Autonomous Vehicle Technology for Your Wire and Cable Factory

Self-driving cars have received a lot of press in the last few years. It makes sense, the promise of cars that drive themselves is easy for anyone to understand and is a convenience most of us would enjoy. It has been predicted for decades, but recent technology advancements have improved the feasibility and many experts agree we are only a few years away from the completion of this monumental challenge. In fact, in most new cars there are already convenience and safety features that come from the self-driving technology suite.

Among the technological advances most responsible for the advancements are lower cost cameras and computers (from automotive and consumer electronics) and neural net programming techniques that have allowed object and sequence recognition to skyrocket in accuracy. While these tools are helping vehicles trying to drive themselves, they can also be used in a variety of other fields. The same building blocks are supporting cashierless retail stores, intelligent security systems, and many other applications where vision systems use recognition and knowledge of context to gather information.

Autonomous vehicle and consumer cell phones have dramatically increased the computer vision field

Manufacturing has countless opportunities for visual supervision and eventually complete computer guidance of machines. Many of the same science fiction stories that predict autonomous cars also predict unmanned factories. Complete lights out factories will likely take far longer than self-driving cars, but just as automatic lane following and adaptive cruise control are preceding full self-driving vehicles, there are manufacturing tools ready today to help all kinds of factories improve quality and operations through the use of new technology.

In manufacturing the tricky questions are where to start? And can you generalize your technology enough to allow different factories to benefit from the same products? Overview has spent time working through these questions and have decided to focus our effort on the Wire and Cable industry.

Why Wire and Cable?

First of all, it is exciting to try and help an industry which is creating crucial part of modern infrastructure. Wire and Cable is also a good choice because it has a lot of attributes that suit it to the new technology. First and foremost, wire and cable is already a heavily mechanized industry where machines make the same product continuously for hours on end. However, in many cases the machines pre-date computers and so there is little to no digital feedback built in. With machines already doing all the creation of the product, but without a brain, an intelligent camera can instantly add a high degree of control and feedback, often beyond what many new machines have without intelligent vision.

An Overview camera can get continuous information on every mm of cable as it is made from a tightly framed view on the product as it comes off the machine. Cameras can be fit in between dies and drive wheels or in between other sensors. Generally the Overview setup is one of the smallest quality tools you’ll fit into your machines.

An Overview camera sits inside a custom box with controlled lighting and at a set distance
The Overview camera can interface in a variety of places on all types of taping, cabling, and braiding machines

Finally, the nature and frequency of the errors lend themselves to several of the most accurate computer vision techniques including classification through a neural net. Furthermore, cables vary, but they have enough common attributes that general algorithms can be written that don’t take a prohibitive amount of customization to work. This is truly a differentiator of the new type of algorithm. Classical computer vision techniques can not accurately handle the variation cable to cable.

What’s the Upside in the Factory?

Having machines with continuous error spotting helps in a few ways. Complete error spotting means there is no way an error can escape your factory. Overall quality will increase. Real-time error spotting reduces rework time, and allows quality teams to more quickly debug problems, by summoning people to machines or by uncovering an underlying pattern.

The “cruise control” the Overview cameras allow, help operations in a number of ways. Error spotting and machine stop, can allow dark shifting, where machines run with nobody in the facility. You can also see more overall use of your machines if you currently rely on operators to monitor and shutdown machines in error situations. Operations teams can run more equipment for longer or individuals can tend to more machines.

Operations view allows you to see the status of all machines from anywhere with a web connection

The continuous data collection that accompanies the error spotting can allow a more complete operational improvement by uncovering deeper inefficiencies. While the error spotting is the immediate benefit, the continuous tracking of production and error data can teach you about the effectiveness of your operations on a deeper level.

Data Heavy and Incremental

Industry 4.0 is a buzzy term that often refers to a lot of potential modernization options. Overview’s cameras can definitely be thought of as an “industry 4.0” product, but the optimization from greater data collection is often a secondary benefit. The primary benefit is the real time error spotting, and while it is 100% enabled by advancing technology it is a more immediate and concrete improvement relative to some of the squishier industry 4.0 efforts. We think the immediacy of the error spotting helps in the short term and the data collection works in the background to ultimately make some of the step change improvements that can come from embracing an industry 4.0 mentality.

While we believe factories will see the most benefit from outfitting all of their machines with the Overview system, curious operators can start with a single machine or a handful of machines. The self-contained, one-off nature of the Overview cameras and web interface allow easy trials and slower incremental use of the technology rather than a disruptive overhaul.

If you’re curious about industry 4.0, a few Overview cameras can be a great way to try out the advantages of digitization and data collection.

In Summary

Overview’s vision systems use advanced AI recognition algorithms to create reliable and affordable error spotting systems. Real time defect detection provides immediate benefits in improved quality and reduced rework time and scrap. Continuous connectivity and data collection allow the long term efficiency improvements that industry 4.0 technology promises. A low cost and easy integration allows anyone to try this exciting new technology and see how it fits in their workflow.

Overview’s cameras are small enough to fit in braiding machines and can spot all braiding errors including single windows

About the Author

Chris Van Dyke is Overview’s CEO and one of its founders. Before Overview Chris worked for 8 years at Tesla where he designed custom charging cables for all of Tesla’s EV charging applications. After working on the charging systems, Chris led a team that designed the Gigafactory and spent time working with many manufacturing teams. Chris left Tesla to start Overview because of the exciting promise of computer vision and satisfaction of working in the manufacturing field.

Please contact Chris at chris@overview.ai with any follow up questions

Russell Nibbelink Overview Co-founder

Russell Nibbelink

Co-founder & Head of Engineering

Russell Nibbelink is the head of engineering and a co-founder at Overview. At Overview, he leads the team that helps customers implement cutting-edge, high-speed deep learning algorithms to reduce manufacturing waste and defective parts. 

Before joining Overview, Russell was a software engineer at Salesforce, where he worked on web-scale infrastructure projects. His many accomplishments at Salesforce include the migration of Salesforce’s largest customers to upgraded hardware with no downtime, live database change record ingestion, and the creation of visibility tooling for calculating data throughput rates.

Russell received his Bachelor of Science from the University of California Berkeley College of Engineering, where he was also the head teaching assistant in the field of High Performance Computing.

Chris Van Dyke Overview CEO

Chris Van Dyke

Co-founder & CEO

Chris Van Dyke is the CEO and co-founder of Overview. He is responsible for the company’s strategic direction, corporate development, sales and marketing. With over 16 years of engineering and manufacturing expertise, Chris focuses on optimizing customer’s manufacturing operations using artificial intelligence and computer vision to accelerate product development, reduce rework, and improve yields. Since founding Overview in 2018, Chris has been instrumental in building a company and team dedicated to a disruptive, positive impact on manufacturing for decades to come. 

Before Overview, Chris served in several senior manufacturing engineering positions at Tesla for eight years. For Tesla’s Model 3, he led the 80-person battery design team from the initial battery concept to the product launch to the high-volume production. He also managed the infrastructure and equipment design for the first Gigafactory, including equipment for supporting battery cell manufacturing. Chris also launched Tesla’s Electric Vehicle Supercharger Program, with more than 25,000 stations worldwide. 

Prior to Tesla, Chris was a senior engineer at H2Gen Innovations, during which time he co-authored multiple patents. Chris holds a Bachelor of Science degree in Mechanical Engineering from Stanford University and a Master of Science in Chemical Engineering from the University of Virginia.

Austin Appel

Co-founder & Head of Product

Austin Appel is the head of product and a co-founder at Overview, where he is responsible for leading product development and operations. With over 8 years of experience in manufacturing and systems engineering, Austin works with customers to ensure that manufacturing operations can quickly adopt and use artificial intelligence-based computer vision systems.

Before Overview, Austin spent four years at Tesla in various capacities, including battery manufacturing, and research and development (R&D). He led the design for manufacturability (DFM) and automation efforts for the Model 3 battery pack production at Tesla’s first Gigafactory. Earlier at Tesla, he expanded the production of Model S and X battery packs through equipment design and implementation.

Austin holds a dual Bachelor of Science degree in Manufacturing Engineering and Mechanical Engineering from Northwestern University.