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Digital Technologies Expected to Dominate Manufacturing Trends in 2020
Although most manufacturers are cautiously optimistic about 2020, concerns linger about the potential impacts of trade volatility, tariffs and the global manufacturing slowdown.
The best way to prepare for these potential threats is to optimize the manufacturing process by increasing efficiencies, reducing operational costs and speeding up time to market.
The automobile company Mini Cooper capitalizes on this tactic by using customer studies to add high-demand features and provide more than 800 varieties of its vehicles that can be configured and ordered online.
A greater variety of performance data can be used to create smarter systems and models for all aspects of an operation, including supply chain management.
AI and machine learning are vital for optimizing speed, scale and convenience—for example, eliminating the need to assign entire groups of workers to designated tasks.
“With these systems, it is possible to reduce significantly the time needed to learn manual operation skills, reducing the time invested in jobs that could last up to several years to a mere fraction,”
With the growing innovations in AI and machine learning, sensor technologies, connectivity and data analytics, it is now possible to monitor equipment in real time and analyze key performance metrics.
When even the slightest variances are observed, adjustments can be made that help prevent untimely equipment breakdowns, saving valuable time, resources and money.
Industrial wearables IndustryWeek predicts that the most effective workers of the next decade will utilize wearable tools and equipment such as smart glasses and biometric sensors that connect them to not only work instructions or critical data, but to each other to create the, “Industrial Internet of People.”
Sarcos Robotics has developed an exoskeleton that give wearers a 20-to-1 strength amplification—to a person wearing this robot and lifting 100 pounds, the load should only feel like five pounds.
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Machine Learning in Manufacturing – Present and Future Use-Cases
Major companies including GE, Siemens, Intel, Funac, Kuka, Bosch, NVIDIA and Microsoft are all making significant investments in machine learning-powered approaches to improve all aspects of manufacturing.
The technology is being used to bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed.
This article will focus on how four of the leading companies in the world of manufacturing are using cutting edge AI to make interesting improvements to factories and robotics.
It will focus on two main themes: From what our research suggests, most of the major companies making the machine learning tools for manufacturing are also using the same tools in their own manufacturing.
Norbert Gaus, Head of Research in Digitalization and Automation at Siemens Corporate Technology, “our AI system was able to reduce emissions by an additional ten to fifteen percent.” Siemens latest gas turbines have over 500 sensors that continuously temperature, pressure, stress, and other variables.
Siemens claims their system is learning how to continuously adjust fuel valves to create the optimal conditions for combustion based on specific weather conditions and the current state of the equipment.
By companies having a full understanding of all resources available and a highly adaptable robots the goal is to eventually make manufactures providing mass customization possible.
General Electric is the 31st largest company in the world by revenue and one of the largest and most diverse manufacturers on the planet, making everything from large industrial equipment to home appliances.
For example, according to GE their system result in, their wind generator factory in Vietnam increasing productivity by 5 percent and its jet engine factory in Muskegon had a 25 percent better on-time delivery rate.
In the video below, GE explains how it’s Brilliant Factory technology is being used at its Grove City, PA factory: While GE and Siemens are heavily focused on applying AI to create a holistic manufacturing process, other companies that specialize in industrial robotics are focusing on making robots smarter.
The video below, shows how a FUNAC robot autonomously learns to pick up iron cylinders positioned at random angles: KUKA, the Chinese-owned German manufacturing company, is one of the world largest manufacturers of industrial robots in the world.
Most industrial robots were very strong and stupid, which meant getting near them while they worked was a major health hazard requiring safety barriers between people and machines.
KUKA claims their LBR iiwa “is the world’s first series-produced sensitive, and therefore HRC-compatible, robot.” Its use of intelligent control technology and high-performance sensors means it can work right beside a human without the risk of accidentally crushing a person.
The ability to work safely with humans may means mobile robots will be able to deployed in places and functions they haven’t been before, such as working directly with humans to position components.
Greater industrial connectivity, more widely deployed sensors, more powerful analytics, and improved robots are all able to squeeze out noticeable but modest improvements in efficiency or flexibility.
We are seeing these newer applications of machine learning produce relatively modest reductions in equipment failures, better on-time deliveries, slight improvements in equipment, and faster training times in the competitive world of industrial robotics.
Customization is rare and expensive while high-volume, mass produced goods are the dominant model in manufacturing, since currently the cost of redesigning a factory line for new products is often excessive.
- On 19. juni 2021
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