Die Casting – A Typical Digital Industrial Case Share

Die casting, also known as a high-pressure casting, is a near-net forming technology widely used in recent years in the automotive, aerospace, and electronics industries. In the die-casting process, molten metal (usually a light alloy) is filled into the cavity at high pressure and speed under the action of a punch and cooled rapidly to form the final casting. Die casting is generally divided into cold chamber die casting and hot chamber die casting; cold chamber die casting is mainly used in the generation of large parts, such as automotive parts, communication base station cooling components, etc., while hot chamber dies casting is widely used in the production process of small electronic or 3C products, such as USB connectors, laptop shells, etc.

Typical cold chamber die-casting process

Die-casting has a good automation basis   

Compared with the normal casting process, die casting is characterized by high speed and high pressure, and the products produced are generally light alloy thin-walled parts, but die casting technology is also applied to the production of pure copper rotors, unlike aluminum and magnesium alloys, pure copper has a high melting point, so the short die life in the pure copper die casting process is a big problem.

Among all casting technologies, die casting has the highest degree of automation. Modern die-casting companies use automated die-casting island technology, which highly integrates die-casting machines (usually dozens or even hundreds of them) to form a fully automated production process. At the same time, the smart factory technology is used to monitor the production process of die-casting machines, to grasp the performance and status of each die-casting machine in real-time, and to make timely adjustments to the production process of die-casting machines through big data measurement and real-time feedback to ensure the final quality of products.

Typical die-casting automotive structural parts

Real-time control of die-casting mold temperature is a simple example.
 Take cold chamber die casting as an example; during the production process, due to the high-temperature liquid, metal constantly fills the cavity, making the mold temperature rise continuously. At this time, to ensure that the mold temperature does not overheat, cooling water is generally used to pass into the mold for cooling. Suppose the cooling water pipeline design is reasonable. In that case, we can generally control the cooling water temperature and flow rate to ensure that the mold temperature reaches the so-called thermal equilibrium. From this point of view, we can design a cooling water feedback system to control the temperature and flow rate of cooling water through calculation and an instant feedback system after knowing the actual value of mold temperature. Finally, we can control the temperature of the mold. This is a typical application of die-casting smart factory at this stage.       The above intelligent control case is only a small application scenario in the “smart factory” to achieve the real sense of the “smart factory” need to collect a large number of real-time production data, which plays a decisive role related to product quality data, such as density, porosity, and oxidation trap. For example, density, porosity and oxidation inclusions, etc., because these data are the indicators that customers are most concerned about and are also the core indicators to measure whether a casting is qualified. At this stage, these most critical indicators are the most difficult to obtain because, for metal alloy products, we can not directly observe the product’s internal structure. Most of the manufacturers use the way to sample castings, cut in the key areas clearly defined by the customer, and then directly observe whether there are problems; another method is to use two-dimensional X-ray inspection technology to scan and observe the local location of the sampled castings, the biggest problem of this method is that the three-dimensional casting information is compressed into two-dimensional slices. The information obtained from the observation does not completely reflect the actual situation.

The European MUSIC project in AUDI AG Ingolstadt plant smart factory implementation plan (click to enlarge)

Internal quality monitoring of castings     

With the continuous development of the automotive industry, the requirements for the quality of components are getting higher and higher, and large automotive manufacturers are constantly setting requirements for the internal quality of components, quantitatively specifying the criteria by which internal defects in components can exist. In this context, component suppliers must be able to detect and calibrate the distribution of defects within all castings in real time during production and compare the standards to assess whether the castings meet the requirements.        So how to properly observe and record the defects inside the casting? The best available technology is computed tomography, or CT, widely used in the medical field. Still, the application of CT technology to industrial inspection is recent. The application of CT technology to the inspection of the internal quality of castings must meet the following requirements: First, the speed of the inspection must be high enough to match the real-time production process of the castings; second, the quality of the images obtained from the inspection must be good enough to match the subsequent software for accurate identification of the images; third, the software or algorithm for identifying the images must be fast enough to ensure accuracy without delaying the production progress.       The first two requirements are for the CT inspection, while the last is for the software or algorithm. To the existing CT technology for a general overview, we will find that the most promising detection instrument is produced by General Electric’s fast inspection CT equipment (Speed-Scan), which the German Volkswagen has used for the actual casting inspection. But, a look at the domestic die-casting industry, the use of real-time CT technology to control the quality of castings from the operational level is a serious challenge; the biggest constraint is the cost – CT inspection equipment is extremely expensive and will be used for production lines generally need a large number of CT inspection equipment, which most domestic enterprises can not afford of. With the continuous development of the industry and the increasing quality requirements for castings, the use of CT technology for real-time inspection of the internal quality of castings will be a common requirement for suppliers of OEMs in the future.

The casting production line deployment of a high-speed CT system

Process feedback and adjustment 

 On the premise that CT and 3D inspect the solid casting data obtained, we assume that there is an algorithm that can analyze this data as a whole in a very efficient way and give all the information about the defects inside the casting, including type, size, and distribution, etc. Then we can use this information to make adjustments and corrections to the production process and obtain qualified castings without overrunning defects. This process – the process of obtaining casting information and correcting the process – is what we call the process feedback and adjustment process. Of course, we cannot complete this process based on the information of only one casting. The most normal situation should be to obtain information on many castings and solve the defect problem of castings by statistical analysis and process correlation.

The next question is, even if we obtain a large number of casting internal defect distribution information, how can we circumvent the non-conforming defects by adjusting the process parameters? Computer numerical simulation is the most powerful analytic tool available, also known as computer-aided engineering (CAE) technology.

Using computer simulation technology, we can achieve virtual production in a local sense, especially for die-casting; we can directly simulate the filling and solidification process numerically by studying the speed, pressure, flow pattern, spattering, and other behaviors of the fluid-filling process, to determine whether the filling process exists to roll gas; by calculating the temperature changes of castings and molds under different cycle conditions of die-casting, to determine and study potential thermal joints of the mold, casting defects (shrinkage, shrinkage) and die-casting heat balance behavior. Through this numerical simulation technique, based on certain analysis conditions, we can, to a large extent, judge and circumvent the defects inside the casting, improve the casting performance and significantly increase the production efficiency, achieving the purpose of process feedback and correction as we discussed before.

Clutch shell filling process simulation

We string the whole process together: digital technology (CT) is used to detect the 3D defect data of the product in real time, and if the product fails, the data is transferred to the CAE analysis center, which uses simulation technology to analyze and generate solutions to the defect problem, and the solutions are fed back to the production and process front-end for implementation and reacquisition of the product, which continues to be digitally detected and obtains 3D defect data, and if the product is qualified, the iteration ends, and vice versa, it continues.

Master the core digital technology

 As you can see, the key role in this process is CAE analysis; the proposed solution’s effectiveness will impact the efficiency of the entire process. Whether it can master the core of CAE technology and whether it can be well applied to the actual production of numerical simulation technology can largely measure the technical ability of a die-casting enterprise because digital technology is the necessary road for the enterprise, the earlier on this road to master the core of digital technology, the more it can stand out in the future competition of enterprises.

Therefore, if digital inspection technology and CAE analysis technology are well applied in the existing die-casting enterprise, we can see a complete and typical digital factory scene. Digital inspection technology digitizes the physical entity. In contrast, CAE analysis technology transforms the digital information obtained from inspection into problem solutions based on virtual production, in which digital inspection is a fully automated process. In contrast, CAE analysis still requires human participation. If CAE analysis can be solidified into an algorithm and fully automated, this is the prototype of the future intelligent digital factory.

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