Intelligent Temperature Control of Die Casting Tools
"The temperature control of die casting tools comes at the end" - this approach is still used to design temperature control channels in die casting molds. However, an efficient design and process-specific optimization of complex 3D temperature control systems in die casting tools is different.
An intelligent mold temperature control supports the quality and dimensional accuracy of the casting as well as the economy of the casting process. Intelligent temperature control means planning the heat balance of tools and designing and constructing all measures in a specific and targeted way. In order to achieve an optimal result for all relevant quantities such as casting quality, process stability, cycle time or tool life, the process heat must be dissipated from specific areas of the tool in a targeted and timed manner.
In the thermally relevant tool segments, the temperature control must be individual, efficiently controllable and possibly variable in time. The corresponding tool segments must therefore be thermally quick. This is possible thanks to the near-contour and contour-adjusted tempering channels, different heat-conducting materials in the die casting tool and powerful heating / cooling devices with the option of vario-thermal control.
How can this be done during tool design? For 30 years, possibilities of computer-aided, optimized design of casting processes and modern, partly generative manufacturing technologies for contoured and contour-adjusted mold segments have been available. The potential of virtual casting process assessments and optimization are recognized and quantifiable - ready to be leveraged. With the “front loading” approach, which has been known in mechanical engineering for 140 years, the virtually generated knowledge about the individual casting process can already be used in the construction and design of the casting tool.
The porosity can be eliminated by appropriate temperature control of the tool at the critical points. There are hundreds of options for dimensioning the temperature control: different switch-on and switch-off times, different flow temperatures and flow rates of the temperature control medium led e.g. in this example, to over 700 conceivable variants, of which a statistically relevant part was examined in a virtual DOE. This virtual DOE consists of casting process simulations of 50 variants, which represent the range of all meaningful parameters of temperature control.
The results of these calculations now allow the evaluation of the different variants with regard to different criteria such as cycle time, volume of porosity or service life of the tools in critical areas.
In this scatterplot, each point represents a virtual experiment. The solidification time decreases with increasing flow rate of the tempering medium: the cycle time is reduced. At the same time, the standard deviation increases: the process becomes less stable. / Source: MAGMAIn connection with the topic of foundry 4.0, i.e. the digitization of die casting processes, the question of the relevance of controllable process parameters must be asked in relation to measurable targets and objectives of the casting process. Based on the numerous virtual tests, the relevant parameters can now be filtered out so that the process control can be designed drastically more efficiently.
In the all day’s practice of developing a foundry process that is as problem-free as possible, the foundry technician usually has to live with the design of the tool with the respective tempering measurements. Usually there are a number of sample series in which some process parameters are checked. Typically, there are rarely more than 10 documented parameter variations. Ultimately, undocumented parameter variants are irrelevant in terms of sustainable, efficient casting processes and their planning. If all costs arising in a sample series are assigned in a commercially correct manner, five-digit EURO sums are quickly achieved.
In comparison, the costs for a virtual DOE like the one described here are negligible. With a maximum of two man-days for a project as described above, it is possible to test as many different parameter variants as would never be possible on the die casting machine in the foundry. At the same time, the relevant casting parameters are found, the digitization of the casting process is put on a reliable basis and the entire procedure is perfectly documented.
The costs of the virtual assessment and the virtual optimization of die casting processes are one to two orders of magnitude lower than those of the usual sample series. At the same time, the value of the findings from the virtual experiments is much higher.
It is reported that the return on investment when investing in software, training employees and adapting decision-making structures can already be achieved with two to three projects.
