NFT's and Blockchain for Engineering

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Does the engineering world need to care about NFTs and blockchain?  in Venturebeat. 

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If the first question out of people’s mouths about either blockchain or NFTs is “What exactly are they?” the second question inevitably is “Is this something that I actually need to care about?”

If you’re an artist who makes a living selling art, the answer might be yes. But if you’re in the engineering world, the potential benefits are far less clear.

If you’re an artist, you can see where this might be useful: You can put your art up for sale, and a collector can purchase an NFT that says that they are the official owner of that piece of artwork. (In theory, anyway – more on this in a bit.)

So, how might this apply to the engineering software space?     ‘I did that’

Unlike the individuals in the art world, people in the engineering world don’t generally create 2D files and 3D files just for the purpose of artistic expression and then try to sell them. They’re creating these assets because they intend to do something with those files, like designing and manufacturing an actual real-world object.

So, strike one: not much utility to be found for NFTs and blockchain on that particular front. But maybe there’s some other application in the engineering space, perhaps around intellectual property and documentation of the product development process?

Picture a manufacturer that is designing an innovative new bicycle. One engineer is in charge of the bike frame. As they go through the development process, each time they make a new CAD file, they check it into the blockchain so that their work on this new product is documented on the blockchain. Years down the line, if they need to prove their work on this product for some reason, a permanent, publicly available record is there for all to see, and the engineer can say “I did that.”

It sounds like a nifty use case. But alas, here is where the “theoretical” benefits of blockchain quickly run into some buzzsaws.     Not so fast…

For starters, while there are a small handful of companies making technology that does this type of thing, they are few and far between. In terms of the innovation-adoption curve, this field is really still in its infancy – which is surprising since the underlying tech has been around for almost 15 years.

That’s not to say that there aren’t plenty of enthusiastic voices out there around the potential of blockchain and NFTs in the engineering world – but not all of them have saintly motives. If someone has purchased a boatload of Bitcoin or NFTs for purely speculative reasons, they’re likely to champion anything having to do with blockchain and its potential because it indirectly benefits the investments they’ve already made.

Then there’s the matter of blockchain’s environmental impact. Even the newer, more evolved blockchain protocols like Ethereum still consume gargantuan amounts of energy [subscription required] as they record and validate transactions across a distributed and decentralized ledger. With the “proof of stake” mechanism for validating entries, this energy usage is less of a problem, although it has other issues. The bottom line, however, is that, on a fast-warming planet [subscription required] staring down a climate emergency, blockchain is a hard technology to embrace unless transactions can be made radically more efficient.

All of this is to say nothing of the fact that much of what blockchain could enable the engineering space to do is already possible to do – and much more easily accomplished – via existing methods. Want to show proof of prior work on a product? Any time you check your CAD file into some kind of CAD or PLM system, there’s an audit trail of who accessed, created or modified the file. Need an indisputable patent? There’s a patent office that decides those types of things.

While it might be nice to get an “official” NFT saying that the work in a CAD file is officially yours, that NFT doesn’t mean you own it in any legal sense. It’s just a digital signature that means something in “the world of blockchain” but doesn’t necessarily mean something legally. ....  '

Smart Screws

 Interesting application to link manufacturing elements (here screws) with specifications to determine their proper application or condition.    Very useful application it seems. 

Smart Screws Keep Bridges, Machines, Wind Turbines Safe

Fraunhofer-Gesellschaft (Germany), May 2, 2022

At Germany's Fraunhofer Cluster of Excellence Cognitive Internet Technologies, researchers developed a self-powered Internet of Things (IoT) device that permits remote monitoring of the stability of screw connections in an object or structure. Smart Screw Connection incorporates sensors and radio technology in a device featuring a screw fitted with a washer containing a piezoresistive DiaForce thin film. The sensors record the preload force at three points when the screw is tightened, and changes in the preload force, which alter the electrical resistance in the thin film, are transmitted via radio module on the screw head to a base station. Fraunhofer's Peter Spies said, "No engineer is required to be on site and check each screw individually, as all the data are transmitted via radio to the service station."  .... '

Digital in Construction

Somewhat surprising given their engineering focus.

Decoding digital transformation in construction

Few engineering and construction companies have captured the full benefit of digital. Five practices can help E&C companies move beyond isolated pilots and unlock digital’s value across their enterprises ..... "

Digital Twins Grow Up

"Digital Twins Grow Up," by Samuel @samthewriter Greengard, says that digital twins, or exact #virtual representations of physical objects and #systems, are revolutionizing #engineering, #manufacturing, and other fields.

Digital Twins Grow Up    By Samuel Greengard    August 6, 2019

One of the things that makes computers so remarkable is their ability to create digital representations of physical objects and systems. This allows designers, engineers, scientists, and others to build models and simulations that deliver deep insights into how machines operate, when systems fail, and how complex scenarios play out over time.

Exact virtual representations of physical objects and systems—a.k.a. digital twins—are redefining and even revolutionizing fields as diverse as agriculture, engineering, medicine, and manufacturing.

"We have reached a point where it's possible to have all the information embedded in a physical object reside within a digital representation," says Michael Grieves, chief scientist for advanced manufacturing at the Florida Institute of Technology and the originator of the concept nearly two decades ago.

You've got twins!
From power turbines to jet aircraft, smartphones to office buildings, organizations are now using digital twins to predict how systems will perform, when they will fail, how people use them, and how a vast array of variables and conditions factor into outcomes. These digital representations, often incorporating computer-aided design (CAD) and building information modeling (BIM) software, are becoming crucial tools for unlocking cost savings, greater efficiency, and innovation.

The value of digital twins revolves around their ability to reduce or eliminate wasted physical resources—which can include, time, energy, and materials, Grieves points out. "The use of digital twins is ushering in the next phase of operational and productivity improvements," says Joe Berti, vice president of offering management for Watson IoT IBM Cognitive Applications. He says that a growing array of data points—generated from sensors and devices residing within the Internet of Things (IoT) and pushed through machine learning and AI systems—are advancing the sophistication of digital twins at a rapid rate.

For example, NASA now uses digital twins to better understand how to design, test, and build spacecraft. The agency is developing a framework that allows it to see when a component or vehicle is operating efficiently and safely in the virtual world before commencing manufacturing in the physical world.

GE also has embraced the concept. It operates digital steam turbines and wind farms that are exact representations of all physical assets. The firm has predicted that integrating its wind power software with a 2MW wind turbine in a digital twin setup can increase energy production by as much as 20%.

Meanwhile, the City of Cambridge in the U.K. is creating digital twins to better understand traffic and manage air quality.

Gartner has predicted that "billions of things" will be represented by digital twins by 2022. "Their proliferation will require a cultural change, as those who understand the maintenance of real-world things collaborate with data scientists and IT professionals," the firm noted in an online post about strategic and technology trends.  ....  " 

AI Used to Strain-Engineer Material Properties

Conceptually quite interesting.   AI seems to be involved in controlling the combinatorically complex ways that strain can be applied.   Does not link to the article mentioned.  Searching that.

Using Artificial Intelligence to Engineer materials’ properties
New system of “strain engineering” can change a material’s optical, electrical, and thermal properties.   By David L. Chandler | MIT News Office 

Applying just a bit of strain to a piece of semiconductor or other crystalline material can deform the orderly arrangement of atoms in its structure enough to cause dramatic changes in its properties, such as the way it conducts electricity, transmits light, or conducts heat.

Now, a team of researchers at MIT and in Russia and Singapore have found ways to use artificial intelligence to help predict and control these changes, potentially opening up new avenues of research on advanced materials for future high-tech devices.

The findings appear this week in the Proceedings of the National Academy of Sciences, in a paper authored by MIT professor of nuclear science and engineering and of materials science and engineering Ju Li, MIT Principal Research Scientist Ming Dao, and MIT graduate student Zhe Shi, with Evgeni Tsymbalov and Alexander Shapeev at the Skolkovo Institute of Science and Technology in Russia, and Subra Suresh, the Vannevar Bush Professor Emeritus and former dean of engineering at MIT and current president of Nanyang Technological University in Singapore  ... 

 ... “This new method could potentially lead to the design of unprecedented material properties,” Li says. “But much further work will be needed to figure out how to impose the strain and how to scale up the process to do it on 100 million transistors on a chip [and ensure that] none of them can fail.”

“This innovative new work demonstrates potential to significantly accelerate the engineering of exotic electronic properties in ordinary materials via large elastic strains,” says Evan Reed, an associate professor of materials science and engineering at Stanford University, who was not involved in this research. “It sheds light on the opportunities and limitations that nature exhibits for such strain engineering, and it will be of interest to a broad spectrum of researchers working on important technologies.” ... '

AI and Everyday Products

In MIT News, some interesting directions, good view of whats happening in academia linked to practical applications.

Revolutionizing everyday products with artificial intelligence
Mechanical engineering researchers are using AI and machine learning technologies to enhance the products we use in everyday life. .... " 

By Mary Beth O’Leary | Department of Mechanical Engineering 

What Machine Learning Engineers Need to Know

Useful piece. 

What machine learning engineers need to know
The O’Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar.     By Ben Lorica. 

Machine Learning and Chaos

Quite remarkable, if the results can be applied to real world engineering problems.

Machine Learning’s ‘Amazing’ Ability to Predict Chaos by By Natalie Wolchover in Quanta Mag

In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.
alf a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future. Unable to pin down the state of these systems precisely enough to predict how they’ll play out, we live under a veil of uncertainty.

But now the robots are here to help.

In a series of results reported in the journals Physical Review Letters and Chaos, scientists have used machine learning — the same computational technique behind recent successes in artificial intelligence — to predict the future evolution of chaotic systems out to stunningly distant horizons. The approach is being lauded by outside experts as groundbreaking and likely to find wide application.

“I find it really amazing how far into the future they predict” a system’s chaotic evolution, said Herbert Jaeger, a professor of computational science at Jacobs University in Bremen, Germany. ... " 

No indication in the paper abstract of how far such predictions can extend.   Or how their prediction value has been tested.     Technical:    https://arxiv.org/abs/1710.07313    A Challenge still it appears.

See also previous links to signal processing.

Will AI Address Engineering Grand Challenges?

The statement of the challenges alone is interesting.

Jeff Dean Thinks AI Can Solve Grand Challenges–Here’s How
Alex Woodie in Datanami

In 2008, the National Academy of Engineering presented 14 Grand Challenges that, if solved, had the potential to radically improve the world. Thanks to recent breakthroughs in artificial intelligence – specifically, the advent of deep neural networks — we’re on pace to solve some of them, Google Senior Fellow Jeff Dean said last week at the Strata Data Conference.

The Academy certainly didn’t lack for ambition 10 years ago when it drew up the 14 Grand Challenges. Delivering a solution for any one of them – such as providing energy from nuclear fusion or finding out how to sequester carbon – could have a dramatic impact billions of people’s lives.

As a result of advances in deep learning techniques, the presence of enormous data collections, and the availability of massive server clusters, we will be able to compute our way toward solving them, Dean told a packed room of attendees during his presentation Thursday afternoon at the San Jose McEnery Convention Center. ... " 

Predictive Maintenance

Some of the earliest examples of useful AI were in the area of maintenance, and predictive diagnosis.  We examined a number. of engineering applications.  The market value of use is enormous.  From cars to jet engines.   Natural to look at the latest machine learning techniques here.  MIT's progress in the area.   Detailed papers are pointed to.

Let your car tell you what it needs
MIT team develops software that can tell if tires need air, spark plugs are bad, or air filter needs replacing.

David L. Chandler | MIT News Office 

Imagine hopping into a ride-share car, glancing at your smartphone, and telling the driver that the car’s left front tire needs air, its air filter should be replaced next week, and its engine needs two new spark plugs.

Within the next year or two, people may be able to get that kind of diagnostic information in just a few minutes, in their own cars or any car they happen to be in. They wouldn’t need to know anything about the car’s history or to connect to it in any way; the information would be derived from analyzing the car’s sounds and vibrations, as measured by the phone’s microphone and accelerometers.

The MIT research behind this idea has been reported in a series of papers, most recently in the November issue of the journal Engineering Applications of Artificial Intelligence. The new paper’s co-authors include research scientist Joshua Siegel PhD ’16; Sanjay Sarma, the Fred Fort Flowers and Daniel Fort Flowers Professor of Mechanical Engineering and vice president of open learning at MIT; and two others.  .....  " 

Chaos Engineering in Practice

Don't remember ever hearing of Chaos Engineering, but had done some engineering in Chaotic, non predictable situations.  It should be noted that this is not about chaos theory, in a mathematical sense, but rather testing and adjusting complex systems.   Nora Jones in InfoQ describes it in practice.

Free 81 page Book on the topic via O'Reilly,

Where they describe it and its development and use by Netflix:

Building Confidence in System Behavior Through Experiments.

" .... With so many interacting components, the number of things that can go wrong in a distributed system is enormous. You’ll never be able to prevent all possible failure modes, but you can identify many of the weaknesses in your system before they’re triggered by these events. This report introduces you to Chaos Engineering, a method of experimenting on infrastructure that lets you expose weaknesses before they become a real problem.

Members of the Netflix team that developed Chaos Engineering explain how to apply these principles to your own system. By introducing controlled experiments, you’ll learn how emergent behavior from component interactions can cause your system to drift into an unsafe, chaotic state.  .... "

How might this be integrated with forms of process modeling.  like BPM?  Could the testing be applied to a process model?

Arup and 3D Printing

We did some work with the Engineering and  design firm Arup in the innovation space.   Was interesting to see they are doing work with 3D printing.

This Intelligent 3D Printer Is Building Big, Beautiful Structures  by Alison E. Berman

Ai Build, a London-based startup, aims to pave the way to 3D printing on large scales.

The company is equipping industrial-grade Kuka robotic arms with artificial intelligence and "3D printing guns" to 3D print large structures that focus on maximizing efficiency with labor and materials.

Founder and CEO Daghan Cam dreamed up the technology while considering traditional commercial construction and wondering what a more efficient and automated process might look like.

In October, the company partnered with engineering consulting firm Arup Engineers to debut the 3D printed “Daedalus Pavilion” at the GPU Technology Conference in Amsterdam. The structure is roughly 16 feet wide and 14 feet tall. Its 48 parts were printed in 15 days and assembled in less than one.

Ai Build's system uses video cameras outfitted with machine learning algorithms to allow robots to learn from their mistakes—meaning they can operate more quickly, correcting for errors on the fly instead of moving slowly to prevent them. According to Cam, Ai Build's arms can print in half the time it would take using standard techniques.   .... " 

Engineering Mindsets

A view of the book Applied Minds. by Guru Madhavan.   Here I would suggest that the engineering mindset has taken over vast realms of value over the last three decades. Yes, both scientific discovery and design methods are important components.  But at the end of the day engineering thinking makes it work and keeps it working.   Could it be applied?  It is being applied every day.

” Such a thought process could be applied to problems in our everyday lives, Jon writes, as “an organizing principle for personal or professional progress … Engineers translate the realm of ideas into practical reality; they not only make the world work, they make the world not break.”

A Phase Advisor for Industrial Chemical Engineering

A recent conversation with a chemical engineer at MIT led me to remember an expert system a member of our AI group, Dan T. Davis wrote in the late 1980s.  It was meant to provide advisory consulting in the surfactant chemistry space. And was based on the expertise of  employee Bob Laughlin, author of "The Acqueous Phase Behavior of Surfactants".   It was based on methods of knowledge engineering and rule based expert systems.  I am doing some additional digging on the methods and history of this system and will post them here.    Anyone have specific examples of use and methods, contact me.

Frugal Innovation

Frugal Innovation,  by Navi Radjou and Jaideep Prabhu, The Economist, Book brought to my attention.  Today we have many more resources than ever before. Via IdeaConnection.

Content Engineering and AI

Short piece by Omri Astel made me think about how ad content can be engineered based upon the audience.  Early steps have been taken in this direction.  For example, with Watson User Modeling. See this Description/Documentation.    And also some demonstrations.   We experimented with similar methods in the late 80s, managing the interactions of our brand equity.  Based on those early experiences, considerable progress has been made, but not yet to the level of what we would call intelligence.

Donald Knuth on the History of Computing

In CACM: Stanford Prof Donald Knuth is a hero to many of us who grew up during the real emergence of computing, to the point that most every person uses it personally every day.  He spent much time systematizing it as a science  Are we now dumbing the science down?  Yes, I say, but that has also brought it to many more people as sets of engineering principles.  A direction that sounds perilous.   Call it accessibility.     Talk and commentary.  (Updated)

Shelf Edge Displays

Experimented with the behavior influence of shelf displays. They can provide information and engage.  Here is a new example:

" .. Display Solution, based in Gilching, Bavaria, Germany, is a leading developer and marketer of customized LCD solutions for existing and future markets, combining leading-edge screen and electronic technologies. Products range from single components to complete system solutions built around TFT displays for commercial and industrial use with application in such areas as digital signage, point-of-sale, point-of-information, automation, measurement engineering, medical instruments, and other mobile and embedded applications. ... " 

Where do you Start With the Internet of Things?

Good piece on the topic. by James MacLennan.   There is lots of talk everywhere of course, but what do you do to fit his into a business?   We spent years thinking  about this when it was mostly implemented with RFID. Now the parts of the system can be more intelligent.  " .... where does a Business Unit (BU), a product line, an engineering team start the journey. Remember, industrial manufacturers historically (and quite correctly) see their role as “making & shipping Stuff”, and the shift to “providing & supporting Data-Enabled Stuff” is a pretty big leap. In addition, there are plenty of other things going on – there aren’t many teams with spare time on their hands, looking for a Next Big Project to fill the idle hours. ... As I make these connections with BUs in our organization, I am seeing patterns in how the product teams are adding Information as a fundamental part of their offering; they fall into these categories … "

Read the rest at the link.

Service Science from Informs

I have been reminded by Jim Spohrer about the importance of services, tasks and the allied sciences to provide what we call intelligence, artificial or otherwise.  Now often being called Cognitive Science.  I had never before thought of that particular characterization.  Informs,  the Management Science Organization that was my primary professional connection for years, has a number of resources about service science. Including the below description of scope.  Recently they write:

" ... Smart Service System submissions would be most welcomed at the Service Science, INFORMS Journal. 

http://pubsonline.informs.org/journal/serv

About Service Science
Modern businesses rely on technology, communication, information, automation, and globalization. They operate in a complex web of suppliers, customers, and other stakeholders, creating value by sharing skills and capabilities with others for mutual benefit. Service science is the emerging study of such complex service systems. It involves methods and theories from a range of disciplines, including operations, industrial engineering, marketing, computer science, psychology, information systems, design, and more. Effective understanding of service systems often require combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. .... "