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SFSA Webinars

Note - we're splitting this page up into individual pages for each type of webinar

What is Steel?

Webinar slides in PDF format


19 minutes 26 seconds recorded on November 9, 2015


What Makes Steel Strong?

Webinar slides in PDF format


17 minutes 45 seconds recorded on January 26, 2016


Heat Treating Steel

Webinar slides in PDF format


29 minutes 37 seconds recorded on February 23, 2016


What Makes Steel Stainless?

Webinar slides in PDF format


24 minutes 23 seconds recorded on March 29, 2016


Introduction to Steel Melting Practices

Webinar slides in PDF format


22 minutes 27 seconds recorded on April 26, 2016


Pouring and Gating Steel

Webinar slides in PDF format



25 minutes 45 seconds recorded on May 24, 2016

Induction Melting Steel

Webinar slides in PDF format



22 minutes 54 seconds recorded on August 23, 2016

Design of Experiments

See Webinars - Design of Experiments

Clean Steel

See Webinars - Clean Steel

Porosity

See Webinars - Porosity

Cracks in Steel Castings

See Webinars - Cracks in Steel Castings

Test Uncertainty in Mechanical Testing of Steel Castings



19 minutes 28 seconds recorded on July 24 2019

Steel Casting Performance



22 minutes 11 seconds recorded on November 21 2019

Mold Metal Challenges

See Webinars - Mold Metal Challenges

Mechanical Testing

See Webinars - Mechanical Testing

Segregation and Steel Castings

See Webinars - Segregation and Steel Castings

Heat Treatment

See Webinars - Heat Treatment

Corrosion

See Webinars - Corrosion

Making Steel Castings

See Webinars - Making Steel Castings

Making Steel Castings Case Study

See Webinars - Making Steel Castings

Making Steel Castings Customer Series

See Making Steel Castings - Customer Series

Specifications

See Webinars - Specifications

Feeding and Risering

Feeding and Risering Guidelines - The Red Book

Christoph Beckermann, University of Iowa

1 hour 2 minutes 56 seconds

Miscellaneous

Basics of Export Compliance - ITAR and EAR

54 minutes 5 seconds recorded on April 25 2018

Introduction to AI for Materials

47 minutes minutes 46 seconds recorded on May 21 2025

AI and Robotics for Steel Making - An Overview

So good morning everybody and thank you for attending.

Um, so I'd like to first introduce myself, Joseph Giampapa from Lindon AI and my partner Ana Maria Berta.

She's online as well.

He also from Linden AI.

If you have any questions while I'm on a slide and before I make a pause, please feel free to type it into the chat.

And Ana Maria will look for those chat messages and if it's something to intervene and ask me a question, she'll she'll ask away.

Also, David, if there's something that you think I'm missing, feel free to intervene.

Otherwise, I would say I'll be making pauses between major sections so that people can have their questions then, and then we'll have a discussion afterwards.

Um, OK, so David, could you advance to the Excel slide, please?

So as I said, you know, the goal of this is to Reorient your, your way of thinking about AI for your organization.

Um, so in the media, we hear a lot about large language models, um, and, and the need for server farms and how we need more power generation plants.

Uh, we're hearing about a very distorted image of AI.

Um, it's an image that adds value to one sector of the AI ecosystem, but it's grossly overlooking everything else and that everything else is very applicable to small companies.

So that's what I'm focusing on here.

Next slide please.

So, Most, you know, the first part of this is going to be a definition of AI and related key terms, and I hope that it will be entertaining and something that you can operationalize.

Uh, then, uh, go into some parts about the business, um, uh, cases for AI, uh, and then a little bit about the interplay between AI and robotics for manufacturing.

Um, and robotics is huge and could probably be its own webinar in its own case, but there's, there's a special relationship that I want to emphasize.

And if there's and if people don't have any specific questions, we can go into more detailed questions about robotics for manufacturing during the discussion.

Next slide please.

OK, so, um, what is AI?

Uh, you know, my operational definition is human knowledge and reasoning in useful digital form.

this is critical because you don't know what the form is of human knowledge.

We already encode it in a variety of different ways in the ways in which we do things.

It's carried about by the personnel in our company, in our companies who have specific expertise.

It's documented.

It's institutionalized in our processes, the organization, the tools that we use, the ways in which we do things.

Uh, these are all the different ways in which knowledge is stored and distributed throughout a company so that you can use it to your advantage and to the end of creating your product and being responsive to a market.

Um, so that's the first key point that I'd like you to take away from this is that it's going to exist in a lot of different forms and you have to think about how to access it, how it's going to be useful, and what is the, and then eventually if you want to actually put it into digital use, what is the computational machinery that you can use for doing that?

And there are lots of tricks.

Part of the science of AI is discovering the tricks in which you can do that.

Um, if AI as a concept distills into a well-known discipline, it gets a name, and nobody thinks about it as AI anymore.

So you know how everyone uses Google.

Google is, you know, it's a nickname for, um, or there's a more accurate term for it, but I can't remember, for information retrieval.

Information retrieval was an AI research topic in the 1990s.

So that's an example of how when an AI topic becomes useful, you don't call it AI anymore.

It just has its own name, and then when a brand.

Um, you know, it becomes a film associated with a brand who use a brand to cover it.

Uh, next, please.

So, uh, robotics is primarily about control theory.

That uses AI for perception, state estimation.

And planning, you know, deliberative and reactive planning, um, deliberative is where it's thinking into the future before it actually commits to a plan.

Reactive plan is a controlled response type of activity.

Um, in scheduling, so if you have deliberative planning, then you have your scheduling which is prioritizing the task and trying to attain some efficiency from that.

Robotics exists in many forms.

You have the mechatronic implementation of a robotic platform, which is the mechanical and electronic control of the mechanics.

And there's some control theory baked into the mechatronics, but AI allows you to explore a broader representation and more variability in the way in which that control loop is implemented.

Next slide, please.

Everyone has heard of big data AI.

In the 2000s, it was the biggest thing that everybody was talking about, and it has given rise to, you know, the large language model type of AI that we're hearing about in the media now.

A big data AI is just one form of AI.

Um, and it has, it has its essential role, but it's not the only one.

So think of big data as where you have a lot of data, it's unstructured.

It would be very costly to try to add structure to it and you don't want to do that.

You have a very large number of known and unknown characteristics all mixed in, and trying to distill all of them would be just too much effort.

Um, it doesn't begin to work until you have um.

Orders of magnitude of data samples, like, you know, millions as a minimum, it begins to work, but you usually have to have 10s to hundreds of millions if not more, and they're usually used for one type of problem, such as the identification or classification or the programming of robotic behavior.

There's a technical difficulty.

They did somebody complained that they can't see the slide.

OK.

Um, we're still seeing data AI slide.

OK, so Does anyone else have this problem?

Yes, I only see the big data AI slide as well.

All right, but that's the one that we're on right now, OK.

OK.

So yeah, so big data is used for one type of problem like classification or programming the the trajectory of a manipulator and detector in space.

Um, it is something that would take a very long time for humans to characterize.

Uh, so for example, um, you know, how humans would do it is they would do a lot of statistical analysis on the data set.

They would say, OK, I hypothesize that the data can be characterized in this way.

And they create a selection criteria for selecting the data.

They get a certain quantity of data that conforms to that hypothesis, but then there's another, and then they have to keep on trying to determine what are the characteristics of the data by doing a variety of statistical analysis on the data set to be able to access sections of data that Relevant to solving the problem and that takes a very long time and it's not a very accurate process, whereas big data AI is actually computational machinery that first of all it makes the hypothesis about what are the features of the data that are important to comprehend and to model, and then it also determines what is the Um, the frequency or occurrence of those features in the data set to for indicating the saliency of the underlying process.

So you look at data as an indication of what's happening underneath the scenes, underneath the hood, um, and so big data AI is a way for Identifying the symptoms and the behaviors that you need to look for.

The thing about big data AI is that from a computational perspective it's very expensive.

That's where you need the GPUs because you're usually doing a lot of numerical computation, a floating point arithmetic, and um.

You're the other big expense is sometimes collecting and labeling data.

Now, as I said, this might seem a little bit contradictory, but part of the collecting and the labeling is to first of all, reduce the, the noise, the number of parameters that are unrelated to your problem, and labeling is to ensure that you have reliable data for predicting an outcome.

Uh, within the type of responses that you want.

So for example, if I, if I'm trading an algorithm to to identify house pets, if it, if you know, on the presentation of an image of a cat, it replies gorilla, that's clearly a mislabeled data item that shouldn't have been in there, and that's, that's noise.

So part of the process of big data AI.

is to curate the data so that your labels are consistent with the types of outcomes that you want to achieve.

Next, next slide, please.

So, uh, for manufacturing, uh, big data is usually used for predictive maintenance based on motor vibrations and power draw.

Um, and you need it for determining what are the states, the operating states of the machinery based on the microphones that you're using.

There's going to be a variance in the signal that they provide.

You have a variety of signal strengths and filters, and in the real world there's lots of noise, so big data AI is good for finding the signal in all of that noise.

It's good for inspection of defects in visible light, primarily because you have atmospherics, dust particles, a lot of dust and particles in the air that have an impact on a type of lighting system that you're using.

You have a variety of the surfaces of the pieces that are being examined.

You have a variety of lighting sources.

There, there's ambient light.

There's structured light.

Um, you could be using X-ray imaging, other types of imaging devices, and you have a variety in sensor performance.

Sometimes just a sensor being out of configuration is enough to send an algorithm and a data model in very wrong predictive capabilities.

So you have to Uh, you have to carefully check the calibration and that's called the intrinsics, the, the internal performance characteristics of your sensors to ensure that they are providing reliable data to your algorithms and um.

Uh, even then, there's going to be a lot of variances in the, in the signal that they provide.

Uh, so big data helps solve that problem.

Uh, next slide, please.

So small data is something that everybody is used to.

Uh, we've already been using it for a long time.

Uh, it's often, so, um, you want a reason.

So small data AI is reasoning about a state it's either a reasoning problem or a state exploration problem, and you're using structured data that's known.

You can label it.

You know very well the variables, the data types, you know where they're coming from.

The meaning and the interpretation of the data is clear and well understood, but the difficulty is in reasoning about the combination of the data because you have a what's called the state state space explosion that's combinatorial.

So think of an example is game playing like chess.

Um, you have, um, you know, just a few types of pieces that are well known that you know what their behaviors and characteristics are, but the range of all their possible moves makes a very large search space, and solving the problem of playing chess is a type of small data AI.

Now, related to your, your enterprise, it could be that, um, I think that's next slide please.

Some examples of small data, so this clarifies.

Yeah, for example, maintenance and troubleshooting guidance.

So for example, manuals.

If you have manuals and PDF form, small data AI would be a help desk that has that incorporated for doing, for example, a search over those data.

If it's structured, you can search record books, um, and it's not just doing the search and retrieval, it's also reasoning about that.

Um, you can use it for production.

Well, small data is planning and scheduling.

So if you're using a manufacturing system, it probably under the hood there's a planning and scheduling small data AI engine.

You can use it for weld planning.

You can use the information that's in a CAD design for determining what the assembly sequence is for components.

Um, you can use small data for casting inspection of CAD design.

So if you have an X-ray image of a CAD design, um, it's a combination of big data and small data in which you, you might use the big data for being robust to the variances in, um, in the images, uh, and in the, um, uh, the contrast of the images, but you use A very simplistic way to find the contrast and outline the border and then you would use a small data AI for then reasoning about the significance of the defect in the cast in in that casting and small data is also used in project planning and costing based on existing inventory and whatever other criteria that you have.

So think of small data as being able to Take into consideration your known knowns and more reliably, accurately and quickly reason about the the complex constraint space in which you're trying to optimize something.

The optimization could be you want to reduce your production times.

You have a limited workforce, you have very tight production schedules of multiple types of products that you have to get out.

AI can help in that respect if you're not already using it for for those types of problems.

Next slide, please.

So, um, a critical concept that, uh, isn't talked about much is the digital thread, and this became, um, This was the biggest proponents of this are the US military Office of Secretary of Defense Mantec that they, they are very much concerned about how, how weak their defense industrial supply base can be in time of need.

So I think at the time of production during World War II.

We were able to scale our industries through through human workforce.

We don't have that luxury anymore.

We don't, we're not able to scale production through hiring people, um, so, um, they're very concerned about what are, what are the pieces, how, how rapidly can we produce things.

So the office of the Secretary of Defense has this program.

in a working group to reduce rework and scrap in the production process because rework and scrap means delays.

Uh, it also means that there could be negative consequences and further delays if pieces are not inspected earlier in the production process.

they get further down in the assembly of the final component and then they realize that the fault is that one piece and everything else has failed because of it.

So they've been pushing this group to come up with a theory of the digital thread.

which is an abstract representation to integrate all of the systems in production from the time you take a customer order to the time that it's shipped and you're actually getting reliability information from the product at the customer site.

Um, so that's, that's where, you know, you have the performance data over the course of the product's lifespan.

And this is, this is important because the digital thread enables your little data reasoning across the whole enterprise.

Uh, so if you need to rapidly program a robot to learn how to perform an activity based on a type of part, one of the things you're going to need is the digital design of the part it needs to manipulate.

And if you can share that with the robot, that's great.

You already, you already solved a significant hurdle that somebody is going to have to program.

The other thing is.

If you need to inspect a part, your digital design is going to be the guidance by which the inspection takes place.

There are going to be other standards that come into play, such as the performance, the tolerance, and then there's also the contract which would have to be in digital form to determine what is acceptable and what isn't acceptable.

But all of that, if it's in digital form can be automated.

And doesn't require a lot of human concentration to analyze.

So thinking of the digital thread is the key insight that I would like everybody to take away from this presentation and that going from start to finish is going to be really tough.

It's something that you can do, you can get little islands of opportunity but within an enterprise there are islands of opportunity, low hanging fruit, as we like to say, that are that lend themselves to digitization efforts much more rapidly, and they're going to give a minimvalue product immediately to the operation as you begin to set up those little islands of digitization.

Um, you'll be connecting pieces.

You'll be making small modifications throughout your, your, your processes and your your tools that allow you to make those modifications, and you're going to be assembling a digital thread to be able to give direction and vision to what you're doing, just keep thinking of all the other things that could benefit from the knowledge that you're capturing in digital form and how you're using it.

and it should become a background discipline, something that you think about consistently as you're looking to improve your operations.

Um, now, having said this, uh, I think I have another slide.

The next slide please.

Right, so, uh, that's right.

So, the importance of the digital thread, um, it's the ultimate goal of a digital transformation, and business schools are beginning to talk about digital transformation, um, but they're not, they don't know anything about AI and robotics for manufacturing, so they're not completing the the fulfillment part of it other than just warehousing.

But our counterparts in Asia who have been customers of mine through Carnegie Mellon University have contracted me to develop automated manufacturing robots that take advantage of orders from the time that the customer places an order digitally online.

Uh, to the, um, uh, the engineered drawing in digital form that they then shipped to the robots, uh, for the robots to, uh, to manufacture according to their processes.

And it's a very cost effective means of maintaining human knowledge.

It's the best way.

And it's something that can be archived, retrieved, used, recombined in ad hoc novel ways.

Um, you see this in the apparel industry with the ready to wear industry that the Chinese implement.

So, um, the Chinese, um, Apparel industry, they follow social influencers and as soon as there's a social influencer who unveils a new type of garment, they they capture that design and they put it on their website for sale.

Even though they don't have anybody to manufacture it yet.

And based on the bids that they get for that garment, um, the, the Chinese websites will then access a network of apparel manufacturers who bid on the cost of making that.

in an auction, the apparel manufacturers, you know, what an award is granted, they get the contract, they manufacture it custom order according to what the person, you know, made, and then it's shipped immediately.

So you can actually offer a, a wide array, a pallet of a variety of different products to your customers without actually having to have them in inventory in a warehouse, depending on how long it takes for you to do that.

Um, and, um, well, the Chinese, they have a, um, a large, uh, human labor force.

They're also investing heavily in automating their production, and they've been doing a lot of research and application of AI to robotic manufacturing.

Uh, next slide, please.

So, this is a project I worked on in the 1990s.

I don't know what state it is, but this is something I did in Italy.

Um, Italy has a very tight labor force.

The labor unions are national.

Companies declare the type of labor force they're going to hire and to the national government, and then they make a contract.

Then there's a base contract that they make that's according to the guidance of the National Labor Union.

Uh, and then there's the private contracts.

So, um, one of the side effects of this is that they cannot fire people very easily or at a time they could not fire people.

So they cannot do surge hiring in times of large orders.

They cannot lay off.

Uh, so, um, they have to be very judicious in the way in which they hire people, but one of the things they've been doing with the help of national, regional and local governments that make available funds for workforce development.

they developed a mix of engineers through apprentice programs that integrate computer scientists with the mechanical engineers, the material scientists, the chemical engineers, the process engineers working on real industrial problems, and this was a problem that I worked on for the shipbuilder Finanieri and Lloyd's Register, the insurance company based in London.

So an oil tanker has to be certified for the waters in which it's going to be carrying its cargo.

You cannot um.

You cannot use an oil tanker for the Atlantic to carry crude oil in the Indian Ocean, for example, or, you know, there are certain restrictions about the safety given the types of weather conditions that the tankers are going to be encountering.

So one of the, the first considerations that any that the shipbuilder has to go through is.

Given the inventory for the types of steel that they have in-house, and you guys would know this better than I would, um, given the quality of the steel, um, that makes a difference on how the structure of the hull is designed.

Uh, so in a double hulled oil tanker, there's a reinforcing rebar that is welded between the two hulls.

And depending on the quality of the steel, there's a regulatory requirement about the spacing of that steel.

So in the left part where I have the inventory body of water and regulatory, those, that was the first interplay of considerations that they wanted their small data AI system to address how to help them figure out what the requirements would be.

Uh, for inventory of a certain type, what would be the time and the cost of producing that hall.

And then once they, once they had a rough calculation of the time and cost they would take and what their inventory and supply chain requirements would be, then they began to reason about the planning of the manufacturer, the tools, the fixtures, the dry docks, the workforce skills.

Um, and, uh, scheduling, and those two had an impact on the time and cost and they wanted to do all of this, um.

Across the wall type of engineering and design up front, uh, so that they can offer the most efficient cost ship design to their customers before they even commit to building it.

This was a project that we began working on in the 1990s, and I believe it's still ongoing, but you know this was the, so I was working on the part on the left, reasoning about estimating the time and cost given inventory, the regulatory restrictions for insurance purposes given the application of the of the ship.

Uh, next slide, please.

So, um, at this point, I'd like to take a break and open the floor if anybody has any questions about what what I explained so far.

I put people asleep?

No.

OK, so let's continue.

The next slide, please.

OK, so, um, you know, what's the business value proposition of AI?

Uh, the first is mechanization of mechanized production.

Um, so think of, um, the industrial revolution.

I mean, we, you know, people have probably heard about Eli Whitney and Colts firearms.

Uh, basically, the mechanization of production was a defense industrial, uh, type of, uh, problem.

Um, the, um, We were inspired by the French, uh, French advisors at West Point, um, inspired, um, uh, somebody at West Point to write a military manual about the need for, uh, mechanized production of muskets.

And that became a joint research project between the Harpers Ferry Armory and the Springfield Armory in Springfield, Massachusetts to try to mechanize the production of muskets as much as possible so that the components could be uniformly reproduced, they could be truly interchangeable, and that you can achieve scalability.

And in the process of trial and error, which took took the course of about 100 years, there are lots of things that derived as a result of it.

One of them was the recognition that the tooling and machining of the tooling and the creating of the machines for the production of the components was a large upfront cost which required the government to actually invest ahead of time in the the tooling and the setup of the machinery for the production.

Which up until that point, that point hadn't been a way in which the government did contracting.

Uh, so, um, in the early days people didn't, they discounted that.

They actually didn't think about it.

They said, oh, we'll just do it and you know, we'll pay you on, on delivery.

Well, um, so that was one of the things that changed.

Um, it's not going to change the contracting requirements, uh, but, you know, what, what you have is the possibility of creating new machines with software.

And you know, this became very clear from the time that we had the PC revolution and computers became more accessible at a low cost.

Everybody's writing their own software.

You have a limitless possibility of creating a new software machine.

Um, what AI does is allow you to apply that same flexibility to, um, mechanical production, uh, because now you have, um, AI enabled perception.

AI enabled planning and AI-enabled control that that allows for the complexity and variation of creating real mechanical artifacts.

So, um, abstractly, you're just, you know, saying, well, we're, what we're doing is we're putting knowledge for how humans do things manually in this in the production and using our machines and tools, and we're transforming that into software and digital forms and we're creating limitless number of production machines that we can configure very quickly just with, you know, just as long as we can hook up the connectors and get the data flowing.

And then have it, um, you know, attached to the manufacturing machines that can be reconfigured based on those parameters.

Now, you know, I understand that I'm oversimplifying, but the vision is there and you see this with CNC machines and that that have automatic tool changers, those are very flexible.

They do a bit of adaptable fixturing.

Uh, even with the additive manufacturing, it's basically a robot arm within within an enclosure.

So we're seeing that concept appear in types of islands.

So you know, now that you have these digital islands, think about how you can reconnect them to other components and other steps in your manufacturing process and the value that your company is creating.

So you know, number one value proposition is the mechanization of your mechanical production processes.

Next slide please.

A right size production and you know, high mix low volume, we want to be able to go from high mix low volume to low mix high volume.

Now, so we're already oriented for low mix high volume, and the reason for that is the upfront overhead cost of tooling.

Um, Now understanding that you have that, the question is, uh, can that be lowered anymore?

Can you, can you repurpose it for creating a new variant of your product that addresses a different market sector that you're not currently addressing?

Um, our counterparts in Asia are investing in that significantly.

One of the things that Americans complain about when doing business and contracting with the government or even defense is they'll get an order for something in a very small bot that's experimental, and then over the course of a couple of years they'll get an order for hundreds of those or something like that.

So for a very rapid rescaling of the production, so.

You know, the value proposition is, if you think about an AI control of your mechanical production process, that should enable you to more effectively respond at those types of situations.

Um, and it should also allow you to respond to Um, uh, prior production that perhaps you produce a large, uh, quantity of items and you need to rapidly switch, uh, between that and multiple, um, product lines.

So, um, next, next slide please.

So, uh, again, higher precision production.

So in the introduction of mechanical means of producing goods.

Uh, increase the, um, the requirements for metrology, reliable mechanical metrology.

Um, if you begin to manufacture things through automated means, um, you even have, in some cases you have even more requirements for precision above and beyond what you're currently used to dealing with.

Um, some of the cases could be, well, you know, if I, if I need to, uh, program a robot to have a behavior of removing so much welded material.

Uh, perhaps the issue is not so much the weld material as it is the fixturing used for creating the weld in the first place, because if the fixturing or if the production of the pieces was within tighter tolerances, there would be less, less of a gap to fill with weld material and so there would be less need for grinding and removing the excess weld material.

So that's, that's one example.

Um, the digitization basically gives you a review.

It gives you an opportunity to review your production processes and make improvements and tighten to tolerances and also to parameterize your process and discover that you can do things a little bit differently or with more variety and possibly offer that as as a product for your customers.

Uh, next slide, please.

Um, AI adds value to your workforce.

Uh, so, yeah, robotics, um, Robotics is good.

It addresses the 3Ds, dirty, dull, and dangerous.

Uh, it also replaces unhealthy, highly repetitive tasks.

Um, um.

My partner and I were at a conference a couple of weeks ago in which food production, they have workers holding boxes, putting boxes on a conveyor belt as machines are automatically filling the boxes with food.

After they place the box on the conveyor belt, they have to go take a box that has already been filled, close it, tape it, and put it on a palletizer.

And they have to do that at a rate of speed that doesn't give them time to scratch their nose or change a roll of tape.

It is incredible, and they have turnover and burnout.

Um, workers don't stay in that position for more than 10 days.

Uh, so, um, you know, that definitely can be automated through, uh, through robotics, um, and, um, you know, People are happy and my father, for example, who has worked in as an inspector, he was happy when I told him that his job was replaced by a robot.

You know, it's not that he wouldn't be an inspector anymore, but he changed to a more higher level and fulfilling job.

So workers acquire market relevant skills.

They have less motivation to job hop, and the skilled workers, the people with the know-how and the experience, can be more appropriately tasked to problem solving, which is what humans are really great at, and, you know, thinking about the best way to solve solve new problems for your company.

You don't want to have them bur burdened down into something that could be replaced by automation.

Um, and then from the perspective of your labor, similar to what I explained in Italy, um, automation within the workforce allows the company to manage the cost of labor for the company.

They don't solve their problems through surge hiring.

They don't lay off, which also makes for stronger loyalty and permanence of the employees toward the company.

Uh, next slide, please.

OK, so I'll pause there if anybody has any questions or comments.

I'd only just I don't know how much more you have, but we're already 3/4 of the way through the time slot longer, but I don't know when you want to really have a discussion.

OK, I think I have about 4 more slides, so we're close to the end.

I have a question though.

Oh, sure.

Yeah, this is Joe Korff.

Hey, have you been in a steel foundry and and actually walked through the processes of a typical jobbing foundry to, to consider what applications you may see, uh, may be the quickest and best to incorporate?

I have been through a couple, yes.

And which processes in the jobbing foundry would you say have the most fruit to bear?

Um, well, yeah, first off, the first one that I saw was inspection, uh, inspection of steal castings, and there's one that David and I discussed, which was spraying of moldings for either for coatings or for quenching.

OK, thank you.

Um, OK, so why both AI and robotics?

So you know, we're used to, you can think about a robot just as a mechatronic device without any AI.

Um, if you program it by a, a teach pendant, uh, that's, that's the way in which you're using it.

Uh, you're not really using it, it's not AI enabled.

Um, and when I was speaking with Yawa.

They were talking about the latest brand of robot that's going to be AI enabled.

I asked him, Well, what does it mean to be AI enabled?

He says, Well, we're attaching a computer to the controller so that you can program it.

Uh, so that's, you know, basically they're talking about software enabled robots in which you can introduce more elaborate and robust reasoning enabled by AI.

And the whole point is you want to be able to program behavioral skills for the robot that are robust to variations in the environment, the workpiece, the tools, and where there's feedback and, um, uh, you know, um, any, any sort of variation that you can't necessarily control by giving it, um, mechanical instructions through through a teach pendant.

And once learned, the skills can be copied to multiple instances of the robots, so you have a scalability that is really enormous.

Next slide, please.

So, um, you know, if you do not think of integrating your robot in a digital thread, you're basically creating a monument.

Uh, so, um, you, you want to avoid that, and the reason is because knowledge capture the first time is expensive.

If you're programmed by teach pendant, that's what you're doing.

All of the time you're waiting is for the programmer to capture the knowledge for doing the things you want to do, and um, you know, you, you're, once you have that encoded in that in the, in the code that's flashed into the memory.

Uh, it gets very difficult to be able to use that, uh, and reuse it and combine it in other types of programs.

Um, so put it in software and have software control your robots.

A knowledge maintenance, that's another costly aspect of AI.

It's not just a matter of encoding knowledge the first time.

It's also maintaining it because the world changes.

The world changes because your end factor changes.

You get a new application.

The environment changes.

There are all these things that have an impact on the behavior of the robot, and you have to adapt to that.

Uh, it gets very difficult if you do not already have, um, an AI enabled process by which you can maintain, uh, maintain the adaptability.

Um, so, you know, the whole goal is facilitate a cost-effective capture and maintenance.

Um, next slide, please.

So, uh, yeah, this is, this is concluding, uh, the presentation.

So there's a lot more that can be covered, such as, you know, if we can talk about robots specifically, uh, how to select a robot, uh, how to create a cell types and safe of safety and their considerations, tooling, fixturing, um, we can discuss them now, um, next slide, uh, David, I don't know if there's anything else.

So, right, just a summary and key takeaways.

So, um, I gave you 4 compelling business reasons and I hope they were convincing.

Uh, to introduce AI and robotics into your manufacturing.

Um, without AI manufacturing robot, risk becoming a manufacturing monument, and you want to break that mindset.

You want to, you want to think of digital threads and how rapidly you can reconfigure.

Um, aim to, and you know, you want to aim to create an enterprise manufacturing digital thread for the capture and maintenance of your knowledge.

45 minutes 23 second recorded on November 1, 2025

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