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AI & Machine Learning in the CNC Machining Industry – ReadWrite

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Frank Landman


Like many industries, the manufacturing industry experienced a significant impact from COVID-19 over the past year. Perhaps the biggest impact is that manufacturers have accelerated their adoption of IoT and various sensors in an adapt to future-proof against similar circumstances. CNC machining, which is the bedrock of manufacturing, is no exception.

But apart from COVID-19, why now? What makes it plausible and sustainable? Two things: the increasing adaptation of 5G and its usability on the manufacturing floor and machine learning that utilizes that network.

Whether you’re taking a look at the supply chain, a completed and installed component, or anything in between, automated optimization is the future of manufacturing. 5G will allow for the vast amounts of data from sensors to be seamlessly integrated, analyzed, and processed in real-time. Machine learning will be responsible for those actions.

These adaptations will also ameliorate some of the worker shortage that manufacturing is currently experiencing. Although there will be less demand for workers, there will be an increase in the technological skills workers need.

What’s the Difference Between Artificial Intelligence and Machine Learning?

Sometimes people use machine learning and artificial intelligence (AI) interchangeably. Artificial intelligence is the broader idea that machines can carry out tasks in a way that mimics humans or is “smart.” Machine learning is a specific application of AI. It, especially deep learning, is what will most impact CNC machining in the near future.

Deep learning is a category of machine learning that layers algorithms in such a way that it creates a learning system for the machine. This learning system doesn’t require as much human guidance as other machine learning or tools currently in use on the machine room floor. As the computer parses the data in one layer and has its findings, those findings improve the decision-making power in the next layer.

How Do AI and Machine Learning Help on the Machine-Room Floor?

AI and machine learning have the potential to impact a myriad of things on the machine room floor, from the people to the product to the machines themselves. There are many machine monitoring systems at use on machine-room floors already. However, as the monitoring systems improve their ability to gather data and pull in information from ERP systems, the data will be ideal for machine learning. This will increase exponentially as sensor use spreads.

Machine learning will be essential to make sense of the vast amounts of gathered data. This data will help manufacturers identify opportunities to optimize processes, machines, reduce downtime, and much more. Here’s a look at some that are on the horizon or already being implemented in various forms.

Reducing Machine Downtime

As sensors monitor standard components on CNC drills, lathes, and mills, they’ll be able to predict when parts are approaching the end of their life cycles. This is key because even as other tasks are automated (such as calculating and entering offsets), the tool wear continues to creep in and impact results before it can be detected.

Subsequently, machines can break mid-production or need a part that isn’t readily available. Predictive maintenance can save money, time, and resources. Planned downtime via sensors allows for just the right amount of maintenance and extends the life of the machine’s components. Machine learning and AI can parse the data and help manufacturers identify the best time to plan for downtime.

MachineMetrics is an example of this. Their anomaly-detecting algorithm can identify when a machine is in a different state than a pre-established baseline. The company gathers streaming data from hundreds of diverse manufacturing companies and thousands of different machines. When a machine may stray from a baseline, it’s time to look at maintenance.

Optimization for CNC Machines

There are several examples of companies that are already exploring ways to optimize CNC machines. Some of it is taking place within a virtual setting (like NCSimul’s latest update), while others are taking place using machine data. The optimization in a program like NCSimul optimizes the pre-manufacturing process, as programmers are able to test parameters within a digital twin—a virtual copy of the CNC machine.

An example of a company using machine learning and IoT to optimize CNC machinery was Fraunhofer Institute’s project to mount 5G sensors directly onto the workpiece. That sensor monitored chatter and would correct to avoid errors when it sensed the chatter. They were able to reduce the average blisk rework rates by 10%, from 25% to 15%. That small change could result in large savings for a manufacturer.

Automation of Processes

The amount of data that the machine-floor sensors produce would overwhelm an individual attempting to analyze it on their own. However, using machine learning to evaluate the data, manufacturers will be able to see trends that they might not have otherwise noticed. When the information from the sensors placed throughout the factory is input alongside ERPs, CRMs, and other systems, opportunities for automation will come to light. These opportunities will help ensure that items are delivered right-on-time. Automation, together with robots, will help manufacturers meet the growing pace and demand of the market.

Robots & Cobots Can Increase Production & Decrease Accidents

Machine learning and AI will also show up on the machining floor in the form of robots and cobots (collaborative robots). Some cobots may do simple tasks such as putting finished parts into bins. In other instances, they may replace tools or insert pieces into the CNC machine’s chuck. Robots and cobots can work 24/7, increasing the production levels of a factory, and by handling some of the more dangerous tasks, can reduce accident occurrence.

CNC machining’s future is quickly approaching a more holistic manufacturing process, where machines and humans are more linked via the IoT sensors and cloud computing with 5G. AI and machine learning are critical to this advancement, and some would argue, undergird the entire process. They will optimize and improve efficiency on the machine room floor, leading to greater output and fewer errors. This optimization will be necessary in order for companies to keep up with the growing demand for quality products with a faster turnaround.

Frank Landman

Frank is a freelance journalist who has worked in various editorial capacities for over 10 years. He covers trends in technology as they relate to business.

Politics

Fintech Kennek raises $12.5M seed round to digitize lending

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Google eyed for $2 billion Anthropic deal after major Amazon play


London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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