Implementing smart manufacturing – leveraging the digital twin
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Implementing smart manufacturing – leveraging the digital twin
Industrial machinery in manufacturing is witnessing dynamic technological advancements. It is a monumental mission to manage this new wave of advanced manufacturing and assembly operations for achieving the highest standard of quality while optimizing cost.
Transcription of our second podcast in this series teaches us how machine manufacturers are leveraging the digital twin to make their manufacturing smarter. We are discovering the significance of digital twin for machine builders and examining the digital twin process from design through manufacturing, operations management, commissioning and service life.
Our resident expert and guest is Bill Davis, who is the Director of Industrial Machinery and Heavy Equipment Solutions at Siemens Digital Industries Software, with over 30 years in the industry and 20 years as an engineer.
Below, is the transcription from our Thought Leadership Community’s second audio podcast on this topic. For the full audio podcasts or blog series, refer to the links at the bottom of this page.
Blake (interviewer): Welcome to the Siemens Digital Industries Software podcast series on smart manufacturing brought to you by the Siemens Thought Leadership team! Bill, in the manufacturing industry we hear about 3D printing or additive manufacturing. Just briefly touch on how that comes into play with smart manufacturing?
Bill: Certainly. 3D printing is one of the newer trends in the manufacturing process. It is what CNC machining calls subtractive manufacturing. So, the additive is building up the part from base materials of powder or liquid product base raw material. The interesting aspect from a machinery perspective is it allows the machine designer to take advantage of the complex machine parts (15-20 parts) that would be built up, so they were expensive, highly-precise and requiring much assembly time. Currently, the engineer can start the process looking at the outcome or output from a performance parameter and discover how that additive part can be made less costly, with greater reliability and durability.
There are some ramifications from a smart manufacturing perspective and the digital twin of that part usually requires more than just additive manufacturing. It requires post-process machining, and another handling of that part to make it ready for the industrial machinery assembly. So, an additive is sparking a whole new class of machinery that includes both the additive side as well as the subtractive machining side in the same machining center. That’s the significant change that we see from a machinery manufacturing perspective. We’ve got the design piece of it, but we also must handle the manufacturing perspective in the machining centers. Therefore, it’s causing our machine shops to reinvest in more complicated machinery.
Blake: Right. And you mentioned digital twin. Can we discuss that topic in the context of machinery and how Siemens Digital Industries Software is impacting digitalization?
Bill: When we look at the digital twin, the conventional discussion is around digital twin design. Everyone claims that they have the digital twin of the machine, and it’s true – they have 3D models and representations of it. However, the digital twin is much more than just the design parts. Conventionally, people think about the digital twin as being primarily the mechanical component of the machine. What’s happening is more adaptability is being driven and introduced in the machine by means other than mechanical. We’re using more servo-motor drives and more software to create a flexible and adaptable machine.
So, you can’t have a digital twin unless that digital twin reflects the electrical part – the software and PLC programming.
Within the design realm, multiple disciplines must be incorporated into the true digital twin and Siemens Digital Industries Software has the software that manages all aspects. Also, there’s a need for simulation to be connected to the digital twin. Historically, this is performed after the design is worked on and released. If a problem is found in the field, then the simulation figures out what was wrong, and an engineering change is made. We then need to integrate that simulation into the digital twin upfront, as part of the creation of the digital twin in the engineering space; otherwise, we don’t have a complete digital twin.
Let’s take that into the manufacturing space because we’re talking about smart manufacturing here, so we take all those parts, whether mechanical, electrical or software and drive them down into specific manufacturing disciplines. Then from our part manufacturing process, the CNC, CAM programs, CMM or inspection files are generated so that we can automate the process system manufacturer for those parts.
On the software side, we simulate the execution of that machine in its digital twin, so if the machine has a rack that’s supposed to extend 200 mm in one direction and 200 mm in another direction, we simulate that with kinematics, so we can see what’s happening to the machine as the slide accelerates back and forth. Thus, we’re simulating the software, execution, and physical domain for that engineering piece.
On the electrical side, we deal with a tremendous amount of variability. One of the topics rarely discussed from an industrial machinery perspective is that we often build machines that are a one-off, and we want to tie them back to a central digital twin machine’s origin of product families and variant configurations. So, from an electrical perspective, you may have manufacturing requirements that for one customer is Siemens hardware, and for another a customer, such as Rockwell hardware. And, those machines, while their functionality is the same, their configuration from an electrical and mechanical purchasing requirement is entirely different.
Therefore, the variability where customers and machinery builders find themselves is reflected in that digital twin. Also, it’s essential to realize how manufacturing execution occurs as a reflection of the digital twin. So, it’s not just the parts that need to be produced, and the program that needs to be executed, but how to manage the delivery of parts, the manufacturing of those parts from an operations management perspective and the quality control.
From my operations management background, I see one of the biggest challenges is having multiple machines that you’re trying to build simultaneously and coordinating all these activities to deliver the correct parts to the right place, at the right time and reflect that in the manufacturing operation. So, we need to incorporate the designed bill of materials, configure it to fit the manufacturing and assembly operation, deliver the finished machine to the customer expeditiously and visualize that process again for the manufacturing process – juxtapose to other customers in this same space.
So, the digital twin spans this whole gambit from design through manufacturing, operations, management, into commissioning and the service life.
Blake: That’s useful information. I think people have a misconception about the digital twin because of hearing so much about it. They only think of it in terms of product, and I think you really outlined to us all the disciplines that are involved.
So, in looking at smart manufacturing for the corporation or manufacturing company that is wanting to improve their process reduction process, could you cover some of the benefits of smart manufacturing? How it’s going to help them at a general level?
Bill: Let’s talk about it from the context of what needs to be delivered by the OEM or machine builder when we begin to have conversations with customers about smart manufacturing. Some factors play into the complexity of the machinery process, and how to create value from the digital twin. How do I address the digital twin’s complexities in both my manufacturing operations, supply chain requirements and regulatory quality requirements? So, the multiple facets of the machinery build are important to leverage that common backbone or framework of the digital twin to integrate digital engineering and regulatory quality requirements in closing the loop.
I can use machinery builders, typically serving many different industries, whether food processing machinery or high-tech electronics. Regulations are a significant part of that and the single source profitability loss. This is what we call margin erosion, which comes from not complying with regulations. So, having that traceability between the digital twin through the execution and proving that we met the regulatory requirements is a crucial part of smart manufacturing.
The manufacturing of the machinery is an elaborate dance between supply chain, internal manufacturing, and assembly. So, we need to have a united knowledge management piece that ties everything together. Smart manufacturing allows us to have the portability to take a design and move it across the border to a different facility, and retain the quality and reliability for the manufacturer’s product. So, this is a significant piece of smart manufacturing that’s tying into the design, extension, manufacturing operations and operations management.
Blake: Thank you, Bill, for another enlightening conversation on leveraging the digital twin in smart manufacturing.
We learned that the digital twin in smart manufacturing is more than merely a mechanical component or 3D model of a product. The digital twin is representing the electrical part, software and PLC programming. Also, multi-disciplines are being implemented into the digital twin via software, managing all aspects, including the integrating of simulation to proactively find problems and solve them.
This concludes the sixth blog in a series on smart manufacturing and the trends affecting the industry. Our future blogs will continue to spotlight excerpts from the transcribed conversation of the original podcasts.
By the expert: Bill Davis