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Companies that fail to optimize their plants for Industry 4.0 implementation will not survive in the near future. To move into Industry 4.0, companies must have Industry 3.0 fully implemented and optimized. Computer technology, electronic systems, and automation became the dominant force in the industrial market, which was the catalyst for Industry 3.0. Old factories became automated and shifted from analog and mechanical systems to electrical and digital strategies—industry 3.0 automated processes on the production line, replacing blue-collar jobs with white-collar jobs.
Leading the pack, companies capture all the benefits across the entire manufacturing value chain, increasing production capacity and reducing material losses, improving customer service and superior delivery lead times, achieving higher employee satisfaction, and reducing their environmental impact.
Across a wide range of industry sectors, it is common to see (30 to 50 percent) reductions in machine downtime, (10 to 30 percent) increases in throughput, (15 to 30 percent) improvements in labor productivity, and (80 to 85 percent) more accurate forecasting.
Advanced robotics, a combination of sophisticated programming and powerful hardware, uses smart sensor technology (including ultrasonic, touch, and light sensors) to interact with the real world around them.
Industry 4.0 has given new life to #robotic technology. A new generation of advanced robotics is emerging, capable of performing difficult and delicate tasks. Powered by cutting-edge software and sensors, robots can recognize, analyze and act upon the information they receive from the environment and even collaborate and learn from humans.
("Cobots"), or collaborative robots are designed to collaborate with humans intelligently and safely and are set to become a crucial part of industry 4.0. For humans, tribal knowledge transfer of a task, for example, from one person to another, usually results in learning the best way of performing the task. The rest is unconscious know-how that's not easily transferable.
In comparison, standard #robots can be programmed to stop when a human enters the robot's facility. This leads to unwanted delays in operation or production, which can be avoided using ("Cobots") with added safety for humans.
#Advanced #Robotics and #Digital #Manufacturing.
The digital twin concept optimizes the performance and maintenance of industrial systems. Global research firm, Gartner, predicts that by 2021, 50% of large industrial companies will be using digital twins to monitor and control their assets and processes.
A digital twin is a digital representation of a real-world product, machine, process, or system. It allows companies to understand better, analyze and optimize their operations through real-time simulation.
Digital twins can be confused with simulation used in engineering. However, unlike engineering simulations, a digital twin runs an online simulation based on data received from sensors connected to a machine or other device.
As an #IoT device sends data almost in real-time, a digital twin can collect this data continuously, maintaining its fidelity with the original throughout the lifespan of the product or system. This enables the digital twin to predict potential issues so preventive measures can be taken. For example, an operator can use a digital twin to identify why a part is malfunctioning (such as leaking) or to predict the lifetime of a product.
#Advanced #Robotics and #Digital #Manufacturing.
Despite its uptake in consumer applications, the manufacturing industry is now beginning to explore the benefits of Augmented Reality (AR) technology. There is a vast untapped potential for the technology to help with assembly processes or help to maintain manufacturing equipment.
Augmented reality bridges the gap between the digital and physical worlds by overlaying virtual images or data onto a physical object. The technology uses AR-capable devices, such as smartphones, tablets, and smart glasses.
An example: A surgeon using AR glasses during a surgical operation. The glasses could overlay data from a patient's MRI and CT scans, such as nerves, major blood vessels, and ducts, onto the patient and highlight them in color. This helps the surgeon find the safest path into the region that needs invasion, minimizing the risk of complications and improving the surgeon's precision.
AR could enable MFG workers to speed up the assembly process and improve decision-making. In addition, AR glasses could be used to project data, such as layouts, assembly guidelines, sites of possible malfunction, or a serial number of components, on the genuine part, facilitating faster and easier work procedures.
#Advanced #Robotics and #Digital #Manufacturing.
With new digital technologies emerging, now is an exciting time for the manufacturing industry. The wave of new technologies opens up opportunities for companies to take steps towards greater flexibility, sustainability, and productivity. Industry 4.0 also facilitates new ways for humans and machines to work together, empowering businesses to achieve more significant insights, reduce error risk, and make better decisions.
Ultimately, #Industry #4.0 is set to take root across the manufacturing ecosystem. By understanding and harnessing the technologies driving Industry 4.0, manufacturers will stay on the cutting edge of this new digital era.
You can review by using the links below to read the prior posts for complete understanding.
Industrial Internet of Things (IoT) https://lnkd.in/gCtcSKrn
Big Data Analytics & Internet of Things (IoT) https://lnkd.in/g9sFmjiM
Cloud Computing https://lnkd.in/gKGzzjM8
Additive Manufacturing https://lnkd.in/gVRkUnEb
Advanced Robotics https://lnkd.in/gYAfPVwf
Digital Twins https://lnkd.in/gcJY68NG
Augmented Reality https://lnkd.in/gqkAe4Jc
#Advanced #Robotics and #Digital #Manufacturing.
Robotics and artificial intelligence (AI) serve very different purposes. Some wonder if robotics is a subset of artificial intelligence. Others wonder if they are the same thing. Unfortunately, the two fields are almost entirely separate.
Robotics with AI capability made it possible to involve perception-based decision-making, which was impossible with rule-based robot algorithms. Predictive maintenance is another crucial aspect where AI finds significant application. Predictive maintenance enables the capability to determine the performance, breakdown, and operating conditions of equipment or machine on a real-time basis.
Artificial intelligence (AI) in the manufacturing market size is expected to grow from USD 272.5 million in 2016 to USD 4,882.9 million by 2023, at a CAGR of 52.42% during the forecast period.
The growing usage of big data technology, industrial IoT in manufacturing, extensive usage of robotics in manufacturing, computer vision technology in manufacturing, cross-industry partnerships and collaborations, and a significant increase in venture capital investments will propel the growth of the AI in manufacturing market.
#Advanced #Robotics and #Digital #Manufacturing.
The next item that makes a typical manufacturing plant a smart manufacturing facility is the implementation of industrial robots. Industrial robots are not a new concept, and it has been in the systems for the last 40-50 years. What changed concerning industrial robots is that they have become intelligent. Earlier, the robots were programmed to do one single task at a time. If you want to do other types of functions, then you must change the codes.
Now robots are connected with the sensor network implemented within the manufacturing shop floor and receive data from sensors and change their action accordingly. Artificial intelligence is also being slowly implemented in robotics systems, making systems autonomous. Through AI, robotics systems are expected to change their activities according to the situation on a real-time basis.
The majority of industrial robots are implemented in the Asia-Pacific region. For example, industrial robots play a significant role in the automotive industry. The government initiative is considered one of the critical drivers for developing and growing robotics. The US and China are actively providing all the necessary incentives to further drive the demand for robotics.
#Advanced #Robotics and #Digital #Manufacturing.
Apart from #industrial #robots, there is a new type of robot rising called a collaborative robot. These machines will work alongside humans to support all the work done by humans. For example, a collaborative robot can observe what a human operator at an assembly line is doing, learn the human's task, and autonomously start performing that same task with precision. Further, the development of collaborative robots has reached such an extent that it would be difficult to differentiate them from industrial robots concerning their application. Collaborative robots that were supposed to do only light work are now capable enough to complete heavier jobs generally done only by industrial robots.
The industrial robot market is expected to be worth USD 71.72 billion by 2023, growing at a CAGR of 9.60% during the forecast period.
The collaborative robot market is expected to be worth USD 4.28 billion by 2023, growing at a CAGR of 56.94% between 2017 and 2023.
The main drivers for the industrial robotics market are increasing investments in automation in various industries and the growing demand from small and medium-scale enterprises (SMEs) in developing countries.
#Advanced #Robotics and #Digital #Manufacturing.
#Robotics #Used in #Assembly #Line
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