DESC:FIELD OF INVENTION
The present invention relates to the field of simulation methods for die casting.
BACKGROUND OF THE INVENTION
Manufacturing of a die for a die casting method is a complicated process. Manufacturing of a die involves huge cost and efforts. To minimize errors in die manufacturing, typically, a designed die is simulated before actually manufacturing the same. The use of simulation programs saves time and reduces the costs of the casting system design. A properly designed casting, prepared mold and melted metal should result in a defect free casting. However, even in the most properly controlled processes foundry-sometimes expensive casting defect can present themselves. Defects can be classified as filling-related, shape-related, or thermal-related. Most common defect modes for die casted parts are shrinkage, porosities, misruns, gas inclusions. Casting simulation saves time and resources but does not guarantee a zero-defect process or product. Reoccurrence of defects, despite best simulations, increases economic burden on the production unit costing use of additional resources and time; not to mention inducing delays in production process. There are some simulation solutions currently available that address certain aspects of defects in real-time. However, these software programs can address only one type of defect at a given point of time. There is no solution available that addresses every single aspect of the casting process real-time.
Aluminum die castings are gaining importance in the production of light weight vehicle bodies, as for example used in new model Audi cars. Therefore, it is even more vital today that these castings can be produced with the high quality methods. In this context the simulation is becoming more essential in the designing process.
However, conventional simulation process has certain drawbacks. One of the drawbacks is conventional simulation process does not take into account real conditions within a die. For example, a conventional simulation process does not consider real time temperatures within a die while simulating a die. Due to this, a simulated die model is not able to correctly represent a real die. This result in various defects occurring in a real die manufactured based upon a simulated die model.
Therefore, there is felt a need of a simulation process that alleviates aforementioned drawbacks of conventional die casting simulation process.
OBJECTS OF THE INVENTION
Following are some objects of the invention which at least one embodiment of the present invention satisfies.
An object of the present disclosure is to provide an effective die casting simulation method.
Another object of the present disclosure is to provide a die casting simulation method that is capable of accurately addressing various defects that may occur in a casting obtained by die casting.
Yet another object of the present disclosure is to provide a simulation method that is precise.
SUMMARY OF THE INVENTION
The present disclosure discloses a die casting simulation method. The method includes steps of sensing one or more parameters of a physical die using one or more sensors, generating a virtual die model using a processor, and simulating a die casting process using the processor on the virtual die model based on sensed parameters of the physical die.
In some embodiments, one or more parameters are selected from the group consisting of a die temperature, metal velocity, water quality, injection pressure, ejection pressure, escape velocity, inbuilt vacuum pressure, humidity, and die alignment pattern.
In some embodiments, the one or more sensors are selected from the group consisting of a temperature sensor, a pressure sensor, a metal velocity measurement sensor, a water flow measurement sensor, a water pressure measurement sensor, a water pH measurement sensor, a water temperature measurement sensor, a water TDS measurement sensor, and a thermal vision sensor.
In some embodiments, the method includes a step of executing rules based on the sensed parameters to simulate the die casting process.
In some embodiments, the method includes a step of simulating the die casting process using a Machine Learning (ML) and/or Artificial Intelligence (AI) techniques.
In some embodiments, the method includes a step of generating alerts based on an outcome of simulation of the die casting process.
In some embodiments, the method includes a step of generating a predictive analytics report based on the sensed parameters and an outcome of simulation of the die casting process.
In some embodiments, the method includes a step of comparing sensed parameter value with corresponding parameter master value.
BRIEF DESCRIPTION OF THE DRAWINGS
The following figures are illustrative of particular examples for enabling embodiments of the present disclosure, are descriptive of some of the embodiments and are not intended to limit the scope of the disclosure. The figures are not to scale (unless so stated) and are intended for use in conjunction with the explanations in the following detailed description. Wherever applicable, the words and phrases used herein should be understood and interpreted to have a meaning consistent with the understanding of those words and phrases by those skilled in the relevant art. Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may have not been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure. Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures.
Figure 1 is a flowchart depicting non-limiting steps of a die casting simulation method in accordance with some embodiments of the present disclosure.
Figure 2 is a block diagram depicting a computing unit utilized for executing method steps of the present disclosure.
Figures 3-5 depict position of sensors in a physical die in accordance with some embodiments of the present disclosure.
Figure 6 depicts a block diagram elaboration flow of method steps, in accordance with some embodiments of the present disclosure.
LIST OF REFERENCE NUMERALS USED IN ACCOMPANYING DRAWING
100 – Method
110, 120, 130 – Method steps
200 – Computing unit
210, 210a-f – Sensor
220 – Processor
230 – Memory
240 – Display unit
250, 260, 270 – Die
280 – Simulation
290 – Physical die
DETAILED DESCRIPTION OF THE INVENTION
The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the disclosure. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary.
The terms and words used in the following description are not limited to the bibliographical meanings, but, are merely used to enable a clear and consistent understanding of the disclosure. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present disclosure are provided for illustration purpose only and not for the purpose of limiting the disclosure as defined by the appended claims and their equivalents.
Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
To clarify the objects, features, and advantages of this invention, specific embodiment of this invention is especially listed and described in detail with the attached figures as follows. The principal and mode of operation of this invention have been described and illustrated in its embodiment. At the outset, a person skilled in the art will appreciate that this invention may be practiced otherwise than is specifically described and illustrated. The invention should not be limited by the above-described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the invention. In the following description of the invention, certain terminology will be used for the purpose of reference only, and is not intended to be limiting.
The present invention discloses a die casting method which is now described in detail with reference to accompanying figures.
Referring to figure 1, steps of a method 100 are shown. The method 100 includes a step of sensing one or more parameters of a physical die using one or more sensors (Step 110). The method 100 may be implemented using a computing unit 200 shown in figure 2. The computing unit 200 may be in communication with one or more sensors 210 to sense various parameters of the physical die. In some embodiments, the computing unit 200 is in wired communication with the sensors 210. In some other embodiments, the computing unit 200 is in wireless communication with the sensors 210, for example, through a Wi-Fi or Bluetooth technology.
The computing unit 200 includes a processor 220 and a memory 230. Various parameters related to the method of the present disclosure are stored in the memory 230. Further, memory 230 stores instructions or rules that can be executed by the processor 220. The computing unit 200 can be, but not limited to, a desktop computer, a laptop, a tablet, a server, etc. In some embodiments, memory 230 can be provided in a cloud server or cloud storage. A display unit 240 may be in communication with the computing unit 200 via a wired or wireless network. Instructions provided or pre-stored values in the memory 230 can be altered by providing input to the computing unit 200. The display unit 240 may include an input device such as a touch screen to receive commands from user to alter instructions or pre-stored values in the memory 230.
The computing unit 200 receives sensed parameters from the sensors 210 and stores received parameters in the memory 230. The parameters are selected on the basis of their influence on a die performance. In some embodiments, the parameters are selected from, but not limited to, the group consisting of die productivity, die temperature, metal temperature, metal pressure, metal velocity, metal velocity, air escape velocity, water quality, injection pressure, ejection pressure, ejection force, escape velocity, inbuilt vacuum pressure, humidity, die alignment pattern, stress on guide pillar of die, remaining use life. A person skilled in the art shall appreciate that these monitoring of these real-time parameters helps in predicting the possible quality of the product prior to the finishing of the product. Sensors 210 are selected based on nature of parameter to be sensed. In some embodiments, the sensors 210 are selected from, but not limited to, the group consisting of a temperature sensor, a pressure sensor, a metal velocity measurement sensor, a water flow measurement sensor, a water pressure measurement sensor, a water pH measurement sensor, a water temperature measurement sensor, a water TDS measurement sensor, and a thermal vision sensor.
The sensors 210 are located at appropriate locations in a physical die. Referring to figures 3-5, various positions of sensors in a physical die are shown. Referring to figure 3, position of temperature sensors 210a, 210b and velocity and pressure sensors 210c, 210d in a physical die 250 are shown. In figure 4, position of a temperature sensor 210e in a die 260 is shown. In figure 5, position of a temperature sensor 210f in a die 270 is shown. It is to be noted that positions of sensors shown in figures 3-5 are for explanation purpose only, and they in no way limit scope and ambit of the present disclosure.
Once the sensors 210 sense a particular parameter, data related to sensed parameter is transmitted to the computing unit 200. In some embodiments, the data is accumulated at one place. Further analogue data is converted into digital data compatible for the computing unit 200. The digital data may be communicated to the computing unit 200 via a wireless network such as W-Fi technology. In some embodiments, the data is stored on cloud storage and the computing unit 200 receives data via cloud storage.
The method 100 includes a step of generating a virtual die model (Step 120). The virtual model may be created using the computing unit 200, more specifically, the processor 220. Instructions required to create a virtual model are stored in the memory 230. In some embodiments, instructions in the form of software can be stored in the memory 230.
The method 100 further includes a step of simulating a die casting process using the processor 220 on the virtual die model based on sensed parameters of the physical die. Rules required for simulating the die casting process are stored in the memory 230. Typically, the processor 220 executes the rules. For simulation, the processor 220 utilizes values of sensed parameters stored in the memory 230. In some embodiments, the method 100 includes a step of simulating the die casting process using a Machine Learning (ML) and/or Artificial Intelligence (AI) techniques. Due to use of ML or AI technique, simulation can incorporate variations in the values of sensed parameters.
As the sensed values are real-time values, the simulation results in near-actual scenario of a die casting process which helps is identifying problems that may occur in a physical die made according to virtual die model.
In some embodiments, the method 100 includes following steps:
a) generating die design as per industry standards and practices;
b) generating die design as per flow simulation;
c) generating die design based on PLM data;
d) generating customized die design based on product needs;
e) generating number of pre-decided cavities in single die;
f) suitable high pressure die casting HPDC machine is selected as per locking force;
g) production of unfinished products;
h) scheduled die maintenance and breakdown recurrences;
i) discarding of die post completion.
During the simulation, various defects in die casting can be identified. In some embodiments, the method of present disclosure helps to identify at least one of the occurring defects of shrinkage porosity, non-filling, blow hole, shrinkage cracking, leakage, gate erosion, soldering, ejector pin dispersion, crack, peel off, bend, blister, flash, micro-porosity, leakage, and casting stuck up.
Referring to figure 6, a typical process flow of method steps is shown according to an embodiment of the present disclosure. Initially, a simulation 280 is run. The simulation 280 utilizes real time parameter values of a die casting process corresponding to a physical die 290. Based on the results of simulation 280, actual die is manufacture. Tool optimization may be done prior to manufacturing the die based on simulation results. Further, trial on smart die is carried out. Data from the trial of the smart die is sent for simulation 280. Based on trial observations and tool optimization, changes in the physical die 290 are carried out. The trial of the smart die can be done again after optimization of the physical die 290. The corrections and optimization is carried out to ensure defect free casting. Further, once the die casting process becomes defect free, values of parameters of the die casting process are set as final values and casting process is initiated in the physical die.
Based on output of simulation, a use may alter parameters of die casting process to visualize effect of change in parameters. Further, the method 100 includes generating alerts based on an outcome of simulation of the die casting process. Typically, alerts are generated when value of one of the parameters of die casting process is not within its limits. Such limits can be stored in the memory 230. Typically, master or threshold value of each parameter is stored in the memory 230. Parameter value of the simulated die casting process is compared with its master value or range using the processor 220. If the parameter value is not within range or not equal to master value, an alert can be generated via the processor 220. The alert can be a visual alert and/or a sound alert. The processor 220 may display visual alert on the display unit 240. A message elaborating the alert situation can also be displayed on the display unit 240. A message corresponding to each alert is stored in the memory 230. The message is fetched by the processor 220 from the memory 230 corresponding to the alert.
The method 100 further includes a step of generating a predictive analytics report based on the sensed parameters and an outcome of simulation of the die casting process. A predictive analytics algorithm can be stored in the memory 230 which can be executed by the processor 220. The report can include various details of defects occurring in die model based on simulations. The report basically predicts future defects that may occur in a die casting process if the die is manufactured as per the virtual die model. In an alternate embodiment of the invention, the algorithm measures remaining use life, wear and tear. In yet another alternate embodiment of the invention, the algorithm provides die maintenance alerts to the production and engineering team.
,CLAIMS:We Claim
1. A die casting simulation method comprising:
sensing one or more parameters of a physical die using one or more sensors;
generating a virtual die model using a processor; and
simulating a die casting process using the processor on the virtual die model based on sensed parameters of the physical die.
2. The method of claim 1, wherein the one or more parameters are selected from the group consisting of a die temperature, metal velocity, water quality, injection pressure, ejection pressure, escape velocity, inbuilt vacuum pressure, humidity, and die alignment pattern.
3. The method of claim 1, wherein the one or more sensors are selected from the group consisting of a temperature sensor, a pressure sensor, a metal velocity measurement sensor, a water flow measurement sensor, a water pressure measurement sensor, a water pH measurement sensor, a water temperature measurement sensor, a water TDS measurement sensor, and a thermal vision sensor.
4. The method of claim 1, wherein the method includes a step of executing rules based on the sensed parameters to simulate the die casting process.
5. The method of claim 1, wherein the method includes a step of simulating the die casting process using a Machine Learning (ML) and/or Artificial Intelligence (AI) techniques.
6. The method of claim 1, wherein the method includes a step of generating alerts based on an outcome of simulation of the die casting process.
7. The method of claim 1, wherein the method includes a step of generating a predictive analytics report based on the sensed parameters and an outcome of simulation of the die casting process.
8. The method of claim 1, wherein the method includes a step of comparing sensed parameter value with corresponding parameter master value.