 725 °C, filling time of 20 s and packing pressure of 175 kPa, can be used to reduce the formation of shrinkage porosity in ZL205A castings.

Effects of Combination of 0.02 wt % Sr and Ti on the Characteristics of AC4B Alloys Produced by Low Pressure Die Casting

Aluminium AC4B alloy is widely used in automotive industries for various components. However, when this alloy is used to produce motorcycle cylinder head by Low Pressure Die Casting (LPDC) process, high reject rate was often found due to shrinkage, porosity and misrun. Addition of grain refiner and modifier is an alternative for this problem, through the control of solidification process that results in grain refining and microstructure modification. This study was aimed to investigate the effect of combination of 0.02 wt. % Sr modifier with variation of Ti grain refiner on the characteristics of AC4B alloy and the reject rate of cylinder head components. The Ti grain refiner was varied 0.063, 0.083 and 0.108 wt. % Ti and added at the holding furnace prior to LPDC process. A series of test was conducted including hardness test from the thin and thick regions of the part, tensile test, fluidity test, vacuum test as well as observation of microstructure by using optical microscope and Scanning Electron Microscopy (SEM) equipped with Energy Dispersive X-Ray Analysis (EDAX). The results showed that the higher the Ti content, the higher the hardness for both thin and thick areas, the lower the fluidity of the molten metal. At the maximum level of Ti of 0.108 wt. % and 0.02 wt. % Sr, the reject rate was significantly reduced from 3.59 % to 1.38 %.

Thermal Stress and Heat Transfer Coefficient for Ceramics Stalk Having Protuberance Dipping into Molten Metal

Low pressure die casting is defined as a net shape casting technology in which the molten metal is injected at high speeds and pressure into a metallic die. The low pressure die casting process plays an increasingly important role in the foundry industry as a low-cost and high-efficiency precision forming technique. In the low pressure die casting process is that the permanent die and filling systems are placed over the furnace containing the molten alloy. The filling of the cavity is obtained by forcing the molten metal, by means of a pressurized gas, to rise into a ceramic tube having protuberance, which connects the die to the furnace. The ceramics tube, called stalk, has high temperature resistance and high corrosion resistance. However, attention should be paid to the thermal stress when the stalk having protuberance is dipped into the molten aluminum. It is important to reduce the risk of fracture that may happen due to the thermal stresses. In this paper, thermo-fluid analysis is performed to calculate surface heat transfer coefficient. The finite element method is applied to calculate the thermal stresses when the stalk having protuberance is dipped into the crucible with varying dipping speeds. It is found that the stalk with or without protuberance should be dipped into the crucible slowly to reduce the thermal stress.

Numerical Simulation of LPDC Process for Thin-Walled Aluminum Alloy

In general, the thin-walled casting with the thickness less than 2mm is not easy to be prepared using low-pressure die casting (LPDC) technology because of its poor filling-ability. In this paper, the LPDC process of A356 thin-walled aluminum alloy was simulated based on the selection of appropriate boundary conditions in order to save the cost. The simulation results compared with experimental results indicate the numerical simulation technology is an effective tool for reducing the manufacturing cost and improving the casting quality.

LPDC Process Optimization of Copper-Alloy Taps Based on CAE Technique

Low pressure die casting (LPDC) process of a copper-alloy tap was simulated by the finite element software ProCAST. The casting defect and its reason were analyzed. The optimized design was discussed and the simulation was validated against the low pressure die casting experiment. Results showed that the main defect is misrun and its reason is that the melt solidifies too fast. It’s well solved by increasing the wall thickness of the relational pipe properly. The result agrees with the experiment.

Determination of optimal location to monitor temperature in low pressure die casting process

Controlling and eliminating defects, such as macroporosity, in castings is a continuing challenge that manufacturers must continually address. Since the encapsulation of liquid regions by a solid shell and subsequent formation of macroporosity cannot be detected during casting, the die temperature, which is routinely measured, has been used as an indirect indicator of this defect. A finite element model has been developed to predict the evolution of temperature as well as the volume of encapsulated liquid in a casting with a high propensity to form macroporosity. The boundary conditions in the model were iteratively adjusted until the temperature predictions matched the experimental data for a variety of operational conditions. A model based methodology has been developed to analyse the correlation between the die temperature and the encapsulated liquid volume. This methodology is employed to assess the suitability of different in-cycle die temperatures for use as indicators of macroporosity formation, and to help determine the optimal location to monitor temperature for the purpose of minimising macroporosity.