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Industry 4.0 and AI Best Practices - Connected World

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Here's an attention-grabbing idea: Deploying cellular-enabled Industry 4.0 solutions can generate a 10-20x operational cost-savings ROI (return on investment) over the course of five years. This is according to a joint research study from ABI Research and Ericsson. The research also suggests Industry 4.0 solutions can generate up to 8.5% in operational cost savings, which, for a factory or industrial site, can equate to an operational cost savings of up to $600 per square meter per year. Industry 4.0, also known as the fourth industrial revolution, is the idea that connectivity, automation technologies, and digitization are creating the fourth major revolution in the business of manufacturing. Thanks to trends like leveraging the IoT (Internet of Things), including wireless networking and sensors to collect machine data and enable predictive maintenance, as well as 3D printing, robots and cobots on the factory floor, machine learning and AI (artificial intelligence), 5G, and digital twins, among other trends, the Industry 4.0 market is projected by MarketsandMarkets to reach almost $157 billion by 2024. A big part of Industry 4.0 is the use of AI technologies to enable smarter machines that can take on tasks like self-monitoring and diagnosis autonomously.


Data Mining and Machine Learning: Fundamental Concepts and Algorithms: The Free eBook - KDnuggets

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We are pleased to announce the second edition of our book Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Second Edition, by Mohammed J. Zaki and Wagner Meira, Jr., published by Cambridge University Press, 2020. The entire book is available to read online for free and the site includes video lectures and other resources. New to this edition is an entire part devoted to regression and deep learning. The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners.



PrintSyst launches pre-printing analysis tool powered by artificial intelligence

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PrintSyst.ai has launched its latest proprietary artificial intelligence (AI) engine which aims to improve the consistency and reliability of 3D printed parts. The 3DP AI-Perfecter is a pre-printing evaluation tool and has been designed to allow companies in the aerospace, defence and automotive industries to produce additively manufactured parts with greater repeatability and reduced labour, time and cost. It believes analysis of parts before the physical 3D printing to be crucial and a process that requires highly-skilled engineers to carry out, while also baring significant risks to a company's reputation should errors be made. PrintSyst has therefore spent the last couple of years focusing on artificial intelligence and leveraging the technology to create a platform that, the company claims, has enabled instant, automatic and accurate pre-printing part analysis that can save up to 99% of the preparation time and cost. "It is a scalable tool and using it is extremely user friendly and simple," commented Itamar Yona, PrintSyst's CEO. "We support multiple 3D printing technologies and our customers enjoy automatic AI-based printing recommendations.


Machine learning can replicate toolpaths in 3D printed fiber reinforced parts – IAM Network

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A research team from the NYU Tandon School of Engineering has published a study that uncovers vulnerabilities in the production of carbon fiber reinforced 3D printed parts. The vulnerability is not related to the strength of the parts, but rather in protecting their toolpaths and preventing counterfeit parts. The ability to 3D print carbon fiber reinforced polymers is creating numerous exciting applications across the aerospace and industrial sectors, among others. The materials are advantageous for many reasons, but their strength-to-weight ratios and durability are most notable. However, the process of 3D printing these materials, and specifically the extrusion-based process, can actually reveal the construction of the part and its design.


Reverse engineering of 3-D-printed parts by machine learning reveals security vulnerabilities

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Over the past 30 years, the use of glass and carbon-fiber reinforced composites in aerospace and other high-performance applications has soared along with the broad industrial adoption of composite materials. Key to the strength and versatility of these hybrid, layered materials in high-performance applications is the orientation of fibers in each layer. Recent innovations in additive manufacturing (3-D printing) have made it possible to finetune this factor, thanks to the ability to include within the CAD file discrete printer-head orientation instructions for each layer of the component being printed, thereby optimizing strength, flexibility, and durability for specific uses of the part. These 3-D-printing toolpaths (a series of coordinated locations a tool will follow) in CAD file instructions are therefore a valuable trade secret for the manufacturers. However, a team of researchers from NYU Tandon School of Engineering led by Nikhil Gupta, a professor in the Department of Mechanical and Aerospace Engineering showed that these toolpaths are also easy to reproduce--and therefore steal--with machine learning (ML) tools applied to the microstructures of the part obtained by a CT scan.


Avular designs custom drones from the ground up with 3D printing – IAM Network

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Source: AvularSince its founding six years ago, Eindhoven, Netherlands-based Avular has built aerial drones for industrial and agricultural clients. Business was good, but for the first several years, there was a frustrating limitation: "We started to get a lot of customers asking, 'Can I do this with a drone? Can I do that with a drone?'" said Albert Maas, co-founder and CEO of Avular. "We often had to say, 'I'm sorry, it's too complicated to take this very niche, dedicated system and build something else with it.'" Two years ago, Avular decided to flip that script.


Reimagining knee replacements with additive technology and artificial intelligence

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Italian medical implant manufacturer REJOINT is introducing mass customization and therapy personalization through a combination of Electron Beam Melting (EBM) and computerized analysis of intraoperative and post-operative data collection through IoT-connected sensorized wearables. The market for knee implants is now estimated at around five million implants per year worldwide. In advanced markets, already in 2011 the number of surgical procedures was 150 per 100,000 inhabitants, with peaks of 250 in some markets such as Austria and Switzerland. The strongest annual increase (7%) occurred in patients 64 years and under 1. The knee arthroplasty market until recently solely consisted of standard prosthetic systems, with a limited range of sizes available.


Reimagining knee replacements with additive technology and artificial intelligence

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Both additive manufacturing and artificial intelligence are integral to its growth strategy. "When it came to additive manufacturing, we were initially …


3D Hangouts – RGB Matrix Fruit #3DPrinting

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The DIY 3D printing community has passion and dedication for making solid objects from digital models. Recently, we have noticed electronics projects integrated with 3D printed enclosures, brackets, and sculptures, so each Thursday we celebrate and highlight these bold pioneers! Have you considered building a 3D project around an Arduino or other microcontroller? How about printing a bracket to mount your Raspberry Pi to the back of your HD monitor? And don't forget the countless LED projects that are possible when you are modeling your projects in 3D!