Billion of people use social media globally, and it has become fast clear that one of the technologies defining our generation. Facebook states to have 2.38 billion monthly active users and 1.56 billion daily active users (Facebook, 2019). With 42.3% of the global population accounted for, the total number of social media users estimated to reach 3.29 billion in 2022 eMarketer, 2018. Given the enormous potential audience accessible who spends many hours a day on social media, it is not surprising that marketing teams of different companies have embraced social media as a marketing tool. One such very popular social media marketing strategy is influencer marketing. Under this type of marketing, "influencers—people with a sizable following on social media platforms—endorse a company's goods or services to their following. From a market worth of $1.7 billion in 2016 to $13.8 billion in 2021, influencer marketing has demonstrated amazing expansion over the previous six years (Maa...
Rich in possibilities for development are human-robot interaction, adaption, learning, manipulation, autonomy, mobility, agility, dexterity, and perception. Some industrial robots used today move without knowledge of their surroundings, therefore their vicinities endanger humans and prevent their usage as cobots, or aides to human workers. 28, Furthermore, as human colleagues are naturally erratic, new artificial intelligence innovations are required to enable robots to predict human behavior and thereby enhance professional safety. Encourage safe and effective human-robot interactions as well as the evolution of new technologies and standards allowing more acceptance of robotics in advanced production environments. infrastructure for artificial intelligence. Key enabers of IIoT will be the convergence of cloud computing, data analytics, computational modeling with artificial intelligence (AI), allowing individual firms to derive specific guidance from the combined expertise of every company. Future manufacturing systems could benefit from all of the historical knowledge acquired from production experiences from such systems in the nation thanks to machine learning. Massive datasets are required for the machine learning techniques applied in mining this enormous reservoir of manufacturing knowledge. research, new standardizing initiatives are required to guarantee the repeatability and dependability of production parts as the manufacturing capacity of AM grows. While others are proven leaders in low-cost, high-volume, low-performance manufacturing with great experience in lowering production costs, others maintain their goods on the forefront of
As more data becomes available they also get more potent
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So data curation and access become essential enablers of machine learning and artificial intelligence applications. Companies will only provide their manufacturing data, though, only if private data can be located and maintained under security.While preserving data security and honoring intellectual property rights, develop new criteria for artificial intelligence and find best practices to offer uniform availability, accessibility, and usability of manufacturing data inside and across industries. Give R&D top priority in order for American firms to create fresh ideas on data access, secrecy, encryption, and risk analysis. Manufacturing Cybersecurity: The U.S. manufacturing industry gets more exposed to data piracy and hostile actors as intelligent manufacturing develops. Target for both state- and competitor-sponsored eavesdropping is the manufacturing industry, which appeals especially. Data can be taken, but it can also be altered and used to produce defective goods, therefore upsetting systems and leading to failure. Improving cybersecurity comes first nationally. With such activities as improved authentication, updated security patches, and risk management for cloud computing, traditional cybersecurity solutions and efforts center on protecting information technology (IT)-based systems. The demand to precisely grasp the variations in vulnerabilities between IT and OT systems complicates cybersecurity in manufacturing companies. Usually unable to be updated on demand, manufacturing systems and their integrated control systems are OT systems directly affecting the physical world and usually cannot be safeguarded by just using newer IT approaches.Developing and/or updating standards and guidelines 30 for implementing emerging technologies for cybersecurity in manufacturing systems, including AI forthreat detection and handling, blockchain for security of sensitive manufacturing casting, or forging techniques, AM generates a new design paradigm. To keep competitive, designers have to learn how to include AM technology into their next systems. Supported by basic research, new standardizing initiatives are required to guarantee the repeatability and dependability of production parts as the manufacturing capacity of AM grows. While others are proven leaders in low-cost, high-volume, low-performance manufacturing with great experience in lowering production costs, others maintain their goods on the forefront of.
Information, and security of IIoT devices when deployed
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In smart manufacturing systems calls for new research efforts. lately created quantum devices have proved their ability to readily compromise accepted security measures. Consequently, fresh approaches in both conventional and quantum areas of cyber security are required. Create standards, tools, and testbeds and distribute policies for applying cybersecurity in smart manufacturing systems. Direct efforts toward improved cybersecurity for American manufacturers. Create World-Leading Processing Technologies and Materials With uses in many industrial sectors including defense, energy, transportation, aerospace, and healthcare, advanced materials are indispensable for the creation of new goods as well as for economic and national security. Sadly, it can take 20 or more years to translate materials discovery into the market. The concept of advanced materials relies on the intended use for the materials since material characteristics define performance. Advanced materials might comprise, for instance, extreme-temperature composites used in hypersonics, energetic materials, high-strength lightweight metal alloys, synthetic biologic materials, anti-corrosion membranes for advanced filtration systems, ultra-high temperature structures for more efficient turbines in power generation, and many others. By substituting faster, more efficient, exact, and strong technologies for current methods, advanced procedures for shaping and improving the performance of these materials can raise the cost-effectiveness and competitiveness of whole industries. Chemical and thermal process intensification, sophisticated remanufacturing and recycling technologies, and atomically precise manufacturing are among the advanced processing methods under research or on their way to provide possible breakthroughs. This information transfer will be facilitated by potent new approaches for material behavior prediction utilizing high-performance computation. Transposing to practice new computational techniques, such those being developed in the Materials Genome Initiative, calls for more emphasis.31 By computing the expected characteristics of advanced materials systems in processing and in service, those techniques reduce the time-consuming and expensive testing now required to design novel materials.
High-performance materials additive manufacturing and essential
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Materials rank technically as the priorities for this goal. High-performance materials. Significant performance improvements in military, energy, transportation, and other industries could come from the discovery and development of lightweight, modern metals, composites, and other kinds of new materials. Unrealized chances for cross-sectoral knowledge transfer abound. Many American high technology enterprises make use of costly materials and techniques of processing.Encourage a materials genome and systems-level computational approach to material design, optimization, and implementation to dramatically save design time and expense in finding, developing, qualifying, and scaling production of high-performance materials.additive manufacturing. With regard to both cost per part and system performance, additive manufacturing (AM)—the ability to directly create structures using three-dimensional (3D) printing and related technologies—is now starting to realize its revolutionary potential to impact the commercial and defense manufacturing sectors. For the aerospace industry, for instance, AM of monolithic, high-performance metal parts can offer tremendous weight reductions and performance increases. In a same vein, printing biological cells seems to generate future human organs and tissues. But manufacturing dependable and safe parts could need for the exact and repeatable printing of millions of metal powder or living cells, and this level of accuracy is not currently readily reached.Adoption of AM into manufacturing sectors depends on the capacity to dependably define processing parameters that result in consistent and repeatable production across many machines and across multiple sites, hence needing machine/process standardizing and trustworthy component material quality. Since parts may be produced free from the limitations of conventional machining, performance. Transfer of knowledge between high-tech and high-volume non-competitors can reduce the cost of high-performance items and improve the performance of low-cost products, so generating major advantages in all spheres.
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