The battery sector is turning to artificial intelligence for clues on how to improve recharging rates without increasing the degradation of lithium-ion batteries.

Last month, a team from Stanford University, the Massachusetts Institute of Technology and the Toyota Research Institute published findings from battery testing aimed at cutting electric-vehicle charging times down to 10 minutes. The research, published in Nature, revealed how artificial intelligence could speed up the testing process required for novel charging techniques.

The researchers wrote a program that predicted how batteries would respond to different charging approaches and was able to cut the testing process from almost two years to 16 days, Stanford reported. The technique was used to evaluate 224 possible high-cycle-life charging processes in just over two weeks, the researchers said.

The research effort has been in progress for at least three years. In 2017, the Toyota Research Institute committed $35 million to artificial intelligence battery research, initially focusing on new materials.

Last year, the research partners claimed artificial intelligence could help predict the useful life of lithium-ion batteries to within 9 percent of the actual life cycle of the products.

The standard way to test new battery designs is to charge and discharge the cells until they die,co-lead author Peter Attia, now of Tesla but then a Stanford doctoral candidate in materials science and engineering, said in a press release at the time.

Since batteries have a long lifetime, this process can take many months and even years. Its an expensive bottleneck in battery research.

Independentof these efforts, a Canadian firm called GBatteries is using artificial intelligence in a bid to cut lithium-ion battery charging times down to five minutes. The company has succeeded in recharging an electric scooter battery in less than 10 minutes.

The main challenge with extremely fast charging is that it heats up and degrades the battery, GBatteries co-founder and Chief Commercial Officer Tim Sherstyuk told GTM.

The rates that can be achieved with todays fast-charging technology, which are slow by gas-station filling standards, are already problematic for batteries, he said.

Most fast-charging initiatives focus on novel chemistries that wont degrade easily, Sherstyuk said. GBatteries, meanwhile, uses artificial intelligence to monitor the state of the battery as it is charging.

Once the impedance of the battery reaches a critical level, the GBatteries algorithm pauses charging long enough to avoid irreversible damage. This allows charging to proceed in a series of high-intensity pulses at a rate much faster than is possible with traditional methods.

The GBatteries technology works for small batteries and has been demonstrated on power tools, cutting charging times from between 30 to 60 minutes down to 11. But scaling it up to cope with an electric vehicle battery pack is going to take a while, said Sherstyuk.

Even if artificial intelligence can help crack the means to charge electric vehicles as quickly as you now fill your tank with gas, it will take a while for the auto industry to incorporate the technology into the mainstream. The time horizon is years, not months.

Nevertheless, there is plenty of industry interest in tackling the problem.

Charging time is usually the fourth concern that people raise when considering to go electric or not, after upfront cost, range of the vehicle and where [to] charge, said Aaron Fishbone, director of communications at GreenWay, which operates a fast-charging network across Eastern Europe.

So, while not a top-tier issue, its still one raised by many people.

GBatteries pulse charging will require a lot more testing before it might be considered appropriate for the 50+ kilowattpower ratings required for electric vehicles, Fishbone said. In the meantime, high-power recharging is already reducing the time it takes to charge a battery.

Although there are not yet many cars that can take them, a 150-kilowatt charger can add 100 kilometers (62 miles) of range to an electric vehicle within a little over seven minutes, Fishbone said.

Nonetheless, anything which can speed up charging time without degrading battery life is a welcome development and can lead to other innovations which push the whole industry."

See original here:
Battery Researchers Look to Artificial Intelligence to Slash Recharging Times - Greentech Media News

Subscribe to The Financial Brand via email for FREE!

Applying artificial intelligence to everything were comfortable doing in banking is much easier than changing how we do things which would make the greatest use of AI.

Few in financial services would argue that the future belongs to those institutions that harness data-driven machine intelligence to do more, better and faster. The insights and efficiencies needed to compete and thrive will come from AI-driven service personalization and optimization.

But AI should do more than speed up a financial assembly line. As Ernst & Young stated in a report: AI-driven financial health systems will become personal financial operating systems. Consumer finance will unbundle products and rebundle personalized and holistic value propositions based on life events.

While that is a worthy goal, the retail banking industry will not come any closer to achieving that if it continues the way it is thinking about and implementing AI.

I call the current mindset for applying AI to financial services the Product Gun. Its the familiar banking model of manufacturing a product, targeting a market segment for distribution, ensuring everything complies, and then shooting it to potential consumers. Its worked well for many years, but its had its day.

Hopes of providing consumers with personal financial operating systems and solutions tailored to life events wont happen merely by blending AI with the same old thing. In fact, applying complexity and leverage to well-understood financial products and processes may produce unintended consequences.

( sponsored content )

But rethinking from the ground up can be rare. Models and the basic process behind them often dont change because business typically likes to save energy. Take the Boeing 737. The jets design dates back to 1964. The first one flew in 1967. The latest iteration still flies today. This makes a perfect example of leveraging an old business model to sustain profits as the saying goes, if it aint broke

Because banking is a regulated industry that deals with heaps of money and risk, a control structure has evolved to organize competencies and lines of business. Risk and profit are put in little boxes for success. Boxes like manufacture, target, and comply all have executives, KPIs, spreadsheets and politics. On the whole, it has worked well.

The problem is, innovative tools like AI get shoved into the same old boxes. Instead of using this technology to reimagine traditional processes, we use AI to build a supercharged 737.

This has some benefits to financial institutions business lines. This could include improving the consumer credit process, reducing compliance exceptions or automating support desks. Each of these, and similar applications of AI, could benefit the industry and those it serves.

However, AI can produce missteps, such as unwittingly biased outcomes. At best staying trapped inside old processes with new AI insides will do no harm, but its still not going to bring us closer to a vision for personalized, holistic financial advice.

Read More:

I want to propose something radical: Financial institutions should be optimizing for their clients needs. This sounds extremely simple but the commitment required, and the roadmap to making this reality is serious, expensive and difficult when it can take a long time to deliver on expectations.

Jeff Bezos said: Put the customer first. Invent. And be patient. It took Amazon 20 years to be profitable, and during that time Bezos kept investing to optimize his understanding of and delivery for his customers. Amazons impressive margins came about relatively recently, and only after a long battle.

The alternative to the traditional Product Gun attitude is something I call Mother Mind. This goes beyond simply shooting products at people. It gathers intelligence about what and who people are and what they need. It understands deeply what customers are going through in their lives, then it guides them with strategies that are actually going to be useful in the context of their lives. Used in this way, AI can keep guiding an institution in ways to better serve people and businesses.

Read More:

REGISTER FOR THIS FREE WEBINAR

The Crawl, Walk, Fly Approach to CX Like the Big Brands

Leading consumer brands provide a better customer experience by using data and insights to drive relevancy and personalization and integrate the experience with other channels.

Thursday, April 2nd at 3 pm EST

Change is hard for financial institutions, but it can happen. Here are three achievable pivots that can help put a bank or credit union on the path to success.

Gather: Move from system-centric data to human-centric data. Even under the progressive framework of Europes PSD2 open banking framework, financial institutions still store and access data in a system-centric way: transactions, products, accounts and balances. Data organized this way makes it very difficult to understand much about peoples individual circumstances.

Today data comes in the language of systems and ledgers. To do anything radically different requires shifting to the language of human lives. This means building interfaces to data that will allow financial institutions to ask questions about peoples behavior and needs. What are their financial personalities? What events in their lives offer the chance to be of assistance?

Understand: Move from products to journeys. The word customer-centric means nothing if products continue to be bankings foundation. How do we distribute the product for less? How do we recommend products to customers at the right time? such thinking is inverse, today.

Consumers needs change as their lives and circumstances change. At any point and time they have problems that need solutions and questions that need answers. Whether or not these journeys are successful is going to start meaning a lot more. Customer love or hate is going to be a profitability issue in a world where switching providers is easy. Focusing on understanding people will result in institutions working in a completely different way the measure wont be on sales but on problems resolved.

Guide: Move from selling to advising. By virtue of living in a product-centric world, financial institutions have become sales-driven. But when the barrier to entry to manufacturing and distributing products keeps lowering, traditional institutions increasingly find themselves fighting fintechs and others for turf they used to think they owned. Shiny objects may grab attention and move a sale once, but when thats over, if an institution hasnt built a meaningful relationship, people will leave.

People want to be understood, and they want to be cared for. In the context of financial services, this means people want advice. Advice is not about buying a product. Its about working towards goals, planning for transitions and hopefully creating an overarching, happy story of personal wealth.

Putting energy into human-centric data and focusing on understanding makes the aspiration of providing personalized holistic advice more possible.

The personal financial operating system wont happen overnight, but institutions can move towards it. Personal financial management offerings that keep people aware of their situation, tools that help them plan for retirement, and hybrid advice platforms that enable collaboration are all steps in the right direction.

More here:
Rethinking Financial Services with Artificial Intelligence Tools - The Financial Brand

(Washington, D.C.) The Army Research Laboratory is exploring new applications of AI designed to better enable forward operating robot tanks to acquire targets, discern and organize war-crucial information, surveil combat zones and even fire weapons when directed by a human.

For the first time the Army will deploy manned tanks that are capable of controlling robotic vehicles able to adapt to the environment and act semi-independently. Manned vehicles will control a number of combat vehicles, not small ones but large ones. In the future we are going to be incorporating robotic systems that are larger, more like the size of a tanks, Dr. Brandon Perelman, Scientist and Engineer, Army Research Laboratory, Combat Capabilities Development Command, Army Futures Command, told Warrior in an interview, Aberdeen Proving Ground, Md.

The concept is aligned with ongoing research into new generations of AI being engineered to not only gather and organize information for human decision makers but also advance networking between humans and machines. Drawing upon advanced algorithms, computer technology can organize, and disseminate otherwise dis-aggregated pools of data in seconds -- or even milliseconds. AI-empowered sensors can bounce incoming images, video or data off a seemingly limitless existing database to assess comparisons, differences and perform near real-time analytics.

At the speed of the most advanced computer processing, various AI systems can simultaneously organize and share information, perform analyses and solve certain problems otherwise impossible for human address within any kind of comparable timeframe. At the same time, there are many key attributes, faculties and problem solving abilities unique to human cognition. The optimal approach is, according to Perelman, to simultaneously leverage the best of both.

We will use the power of human intelligence and the speed of AI to get novel interactions, Perelman added.

This blending, or synthesis of attributes between mind and machine is expected to evolve quickly in coming years, increasingly giving warzone commanders combat-sensitive information much faster and more efficiently. For instance, a forward operating robotic wingman vehicle could identify a target that might otherwise escape detection, and instantly analyze the data in relation to terrain, navigational details, previous missions in the area or a database of known threats.

You have an AI system that is not better than a human but different than a human. It might be faster and it might be more efficient at processing certain kinds of data. It will deal with threats in concert with human teammates that are completely different than the way we do things today, Perelman said.

With these goals in mind, the ARL is now working on mock up interfaces intended to go into the services emerging family of Next Generation Combat Vehicles. Smaller robots such as IED-clearing PackBots have been in existence for more than a decade; many of them have integrated software packages enabling various levels of semi-autonomy, able to increasingly perform a range of tasks without needing human intervention. Current ARL efforts now venture way beyond these advances to engineer much greater levels of autonomy and also engineer larger robots themselves such as those the size of tanks.

Army Research Lab Mock Up of Next-Gen Combat Vehicle AI-Enabled System

Bringing this kind of manned-unmanned teaming to fruition introduces new strategic and tactical nuances to combat, enabling war commanders a wider and more immediate sphere of options.

Commanders will be able to view a target through vehicle sensor packages, or if there is an aided target recognition technology or some kind of AI to spot targets, they might see battlespace target icons pop up on the map indicating the location of that target, Perelman said.

AI-oriented autonomous platforms can greatly shorten sensor-to-shooter time and enable war commanders to quickly respond to, and attack, fast emerging moving targets or incoming enemy fire.

Everything that a soldier does today. Shooting, moving, communicating.. Will be different in the future because you do not just have human to human teammates, you have humans working with AI-teammates, Perelman said.

Enabling robots to understand and properly analyze humans is yet another challenging element of this complex equation. When you have two humans, they know when the other is cold and tired, but when you bring in an AI system you dont necessarily have that shared understanding, Perelman said.

Various kinds of advanced autonomy, naturally, already exists, such as self-guiding aerial drones and the Navys emerging ghost fleet of coordinated unmanned surface vessels operating in tandem. Most kinds of air and sea autonomous vehicles confront fewer operational challenges when compared to ground autonomy. Ground warfare is of course known to incorporate many fast-changing variables, terrain and maneuvering enemy forces - at times to a greater degree than air and sea conditions - fostering a need for even more advanced algorithms in some cases. Nevertheless, the concepts and developmental trajectory between air, land and ground autonomy have distinct similarities; they are engineered to operate as part of a coordinated group of platforms able to share sensor information, gather targeting data and forward-position weapons -- all while remaining networked with human decision makers.

You can take risks you would never do with a manned platform. A robotic system with weapons does not need to account for crew protection, Perelman said.

Interestingly, the Army Research Lab current efforts with human-machine interface are reinforced by an interesting 2015 essay in the International Journal of Advanced Research in Artificial Intelligence, which points to networking, command and control and an ability to integrate with existing technologies as key to drone-human warfare.

They (drones) should effectively interact with manned components of the systems and operate within existing command and control infrastructures, to be integral parts of the system, in Military Robotics: Latest Trends and Spatial Grasp Solutions, by Peter Simon Sapaty - Institute of Mathematical Machines and Systems, National Academy of Sciences.

Increased use of networked drone warfare not only lowers risks to soldiers but also brings the decided advantage of being able to operate in more of a dis-aggregated, or less condensed formation, with each drone and soldier system operating as a node in a larger integrated network. Dispersed forces can not only enable longer-range connectivity and improved attack options but also reduce force vulnerability to enemy fire by virtue of being less aggregated.

Despite the diversity of sizes, shapes, and orientations, they (drones and humans) should all be capable of operating in distributed, often large, physical spaces, thus falling into the category of distributed systems, Sapaty writes in the essay.

Also of great significance, Army thinkers explain, is that greater integration of drone attack assets can streamline a mission, thereby lessening the amount of soldiers needed for certain high-risk operations.

When you are calling in artillery or air support, there is a minimum distance from where you are able to do that as a human being. You dont have the same restrictions with robotic systems, so it allows you to take certain risks, Perelman.

A paper in an Army University Press publication explains how drones can expand the battlefield. By utilizing drone systems for combatfewer warfighters are needed for a given mission, and the efficacy of each warfighter is greater. Next, advocates credit autonomous weapons systems with expanding the battlefield, allowing combat to reach into areas that were previously inaccessible, the essay states. (Amitai Etzioni, Phd, Oren Etzioni, Phd)

This article by Kris Osborn originally appeared in WarriorMaven in 2020.

Kris Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army - Acquisition, Logistics& Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel and The History Channel. He also has a Masters Degree in Comparative Literature from Columbia University.

Image: Reuters

Continued here:
The Army Will Soon Be Able to Command Robot Tanks With Artificial Intelligence - The National Interest

SAN DIEGO, March 18, 2020 (GLOBE NEWSWIRE) -- smartboost, an AI Digital Agency, announced today its new company name and business model. The founders of CNG Marketing and SIO Digital have merged their companies together to create smartboost, the first ever AI marketing agency.

In 2014, smartboost founders Giovanni Letellier and Clement Connor created CNG Digital Marketing, a digital marketing agency that was focused on building small businesses. CNG utilized advanced technology to reach and exceed clients goals. The company quickly grew from two founders to over 15 employees in its first year.

In 2016, CNG created its sister company, SiO Digital, that focused more on medium to large businesses, SiO Digital, was also an AI-powered and data-driven marketing agency. Giovanni transferred his responsibilities as CEO of CNG to Clement and took the role of Chief Strategist, so he could dedicate more time to SIOs growth and future projects.

After a successful six years in partnership with CNG and SIO and while servicing over 100 clients and growing, Giovanni and Clement wanted to merge the two companies to become smartboost.

"Our new name goes much deeper than just a new website and brand colors. It represents the merging of one of the first AI-powered Marketing Agencies with a best-in-class creative digital agency. The future starts now, said Giovanni Letellier, Founder and CEO of smartboost.

smartboost is proud to be the first AI-powered marketing agency alongside an innovative digital creative agency. smartboost is at the forefront of digital marketing and has a proven track record of building businesses through data-driven digital analytics. When business owners are working with smartboost marketers, designers, developers, and engineers, theyre all doing the same job: driving results through data.

smartboost will continue to grow as a collective and is dedicated to staying ahead of marketing trends through advanced AI-technology. This is an exciting time for many types of businesses looking to take advantage of the digital age. By working with smartboost, business owners will be working with the most innovative and technologically advanced agency in the United States.

About smartboost

smartboost is comprised of a highly-skilled team of creative marketers, scientists, and mathematicians all experts in our fields. With a proven track record of data-driven results by the notion of excellence, we see people and Artificial Intelligence working in symbiosis to help businesses survive and grow. smartboost is focused on impact and transparency and our technology is in a constant state of transformation.

PR Contact Kathleen Gonzales kathleen@elevated-pr.com

See the original post here:
San Diego-Based Company takes Digital Marketing to the next Level by Launching the First Artificial Intelligence Marketing Agency in the United States...

Kasper and Frans, thank you for joining us today. To kick off, can you tell us briefly why using Artificial Intelligence (AI) in a green sand foundry is a good idea?Kasper: DISA has been helping foundries collect, visualise and analyse their data with our Monitizer suite for a few years now. Adding AI capabilities to do more with this data is a logical next step and its a big one. Monitizer | PRESCRIBE which is what our AI product is called harnesses the power of AI to optimise the whole foundry process, significantly reducing scrap while increasing capacity and production predictability.

Frans: Theres a lot of hype around AI so at DataProphet, we like to quote real results to show whats possible. Over the last two years, the average AI-driven defect reduction across all of our manufacturing customers is 40%. With some, its 80% or 100%. Few foundries take full advantage of Industry 4.0 techniques so the potential for them is enormous.

Our Expert Execution System (EES), enabled by AI, has helped a foundry in South Africa cut defect rates in grey iron engine block castings by 50% in the first month. For the first time ever, they achieved zero internal defects on all shipped castings over three months and now save over $100k every month.

How does AI help deliver these kinds of results?Kasper: The key word here is automation. Many green sand foundries already collect and analyse process data but its usually limited to single sub-processes like moulding or pouring. The data for each process stays separate and basic manual analysis is done using spreadsheets or simple statistics.With an entire foundry line, optimisation can involve hundreds or even thousands of variables across all the different process stages. Making sense of that complexity manually is just impossible. AI automates this analysis, using the cloud to access vast computing capacity. Thats the only way to handle the complex, large sets of data that will give us new insight that will in turn make a genuine difference to a foundrys performance.

So what does an AI solution like Monitizer | PRESCRIBE actually do?Frans: It starts by analysing historic production and quality data to learn from past mistakes and corrections, to find what works and what doesnt. It considers how the parameters within and across all the different processes are related, how each one influences the other and what the ultimate combined effect on quality is.From that analysis, Monitizer | PRESCRIBE finds the optimal process parameters and tolerances for a particular casting and process. Knowing the best recipe, it can prescribe hence the name the best actions to take to improve quality.

Kasper: A good example is where, even though all your process parameters are within tolerance, you still might see bad quality castings. Often, this is because one metric is slightly high, another is slightly low and so on. Its a specific combination of values that produces the defect, not a single extreme one. Because the AI has learnt how parameters like grain size, moisture content, pouring speed or inoculation rate influence each other, it can pick the right settings for minimum defects.

So thats like a much more effective version of todays offline analysis. How does AI help you apply those learnings during real production?Kasper: Monitizer | PRESCRIBE applies what it has learnt to live data keeping an eye on what your foundry is doing right now, in real time. That gives you dynamic process control, reacting instantly as conditions change, like ambient air temperature or sand moisture content, and telling operators on the line the optimal settings or actions to take in time to prevent defects occurring. It keeps on learning too, constantly optimising the production process towards zero scrap and improving other metrics like productivity and resource use.Frans: Data-driven, real-time optimisation is sophisticated second-order control. By constantly monitoring machine and process data, then telling you which adjustments to make and again monitoring their effect, our AI tool gradually gets every part of your process running in harmony. You achieve a stable operating regime with the best quality and minimum quality variance. A good analogy is with an autonomous car which can automatically keep you in the middle of a motorway lane.By constantly computing the optimum process parameters, our AI keeps your process in the middle of the lane.

Its clear that automation and data analytics have enormous potential but many foundries have yet to adopt the basics here. So is it really possible for any green sand foundry to make use of AI?Kasper: We see digital as a four-step journey where you start with data collection and visualisation, then move at your own speed towards analytics, AI and automatic process control. Of course, we can help customers do all of that very quickly if they want to.Our NoriGate is the only hardware involved for data collection and everything else is a cloud service which we can deploy in any foundry or with existing data collection infrastructure. That makes it very quick and resource-efficient to deploy. You wont need any new IT hardware, data scientists or any extra staff.

We can digitise every step in the green sand process, take data from paper records or pull it from Excel, and give you a single trustworthy, time-stamped database ready for investigation. At each step, you can achieve significant benefits.The point is that, no matter if you are just starting out or are digitally advanced, there are things we can do that help you take the next step very rapidly indeed.

So you dont have to be a rocket scientist to make use of AI?Frans: AIs inner workings can be complicated to understand but together we have developed it into a packaged service that works for foundries. Its not hard to implement it and its not capital-intensive. As Kasper says, everything you need to collect, store and report on the data is already available from DISA and well proven.Some foundries think they are too old school for digital, but AI projects can be realised when theres no strong data environment or even if they havent really previously captured data at all. Our partnership with DISA enables very rapid digital progress in any type of foundry.

Does your partnership between an industrial AI company and a foundry equipment expert make your solution different to the other AI products we see emerging?Kasper: A lot of vendors say they have an AI system, but a pure IT company may never have seen a foundry from the inside before. We bring a combination of deep foundry experience and DataProphets award-winning expertise in manufacturing data science with more than 35 engineers, statisticians and computer scientists dedicated to developing AI solutions. This collaboration makes our service uniquely practical and effective. Its already tried and tested in a green sand foundry environment and were finding that fact is very attractive for customers. For example, we are currently installing the full Monitizer suite including MonitizerPRESCRIBE at a large European foundry group.

From DataProphets point of view, how does DISAs experience in green sand foundries help an AI project succeed?Frans: When you implement an AI solution in manufacturing, its vital to capture domain knowledge completely and correctly. As the leading OEM supplier, DISA know green sand intimately and are very much the experts in the foundry environment. They know what to do and which questions to ask right at the start. That means value from a running system arrives in weeks, not months or years.

DISAs customers also trust them to keep their promises and they understand that MonitizerPRESCRIBE will be delivered and managed through them. If DISA puts its name to it, customers know it will be an effective, high quality product and that will be supported in five years time and in ten or twenty years too.

Is this AI solution just for DISA customers?Kasper: The entire Monitizer suite, including NoriGate and MonitizerPRESCRIBE, is machine-agnostic, so its not limited to DISA machines or even to the green sand process. Monitizer is a Norican-wide solution, so every foundry can benefit from it, whether its pouring iron or die-casting aluminium.

Frans, with your experience, how do you think foundries compare to other manufacturers in their application of digital tools?Frans: Some other manufacturing environments are now quite sophisticated in their use of software and data, which is not often the case for foundries. With IoT infrastructure and Expert Execution Systems like MonitizerPRESCRIBE, there is a real opportunity for foundries to leapfrog the older IoT systems and access the very latest technology without having to make an enormous investment.

Are there any common misconceptions about AI you hear from your foundry customers?Frans: They can be worried that their data might be used in another customers AI which never happens. MonitizerPRESCRIBE can ingest and interpret all a customers foundry data and that certainly doesnt include data from other customers.

Monitizer | PRESCRIBE is designed with full tenant sandboxing: every clients datastore, database, and model is uniquely encrypted, and every component is isolated from every other component in the system. It is not possible to mix data or models between clients and the data is safeguarded with every possible measure.

Kasper: Some people think AI needs another in-house IT system thats big, complex and very expensive. But Monitizer | PRESCRIBE is an online service, it simply gives you a tool to optimise quality and productivity. Also, when we talk to foundry staff, some fear an AI system will come in and take over their job. But this isnt about taking jobs. The information AI gives will help them make better decisions and improve their own performance. It will make them look good.

Are there any other AI-related advantages for foundry owners and their workforces?Kasper: Theres a generational change going on in our industry. Engineers with 30 or 40 years experience are retiring and our customers are worried that their knowledge of how to keep their own unique processes running correctly will be lost. But their knowledge is encoded within historical process data. Monitizer | PRESCRIBE can access that and put it to work. With more automation, the foundry also becomes a cleaner, more attractive place to work. You can spend most of the time in an office-like control room, which will be more appealing to todays potential recruits.

Frans: By learning from human intelligence, expressed in millions of decisions made over the years, the AI becomes the central knowledgebase for the foundry. Then it can support less experienced engineers and operators in their decision making. A lot of value for manufacturing customers lies in selecting and extracting those good decisions so theyre never lost.

If AI helps foundries move from offline analysis to continuous guidance, what comes next?Frans: The end goal is a foundry that runs its own processes automatically similar to what the autonomous vehicle industry is aiming to achieve with cars. Staff will gradually move from continuously analysing processes and adjusting machines to focus on tasks theyre better suited for like innovation, creation and ideation. The plant of the future will re-configure itself for the optimal delivery of new customer orders, adjusting its configuration, production schedule, energy consumption and staff roles to give maximum efficiency.

Kasper: The system will adjust settings automatically, for example, when sand properties change, and you need more additives, or if the humidity changes and the sand dries out faster so you need to add more moisture. All these variations are corrected manually today and, even with Monitizer PRESCRIBEs real-time advice, usually still will be, but the system will handle it all automatically in future.

How close is this fully autonomous future?Frans: Were not there yet, but it will definitely happen for some foundries in the next few years. Most foundries are starting to collect data and analyse it, so they are being assisted by data today. Our system goes from there to guiding them with specific real-time recommendations. The self-driving foundry is the next stop on the journey.

Kasper: Were already helping customers fully automate parts of their DISA line, like moulding and pouring, or sand mixing and moulding, though complete automation of the whole line is a little way ahead at the moment. But I think it will arrive a lot sooner than completely autonomous cars.

Many thanks to both Kasper and Frans for a fascinating explanation of how they are working together to bring AI to foundries.

DISAs AI solution Monitizer | PRESCRIBE is currently live with selected pilot customers and will be available in the coming months. More information can be found here. [https://www.disagroup.com/en-gb/foundry-products/digital-solutions/monitizer/monitizer-prescribe]

Read the original post:
The next step in digital transformation: is Artificial Intelligence production-ready for green sand foundries? - Foundry-Planet.com