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Tesla wants its factory workers to wear futuristic augmented reality glasses on the assembly line

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  • Tesla patent filings reveal plans for augmented reality glasses to assist with manufacturing.
  • Factory employees has previously used Google Glass in its factory as recently as 2016.

Tesla‘s Model 3 might have “biblical simplicity” according to one Wall Street analyst, but building any car still involves hundreds of nuts, bolts, and welds.

To cut down on the number of fit and finish issues — like the “significant inconsistencies” found by UBS— Tesla employees on the assembly line could soon use augmented reality glasses similar to Google Glass to help with car production, according to new patent filings.

Last week, Tesla filed two augmented reality patents that outline a futuristic vision for the relationship between humans and robots when it comes to manufacturing. The “smart glasses” would double as safety glasses, and would help workers identify places for joints, spot welds, and more, the filings say.

Here’s how it works:

Tesla/USPTO

And here’s the specific technical jargon outlining the invention (emphasis ours):

The AR device captures a live view of an object of interest, for example, a view of one or more automotive parts. The AR device determines the location of the device as well as the location and type of the object of interest. For example, the AR device identifies that the object of interest is a right hand front shock tower of a vehicle. The AR device then overlays data corresponding to features of the object of interest, such as mechanical joints, interfaces with other parts, thickness of e-coating, etc. on top of the view of the object of interest. Examples of the joint features include spot welds, self-pierced rivets, laser welds, structural adhesive, and sealers, among others. As the user moves around the object, the view of the object from the perspective of the AR device and the overlaid data of the detected features adjust accordingly.

As Electrek points out, Tesla has previously been employing Google Glass Enterprise as early as 2016, though it’s not clear how long it was in use.

Tesla has a tricky relationship with robotics in its factory. In April, CEO Elon Musk admitted its Fremont, California factory had relied too heavily on automated processes. Those comments, to CBS This Morning, came after criticism from a Bernstein analyst who said “We believe Tesla has been too ambitious with automation on the Model 3 line.”

Still, the company seems to be hoping for a more harmonious relationship between human and machine this time around.

“Applying computer vision and augmented reality tools to the manufacturing process can significantly increase the speed and efficiency related to manufacturing and in particular to the manufacturing of automobile parts and vehicles,” the patent application reads.

 

This article was originally published on Business Insider. Copyright 2018.

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Manufacturing

What you need to know if you’re attending AVEVA World Summit

AVEVA World Summit is where the most innovative industrial executives from around the world gather for an exclusive opportunity to network with 400 global digital leaders across diverse sectors. 

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AVEVA World Summit is where the most innovative industrial executives from around the world gather for an exclusive opportunity to network with 400 global digital leaders across diverse sectors. 

The summit is an opportunity to discover how these leaders and Cognizant — a Platinum Sponsor — are transforming the entire asset and operational lifecycle.

To help you prepare, here is a selection of articles, case studies, ebooks, and clips from Cognizant, discussing digital transformation:

  • Learn Cognizant’s 4 key success factors to Industry 4.0 transformation. For starters, lead with strategy, not the technology. Watch the video here.
  • AI, Machine Learning, and IoT are ensuring the efficacy and efficiency of one of the most demanding engineering projects in the world. Learn how Cognizant helped Norwegian offshore engineering firm Kvaerner adopt these digital technologies.
  • The promise of Industry 4.0 is compelling, but for many traditional manufacturers, the reality is less than ideal. In this new Cognizant report, find examples of manufacturers that are navigating the shift.
  • It’s all about speed: Insight from Cognizant on how 5G will transform the business sector and create a leadership race for data intelligence.
  • Not all smart factories are created equal: Cognizant takes stock of the state of IoT intelligence, and what industrial organizations need to ensure both digital maturity and success. Read more here.
  • The many touchpoints of IoT connectivity allows AI to really shine, and prove its value to manufacturers in the form of proactive preventive maintenance, says James Jeude, VP in Cognizant’s Digital AI & Analytics Strategic Consulting Group, in this piece.
  • The human factor in IoT intelligence is key: Connected employees can “dynamically manage situations as they change,” explains Cognizant’s AVP of engineering and IOT solutions Phanibhushan Sistu.
  • The leap to IoT is a necessary one for your organization. This Cognizant ebook looks at 14 such businesses that jumped confidently into the digital future.
  • It took less than 12 weeks for Cognizant to implement an IIoT platform for a leading global industrial manufacturer. Get the case study here.
  • Without the duo of IoT and the Digital Twin, your organization is living in a black-and-white outlined world, in terms of operational intelligence. Color it in, get more accurate predictions, and fully realize potential.

Aveva World Summit takes place September 16-18, at Marina Bay Sands, Singapore.  

One session to highlight? “Digital Transformation in Hybrid Industries,” featuring Cognizant’s VP of IoT and Engineering services, Frank Antonysamy. This session will examine the benefits of digital transformation, and addressing challenges through a sustainable platform that can adopt best practices, continuous improvements, and grow with the business.

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IoT + Digital Twin = Operations Intelligence: An Equation that Delivers Useful What-If Scenarios

In the equation IoT + X = Operations Intelligence, what role does a digital twin play as the X factor?

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Measure twice, cut once. This basic premise, that it would be advantageous to visualize outcomes before you act, forms the backbone of an entity known as the digital twin. This approach is particularly useful in today’s high-stakes industries such as manufacturing, construction, mining and more. Measuring twice and cutting once allows enterprises to tweak every aspect of the production process to maximize revenue.

The digital twin enables companies to envision what-if scenarios for various operating conditions in the virtual world before it affects processes in the real world. The more fully the digital twin avatar is fleshed out, the more accurate its predictions. This means enterprises need IoT (the Internet of Things) to color in the picture completely. IoT helps the digital twin realize its full potential to deliver operational intelligence. 

The promise of the digital twin

A digital twin is a replica, described by data, of physical assets, processes and systems that helps organizations understand, dissect, predict and optimize their performance. It can combine design and engineering details with operating data and analytics about anything from a single part to multiple interconnected systems to an entire manufacturing plant.

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

If you need to describe a physical asset (say a motor running on a shop floor) through data, you need that motor to both generate data and make that data easily accessible. This is where IoT falls into the picture: it “sensorizes” a variety of physical machines and brings them into the digital twin conversations, says Vivek Diwanji, senior director of technology at Cognizant. IoT-enabled embedded devices can then transmit data about their health under a variety of operating conditions and channelize that information through an Internet connection from shop floor to enterprise resource planning (ERP) software. 

Layered possibilities

A digital twin is about different perspectives – essentially comprised of many layers that are progressively overlaid with more detailed data input. The level of detail depends on the insights you’re looking to derive. If you need to know when a vehicle tire is going to wear-out, all you need to measure is temperature and air pressure. Long-term durability intelligence on the other hand, also needs to measure ambient conditions, daily operation numbers, road type and more.

[Download]: A New Approach to PLM

A lack of common IoT standards across industries makes the data difficult to gather, but that conversation might change with the advent of 5G, Diwanji predicts. For now, digital twin is a powerful tool that enables companies to deliver field services, conduct smart operations and evaluate product development outcomes before investing millions into the pipeline.

By 2020, 30% of Global 2000 companies will be using data from digital twins to improve product innovation success rates and organizational productivity, according to IDC. They can realize gains of close to 25%. And IoT is a key player in that equation to deliver such operational intelligence.

“Digital twin is an application that leverages IoT. The very definition of a digital twin necessitates that a digital model is running in conjunction with a physical model. That connection, between the physical and the digital, happens through IoT,” Diwanji says. “IoT is really the backbone of the digital twin.”

[Download]: Real Estate Manager Goes Digital

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IoT + AI = Operations Intelligence: A new equation for a new world of data

In the equation IoT + X = Operations Intelligence, what role does artificial intelligence play as the X factor?

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A new world powered by the Internet of Things (IoT) demands a new computing paradigm and yesterday’s if-X-then-Y rules-based approach can’t handle today’s industrial complexities. 

IoT-embedded devices generate large amounts of data, but analyzing that volume of information using traditional algorithms can be overwhelming, like drinking water from a fire hose. We need more intelligent computing that can take on both volume and ambiguity — in context and in real time. Artificial Intelligence can be one of the special X factors in the equation, IoT + X = Intelligence, and it learns by example rather than by rules.

The IoT-AI dance

At first glance, it might appear that a rules-based process might work well enough for a solitary IoT-enabled device: monitor the temperature profile of a motor; if it overheats beyond a preset limit, turn it off. However, the real world in which such machines operate is much more complex and you need to parse interdependent signals at the edges to truly make sense of the data being fed to you. For example, air temperature, paint temperature, and humidity in combination may lead to warranty claims in complex combinations that exceed our ability to use traditional data science. AI can help. 

In that sense, AI is IoT’s ally. It tolerates ambiguity at the margins, meaning analysts don’t have to tie up precious capital resources just cleaning and formatting data so it can play well with existing algorithms. While it is popular to declare that the “garbage in, garbage out” theory holds true in data analytics, the good news is that AI can detect outliers and tolerate bad data up to a point, says James Jeude, Vice-President in Cognizant’s Digital AI & Analytics Strategic Consulting Group. “I believe that AI takes our best human thinking and allows us to duplicate it at scale and at low cost and push it into all corners,” he adds

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

IoT’s myriad touchpoints allow AI to prove its value, says Jeude. One of the many use cases of AI is proactive preventive maintenance. If you were to outfit every grocery store refrigerator with IoT sensors that measured current flow and temperature, AI could proactively predict compressor failure, delivering intelligence that can be acted on and saving revenues in the long run., catching failures well before the actual temperature rises and triggers an alarm.

“Humans have a limited ability to process complexity. IoT and AI are absolutely essential together to deal with that complexity challenge,” Jeude says.

Evolutionary AI

Previous iterations of AI founded on deep learning involved training algorithms on vast banks of test cases so the machines would lean on learned experiences to make informed decisions. Such AI is time and resource-intensive and doesn’t allow for flexibility at the edges. 

Evolutionary AI, on the other hand, allows for economical testing of corner cases. It factors in historical context, decision and output data before prescribing actions. “We can use evolutionary AI to drive iterations and pick the ones that are the winners and help us prune the losers,” Jeude says.

[Download]: Real Estate Manager Goes Digital

The very fact that IoT combined with AI creates intelligence is predicated on the fact that the cost of computing has decreased significantly. Equally important, Jeude points out, is that the ability to put decisions into effect has also become cheaper. Both have fallen by an order of magnitude every decade. “That IoT device can shut off a machine, call for repairs, flash warning lights, for a fraction of the cost,” he says.

IoT with AI delivers intelligence by processing volumes of data in real time and in context at complex scales humans can’t work with. With IoT and AI we are well-informed to make critical decisions right at the moment when they are needed the most. In today’s high-stakes digital landscape, that can make all the difference. Whether you’re working in retail, entertainment, manufacturing, finance, mining or countless other industries, IoT in concert with AI can deliver the transformational intelligence you need at scale.

[Download]: A New Approach to PLM

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