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 Ashley Stone, P. Eng.

Ashley Stone, P. Eng.

Owner and CEO, Inventor, MAXImolding!™ - a division of Jacobsen Real-Time X-Ray Machinery Inc.

Magnesium Semisolid Casting Part 5 - Fully Automated Self-Learning Digital Casting Factory

| Author / Editor: Ashley Stone / Nicole Kareta

After 40 years of experience and applied research, the die casting industry is ready for a fully automated digital 21st century casting factory, using fully automated data feedback from the x-ray machine. The unified and patented process control, together with semisolid casting machine and fully automated real-time inline x-ray inspection machine results in high integrity parts with minimal casting defects

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All casting machines and x-ray inspection machines, as well as proprietary linear robotics and machine vision systems integrated into this semisolid die casting process, will ensure the fast production of good parts, and maintain stable automatic closed loop process control.
All casting machines and x-ray inspection machines, as well as proprietary linear robotics and machine vision systems integrated into this semisolid die casting process, will ensure the fast production of good parts, and maintain stable automatic closed loop process control.
(Source: gemeinfrei / Pixabay )

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Read in "Part 4 - New Casting Machine Replaces Complete Casting Line" about the advantages of the new semisolid casting machine.

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By 2019 the magnesium die casting industry had made great progress in the re-evolution of a new semisolid magnesium casting process and machine. That machine was the missing link in proper merging casting and x-ray machine together. This new process, as well as the materials - available today in the form of the semisolid metal (SSM) metallurgy - combined with applied mathematics (AI, machine learning, big data mining, SPC etc.) and the use of self-contained fully operated modules based on an idea of HyperComMachinery concept will revolutionize the die casting industry and lift it to the 21st century.

About theory: let’s think about solving basic math problem defined as f(X)=Y. In our case Y is result of any actual part quality inspection and f(X) a proper mathematics working backwards to define optimum production system process parameters. And only this theory is working on the factory floor, not expert systems as known in past 40 years.

Figure 1: Casting machine and x-ray machine working in tandem in a digital factory of 21st century.
Figure 1: Casting machine and x-ray machine working in tandem in a digital factory of 21st century.
(Source: US and European Patent Publications)

Novelty of this invention is in the way of generating and using additional set of variables of the inspected actual parts with intention of iteratively starting process automatically and speeding the process to obtain good production parts with minimum loses in energy and materials.

A model of a fully automated die casting process is depicted in Figure 1. The operator is only required to power up the machine, chooses 3D part to be casted and ensure material is available and casted parts are taken away. For example, the die-casting wheel for a car would follow a basic sequence like this:

The operator would provide power to the semisolid casting machine, follow an initial checklist related to supply of the material, and safety verification, and then press the start button. The new semisolid casting machine would start casting the first wheel based on proposed initial process parameter data set and, upon completion, the wheel would be immediately inspected at the x-ray inspection system. The fully automated x-ray inspection system would determine the quality of the wheel and generate the digital model of the part that is now unequally identified for life of the part. If the part is good, all information about this good wheel will be stored in a good wheel data set data base. The same process would be followed for the original production (process) data set, as well as the design 3D model data set. If the wheel is bad, information will be stored in a bad wheel data set data base. Assuming that the wheel is bad, the knowledge system will determine which set of parameters need to be adjusted iteratively to improve wheel deficient characteristics, and a new set of inputs will be generated in the parameter generator. By way of a hypothetical example, the inspection may indicate that the wheel is not completely formed, indicating insufficient feedstock has been injected into the mold, commonly referred to as a short shot. The knowledge system determines a longer injection period is appropriate, and iteratively instructs the parameter generator to delay the closing of the nozzle. The next machine cycle is executed, serialized, and passed on to the inspection system. More data from the inspection system generates more information and allows for faster convergence to a set of parameters that yields good quality wheels. This process for producing magnesium wheels by closing the loop with data from an inspection system and accelerating convergence with data sets from good wheels and bad wheels as well as digital 3D model of the original design will allow the production of high integrity wheels with minimal rejects. Fully automated process will generate necessary intelligence to run the process and maintain high quality production at every time.

This is an intrinsically safe, energy efficient, environmentally sound magnesium casting factory with no emission of gases outside of factory parameters. All casting machines and x-ray inspection machines, as well as proprietary linear robotics and machine vision systems integrated into this semisolid die casting process, will ensure the fast production of good parts, and maintain stable automatic closed loop process control. The collection of process data for the same or similar parts worldwide will go via cloud and everybody can use it.

We hope this technological review provides a better understanding of where we are heading and what the future holds for our magnesium casting industry. And aluminum casting industry will follow.

(ID:46355501)

About the author

 Ashley Stone, P. Eng.

Ashley Stone, P. Eng.

Owner and CEO, Inventor, MAXImolding!™ - a division of Jacobsen Real-Time X-Ray Machinery Inc.