With the actual equipment, there are various kinds of equipment conditions and driving conditions for electric actuators and cylinders and thus, it is very difficult to explain all the operating conditions.
Operators will soon be able to test and optimize the machine settings for the next product in line in the virtual world before they make the physical change-over.
We have demonstrated the ability to electrospin liquid CHS into silicon nanowires that when blended with carbon lead to performance comparable to that achieved by CVD grown silicon nanowires but at reduced cost and simplified scaling.
Nick Butler, National Instruments for ControlDesign: Data is the heart of all Internet of Things systems, including systems deployed into industrial environments. When we talk about making the aging electrical grid smarter or the factory of the future more efficient, what we’re really after are insights that can make our equipment and infrastructure smarter and more efficient. And to deliver these incredibly valuable insights, which will result in millions of dollars in savings, uptime or operational efficiency, we need data. Lots of it. We also need complex, computationally intensive algorithms that scour the data to find trends, patterns and anomalies (Figure 1). While these algorithms and analysis routines are a very important piece of the IIoT puzzle, the best data scientists in the world cannot predict equipment failures without enormous amounts of data. Cont'd...
This cannot be a simple extension of today's processes, but needs to be a complete rethink of how manufacturing and production systems are designed to take full advantage of the cloud and the analytics that it brings to bear.
All these companies are logistics and material handling vendors and times are changing in logistics and fulfillment
Kagan Pittman for Engineering.com: Will the Internet of Things be the future of manufacturing? Global conglomerate Hitachi Group seems to think so. Hitachi recently partnered with Mitsubishi Electric and Intel to receive approval for their Factory Automation Platform as a Service (FA PaaS) Testbed at the Industrial Internet Consortium (IIC), a global non-profit organization. The testbed will operate as a testing platform based on the reference model of IIC to test solutions in controlled scenarios that match real-world conditions, for the ultimate purpose of connecting manufacturing sites with head offices in order to streamline their operations. Hitachi hopes to use the FA PaaS to respond to what they see as a market rapidly growing and demanding faster product development, market introduction, quality improvements and shorter lead times. Cont'd...
The Tesla factory in Fremont, California, and the Gigafactory in Nevada are monuments to science and progress.
Eric Emin Wood for IT World Canada: Manufacturing companies with visions of incorporating the latest automated, cloud-based, analytical tech into their production process need to recognize the value of a measured approach, an original equipment manufacturing (OEM) veteran says. Martin Stephenson, vice president of process automation for OEM Schneider Electric Canada, which specializes in power management, building management, datacentres, and process and automation control, says that while some firms are equipped to embrace the change right away, others might find that implementing what he calls “Industry 4.0” isn’t a good fit for them at all. “Just because you can, doesn’t mean you should,” he says. “Customers need to have a truthful conversation with themselves and say, ‘How do we manage what we do now? Are we ready for this step? … Do we have the right infrastructure? Do we have the right cybersecurity in place?’ There are a lot of discussions to be had before this leap of faith happens.” Cont'd...
Louis Columbus for Forbes: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production. Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months. The ten ways machine learning is revolutionizing manufacturing include the following:
Microscan Visionscape® GigE Camera Performs Precision Measurement of Apertures in Digital Camera Housing at Foxconn
his case study from the electronics manufacturing industry could be applied to other sectors, and as vision technology advances and experience from its practical application is accumulated, there are sure to be even more challenges addressed using precision machine vision solutions.
Ian Vallely for Works Management: There isn't enough understanding of Industry 4.0 by UK manufacturers, according to a report by BDO in partnership with the Institution of Mechanical Engineers. It said just 8% of UK manufacturers have a significant understanding of Industry 4.0 processes despite 59% recognising that the fourth industrial revolution will have a big impact on the sector, according to the report . As the increasing use of automation, data exchange, technology and wider supply chain communications driven by Industry 4.0 provides both huge opportunities and threats to UK manufacturing, there remains a ‘gaping hole’ in the education and understanding of Industry 4.0. According to the BDO Industry 4 0 Report, increased productivity, better data analysis, increased competitiveness and lower manufacturing costs are the top ways in which Industry 4.0 will affect UK manufacturing. Cont'd...
With an anticipated skills gap of 2 million jobs by 2025, the manufacturing industry needs to attract and inspire the next-generation workforce,
Evan Gough for UniverseToday: Astronauts aboard the International Space Station have manufactured their first tool using the 3D printer on board the station. This is another step in the ongoing process of testing and using additive manufacturing in space. The ability to build tools and replacement parts at the station is something NASA has been pursuing keenly. The first tool printed was a simple wrench. This may not sound like ground-breaking stuff, unless you’ve ever been in the middle of a project only to find you’re missing a simple tool. A missing tool can stop any project in its tracks, and change everybody’s plans. The benefits of manufacturing needed items in space are obvious. Up until now, every single item needed on the ISS had to be sent up via re-supply ship. That’s not a quick turnaround. Now, if a tool is lost or destroyed during normal use, a replacement can be quickly manufactured on-site. Cont'd...
Freed from the isolation of the real-time system and from other functions such as the user interface, OEMs are able to explore more innovative solutions with less risk and overhead.
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