deep learning for smart manufacturing methods and applications
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What is additive manufacturing Business
Author: Shane Schick,An aerospace company may need to design parts that will withstand the toughest imaginable conditions. A medical firm could require a way to customize its approach to creating a new life-saving device. Filmmakers want to create an unforgettable experience for audiences watching their next blockbuster movie, but it involves modeling sets and props through multiple iterations quickly. These are just a few of the industries where additive manufacturing is creating new opportunities to innovate at speed in a cost-effective way that can also promote sustainability. As technologies like continue to advance, businesses are poised to make even greater use of 3D printing and manufacturing than ever before. What is additive manufacturing?,Additive manufacturing is the process of taking a computer design file and using a 3D printer to turn it into a physical object. An organization might start with an idea for a new part or piece of equipment, for example, which is visualized through a computer-aided design (CAD) application. A 3D printer can then divide the image into layers, which are then built one slice at a time. Material may be melted down (with a laser, for instance), as slices are fused together and then cooled down so they can be handled. Additive manufacturing can also help add value in prototyping and with small batch runs, which are really the backbone of all manufacturing, as opposed to high upfront set up costs associated with creating molds or other tools which are difficult to justify other than on long production runs. Additive vs. subtractive manufacturing,This is in contrast to other methods of production. In subtractive manufacturing, for example, goods are produced by stripping or cutting away from a piece of raw material like a block of stone or a piece of metal. Machining, milling and carving are among the many different forms of subtractive manufacturing. These traditional types of manufacturing can sometimes require firms to produce molds and casts that require considerable effort and investment. Once designed, it can be equally expensive or difficult to make changes in subtractive manufacturing. The ease and rapid pace at which additive manufacturing can render designs has positioned it as a promising technology across the industrial sector. For example, about the potential of 3D printing and manufacturing. Factors such as faster innovation, reduced lead times and cost per parts have been cited as game-changers by respondents. 3D printing and manufacturing approaches at a glance,As with any technology-driven process, organizations tend to adopt additive manufacturing differently based on their business needs and the materials involved. Companies manufacturing products based on polymers and plastics may depend on what's known as according to TWI, for example. Firms that use ceramics and metals as raw materials might opt for an approach called binder jetting. Many of these approaches will also depend on the size of the object a 3D printer needs to create, as well as the number of hours or days that will be involved. If the ability to create prototypes quickly is the priority, an approach called material extrusion can be relatively inexpensive and efficient, . The transformative capabilities of additive manufacturing make it look like an area of emerging technology, but its history spans decades. A traced it to the late 1970s when it initially became known as rapid prototyping. Its growth has been driven in part by the sheer versatility of materials that can be subjected to the process, as well as the wide range of potential applications. Over the past 30 years, for instance, additive manufacturing has been used to produce everything from titanium parts for large aircraft to used in critical surgeries. 3D printing even allows manufacturers to produce items with different interior and exterior materials, which means they can be functionally graded. How 3D printing and manufacturing are evolving,Manufacturers may be showing an interest in 3D printing because it addresses some of their most common pain points. Take productivity: Printers can run day and night, without a great deal of human intervention. Industrial firms can also , while also giving themselves more runway to experiment and improve designs before they are finalized. Perhaps as a result, more than two-thirds (68%) of firms participating in a 2022 survey of engineering businesses said they in 2021 over the year before. Additive manufacturing can also help provide more efficient . Rather than needing a large warehouse with a massive inventory of parts that may only be used a handful of times, the parts can be created on demand, reducing waste and the cost of storage. Complementary technology for 3D printing,The long-term growth of 3D printing and manufacturing may be further enhanced through the addition of complementary technologies. Artificial intelligence (AI) and machine learning (ML) have been used to help determine the ideal parameters for and to during the printing process. Augmented reality (AR) can help provide a visualization of objects, and, to allow for better decision-making and provide training opportunities. AR can also help people better envision what 3D-printed designs will look like before they are put into full production. 3D printers could also be connected to sensors and other devices that compromise the Industrial Internet of Things (IIoT). Manufacturers focused on setting up smart factories based on the IIoT are often trying to , and additive manufacturing could further those efforts. The bandwidth, low latency, and real-time processing could also make it easier for manufacturers to connect remotely with multiple 3D printers. Innovating with Verizon,Manufacturers interested in exploring the benefits and use cases of additive manufacturing will benefit from finding a partner with the right technologies and experience. Verizon's 5G Ultra-Wideband is already , while students involved in the are also gaining access to 3D printers. can also offer manufacturers additional security benefits. Verizon has the advanced technologies and solutions to help manufacturers continue to innovate and keep their facilities connected, efficient, flexible and protected. Learn more about how Verizon can help unleash the next wave of . "," MIT Sloan School of Management, Cambridge, MA, December 7, 2017, accessed 18 May 2023. "," MIT News, Cambridge, MA, February 22, 2023, accessed 18 May 2023. Choose your country to view contact details. Manage your account or get tools and information. These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will not then work. These cookies do not store any personally identifiable information. These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. We use both third party and first party cookies for this purpose. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance. These cookies enable the website to provide enhanced functionality and personalisation. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly. These cookies may be set through our site by Verizon and third parties. They are used to present Verizon advertising on third party sites that you may visit. They do not store directly personal information, but are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising from Verizon. BackClear Filters,All Consent Allowed,
Learn moreMicrochip Technology Is Bringing Us Closer to AI Edge Computing Business
Microchip technology advancements are bringing us closer to AI edge computing,Author: Gary Hilson,The global artificial intelligence (AI) in 2022 according to Gartner. AI use cases extend from , and even . Yet despite the impressive market size, innovation has not been fully exploited because many objects using AI, such as drones, need to communicate with a remote server, increasing latency, security issues and other risks. AI edge technology aims to bridge that divide by bringing computing to the edge. AI computing requires large amounts of compute power and storage, making it impractical to place in the end device itself using traditional server components. Recently, researchers have , less than 4.5 millimeters across and weighing less than two ounces, that combine the central processing unit and random access memory. and microchips have helped to push AI to the device edge, opening up new opportunities for businesses to innovate in ways previously unthinkable. The AI edge not only leverages microchip technology to process data and even run applications, but it also helps reduce latency. There's also less of a need to transmit data back and forth from a central data center to process it and make the decisions. Software can make the edge smarter,While are necessary to bring AI to the edge, the technology is dependent on the software which allows the emulation of an intelligent system. Emergen Research estimated the reached $585.1 million in 2020 and will grow at a compound annual growth rate of 20.1% through to 2028. The evolution and growth of the edge and microchips are in response to diverse use cases that are, in part, enabled by other emerging and maturing technologies, such as 5G connectivity and the Internet of Things (IoT). For example:,Hardware advancements,With simple edge use cases demanding more intelligence and local processing capabilities, . For moving computation to the edge to be successful, it must address the performance and energy efficiency of AI edge capabilities if they're to be made widely accessible and cost-effective. One technology that's well suited for the task is an application-specific integrated circuit (ASIC) chip, which addresses the need for higher levels of processing power. The benefit of using an ASIC is that it's a customized device, so the design and packaging are optimized for each use case, which makes it ideal for unique requirements (including size constraints). ASICs offer strong power efficiency for high-performance applications, a valuable feature for more complex edge computing. The flexibility of ASICs allow for the use of multiple voltages and multiple thresholds to match the performance of critical regions to their timing constraints, and therefore minimize power consumption. An alternative to ASICs are Field-Programmable Gate Arrays (FPGAs), which are well-suited for because their hardware is also customizable, including high-performance and energy-efficient deep learning algorithms. Research from Arizona State University found that for edge computing, as they can deliver predictable performance and their hardware architecture can be adapted to provide consistent throughput. Meanwhile, Princeton researchers have also presented , which conducts computation and stores data in the same place through , reducing the energy and time used to exchange information with dedicated memory. In-memory computing combined with different types of memory is when it comes to solving AI challenges, including the development of neural networks that mimic the human brain while also being mindful of power consumption. Microchip technology advances enable autonomy,Global Market Insights predicted the is expected to grow from $8 billion in 2019 to more than $70 billion by 2026, driven by a surge in IoT devices with machine-learning capabilities and the development of smart cities. Companies that are deep into the development of microchip technology are predicting that AI applications will continue to be . Not only will IoT continue to drive many opportunities, but it will be more autonomous as the AI edge becomes increasingly populated by more intelligent industrial machines, drones and robots, while services for the public become more intuitive. The edge will benefit from advances in centralized high-performance computing as microchip technology trickles down over time and AI exerts greater influence on chip design and new players enter the market. Businesses can expect the AI edge to mature and evolve rapidly over time so that more can be done more quickly with less dependence on a central data center. As the ecosystem of software and hardware grows at the edge, so will the number of building blocks to create new opportunities for innovation. Most of all, the AI edge not only enables new services but also allows businesses to understand how existing services are functioning and how they can be improved. By embedding more hardware-based intelligence in the home, office, manufacturing facilities and entire communities, individual lessons learned can be applied more broadly to benefit the broader population. Learn more about how AI edge . Gartner, , Meghan Rimol, November 2021. Choose your country to view contact details. Manage your account or get tools and information. These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will not then work. These cookies do not store any personally identifiable information. These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. We use both third party and first party cookies for this purpose. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance. These cookies enable the website to provide enhanced functionality and personalisation. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly. These cookies may be set through our site by Verizon and third parties. They are used to present Verizon advertising on third party sites that you may visit. They do not store directly personal information, but are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising from Verizon. BackClear Filters,All Consent Allowed
Learn moreForest Fire Machine Learning: Enabling Faster Response Business
Forest fire machine learning: Enabling faster response and aiding forest fire prediction,Author: Gary Hilson,Traditionally, forest rangers have kept watch over vast, wooded landscapes from the high perches of fire towers and radioed authorities when they spotted smoke plumes above the treetops—however, continuous advancements in forest management, advancements that include forest fire machine learning (ML), have helped bring awareness and improved methods to the cause. Artificial intelligence (AI) for forest fire prediction plays a vital role in preparing for and responding to wildfires. A review of forest fire machine learning applications in wildfire science and management by the journal identifies the following key areas of study and problem domains:,Machine learning isn't just it is also helping . Data gathered by Internet of Things (IoT) sensors, drones, satellites and historical data maps are helping to create new fire detection methodologies powered by ML which are helping to aid in both forest fire prediction and with boots on the ground fire responses. The cost of forest fires,Forest fires can have a massive and lasting impact on communities, both in economic and social terms. For example, the 2018 California forest fires are estimated to have . The is the nation's support center for wildland fires and other emergency situations and it is composed of partners that work together to compile information about the current wildfire situation along with statistics that encompass lands managed by federal, state, local, tribal, and private agencies. Forests, shrubland and grassland cover more than in the U.S. These ecosystems are important environmental and economic resources. Increased temperatures and drought due to climate change , extent and severity of fires. have found that climate change has already caused an increase in wildfire season length, frequency and burned area. Wildfires are estimated to cause each year globally. As the impact of wildfires continues to rise, some public sector agencies are facing . Technological advancements in forest fire prediction have the potential to help solve both issues. How forest fire prediction works,goes far beyond simply stopping fires—it can also be vital in choosing where to begin and how to tackle fires, determining what vehicles and equipment should be needed, and how to best evacuate individuals in its path. To demonstrate the range of data available for forest fire prediction, consider the US Forest Service Predictive Services team :,Researchers working on predicting forest fires are that carry instruments with the ability to capture and measure useful data such as temperature, humidity, location, wind speed and direction. can also measure relevant metrics such as humidity, temperature, rainfall rates, soil moisture, wind speed and direction. This data is then aggregated to build models designed to determine where fire impacts are more likely to happen. According to the publication , AI for forest fire prediction has traditionally used neural networks and expert systems. But as computational limits are reduced and more data becomes available, it is now possible to use more recent machine learning methods—including deep learning and agent-based learning. Modern fire detection and the programs that drive them,Pilot programs are popping up all over the western half of the country to push the boundaries of modern fire detection, such as that uses cellular technology and rotating, high-definition cameras to snap 10 hi-def images of the surrounding landscape every 60 seconds to produce 360-degree views of up to 10 miles. Using a time-lapse video stream, this solution scours the landscapes for wisps of smoke or other potential signs of fires. is a modern solution that delivers 24/7 monitoring, real-time data streams and automated email and text alerts for critical incidents. These tools will help firefighters and first responders to discover and locate fires, monitor fire behavior, scale resources, and help with evacuations. The monitoring system will back up the county's existing network of wildfire detection cameras, which monitor the region around the clock, to not just help to spot wildfires but also to help predict how they will spread. County officials are happy with the upgraded system, although , such as distinguishing between planned/permitted burns and wildfires. In New Mexico, imagery from two U.S. weather satellites helped detect at least 6,000 fires. Images are searched for signs of smoke and hot spots, indicated by shifts in infrared data that could signal the outbreak of a fire. Several algorithms that ran reported that , analyzing factors such as:,If the algorithm detects a wildfire, it alerts the New Mexico Forestry Department, and the whole process from beginning to end takes approximately nine minutes. Fire and smoke maps,Much of the and smoke analysis performed today is . Drones have also helped to aid in the detection and mapping of wildfires. For example, have used thermal sensors to detect fires in early stages or in remote areas. Cameras are being used to help spot dry vegetation likely to be sparked by lightning and paired with AI, detailed wildfire maps can be created to help track and forecast fires. Forest fire machine learning technology,The rise of , , smart cameras, satellites, and more, has not just technology and fire experts converge, it has also created an enormous amount of data. have been key reasons for the enhancements in forest fire machine learning and modeling. are other relevant technologies that can help create simulations for real-time decision-making and could soon allow firefighters and other officials to in relation to the relevant landscape. While collecting data is crucial for forest fire machine learning purposes, quickly disseminating and assessing the data, for instance with , will be crucial to enabling rapid response, helping first responders to combat fires and evacuate those in the path of danger. Advances in connectivity, like the expansion of , will continue to power solutions that make it easier for public safety officials to do their primary job: saving lives. Learn more about how . Choose your country to view contact details. Manage your account or get tools and information. These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will not then work. These cookies do not store any personally identifiable information. These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. We use both third party and first party cookies for this purpose. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance. These cookies enable the website to provide enhanced functionality and personalisation. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly. These cookies may be set through our site by Verizon and third parties. They are used to present Verizon advertising on third party sites that you may visit. They do not store directly personal information, but are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising from Verizon. BackClear Filters,All Consent Allowed
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