Add Depth to Your Processes With 3D Machine Vision

What comes to mind first when you think of 3D? Cheap red and blue glasses? Paying extra at a movie theater? Or maybe the awkward top screen on a Nintendo 3DS? Neither industrial machine vision nor robot guidance likely come to mind, but they should.

Advancements in 3D machine vision have taken the old method of 2D image processing and added literal depth. You become emerged into the application with true definition of the target—far from what you get looking at a flat image.

See For Yourself

Let’s do an exercise: Close one eye and try to pick up an object on your desk by pinching it. Did you miss it on the first try? Did things look foreign or off? This is because your depth perception is skewed with only one vision source. It takes both eyes to paint an accurate picture of your surroundings.

Now, imagine what you can do with two cameras side by side looking at an application. This is 3D machine vision; this is human.

How 3D Saves the Day

Robot guidance. The goal of robotics is to emulate human movements while allowing them to work more safely and reliably. So, why not give them the same vision we possess? When a robot is sent in to do a job it needs to know the x, y and z coordinates of its target to best control its approach and handle the item(s). 3D does this.

Part sorting. If you are anything like me, you have your favorite parts of Chex mix. Whether it’s the pretzels or the Chex pieces themselves, picking one out of the bowl takes coordination. Finding the right shape and the ideal place to grab it takes depth perception. You wouldn’t use a robot to sort your snacks, of course, but if you need to select specific parts in a bin of various shapes and sizes, 3D vision can give you the detail you need to select the right part every time.

Palletization and/or depalletization. Like in a game of Jenga, the careful and accurate stacking and removing of parts is paramount. Whether it’s for speed, quality or damage control, palletization/ depalletization of material needs 3D vision to position material accurately and efficiently.

I hope these 3D examples inspire you to seek more from your machine vision solution and look to the technology of the day to automate your processes. A picture is worth a thousand words, just imagine what a 3D image can tell you.

Fork Sensors, the Best Choice for Range, Reliability, Ease of Installation

Photoelectric sensors are a staple within many industries when it comes to automation thanks to their non-contact detection over longer ranges than many other sensing types. Also available in a variety of housing types and protection classes to meet the specific demands of an application, they offer manufacturers many different variants and models. The range of styles can make selecting the perfect photoelectric sensor for your specific application challenging. This post highlights the benefits of through-beam sensors and why fork sensors specifically, are often the ideal sensor for the job.

Through-beam sensors can detect anything, regardless of color, texture or reflectivity. This makes them highly efficient in any application where material or parts need to be detected during the process. They require an emitter and receiver. The emitter sends a light beam toward the receiver. When this light beam is blocked, the sensor will trigger. A common example of this is the sensor system on a garage door that detects obstructions and keeps the door from closing. (The software can also inverse this, so the sensor triggers when the light beam is not obstructed. Read more about these light-on/dark-on modes).

Traditional Through-Beams vs. Fork Sensors

Through-beam photoelectric sensors are simple technology that are non-contact, reliable and can operate over distances up to 100 meters, making them a go-to for many applications. But they aren’t without fault. Because the emitter and receiver are typically in separate housings, the two parts must line up perfectly to work. This alignment takes extra time during assembly and is prone to problems in the future if the emitter or receiver move,  even slightly. Machine vibrations can cause a misalignment.

Fork sensors, also called C slot or U slot sensors, incorporate both the emitter and the receiver into a single body, providing the benefits of a through-beam sensor without the installation issues.

This allows for reduced installation and maintenance time of the sensor in several ways:

    • Mounting a single sensor instead of two
    • Half as many cables needed for networking
    • No touchy alignment needed when installing the sensor
    • No maintenance needed re-aligning the sensors in the future

Photoelectric fork sensors come with sensing windows widths up to 220 mm and a range of light sources to accommodate many application needs. Check them out the next time you are considering a photoelectric sensor and see if they’re the best choice for your application.

Lithium Ion Battery Manufacturing – RFID is on a Roll

With more and more consumers setting their sights on ‘Drive Electric,’ manufacturers must prepare themselves for alternative solutions to combustion engines. This change will no doubt require an alternative automation strategy for our electric futures.

The battery

The driving force behind these new electric vehicles is, of course, the battery. With this new wave of electric vehicles, the lithium ion battery manufacturing sector is growing exponentially, creating a significant need for traceability and tracking throughout the manufacturing processes.

Battery manufacturing is classified into three major production areas:

    1. Electrode manufacturing
    2. Cell assembly
    3. Finishing formation, aging and testing

These processes require flexible and efficient automation solutions to produce high quality batteries effectively. As such, there are numerous areas that can benefit from RFID and/or code reading solutions. One of the biggest of these is the electrode manufacturing process, specifically on the individual mother and daughter electrode rolls. This is a great application for UHF (Ultra-High Frequency) RFID.

The Need for RFID

The electrode formation process involves numerous production steps, including mixing, coating, calendaring, drying, slitting and vacuum drying. Each machine process generally begins with unwinding turrets and ends with winding ones. A roll-to-roll process.

Two of the three primary components of the lithium ion battery, both the anode and cathode electrode, are produced on rolls and require identification, process step validation and full traceability all the way through the plant.

During the slitting process both larger mother rolls are unwound and sliced into multiple, smaller daughter rolls. These mother and daughter rolls must also be tracked and traced through the remaining processes, into storage and ultimately, into a battery cell.

Solution

Working with our battery customers and understanding their process needs, a UHF RFID tag was developed specifically to withstand the electrode production environment. Having a tag that can withstand a high temperature range is crucial, particularly in the vacuum drying lines. This tag is capable of surviving cycling applications with temperatures up to 235 °C. Its small form factor is ideal for recess mounting in the anode and cathode roll cores with an operating range reaching 4 meters.

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The tag embedded in the roll core paired with an RFID processor and UHF antenna provides all the necessary hardware in supporting battery plants to achieve their desired objective of tracking all production steps. Customers not only have the option of obtaining read/writes, via fixed antennas at the turrets, but also handheld ones for all storage locations — from goods receiving to daughter coil storage racks within a plant.

This UHF RFID system allows for tracking from the initial electrode coils from goods received in the warehouse, through the multiple machines in the electrode manufacturing process, into the storage areas, and to the battery cell assembly going in the electric vehicle — ultimately linking all battery cells back to a particular daughter roll, and back to its initial mother roll. RFID is on a Roll!

Tire Manufacturing – IO-Link is on a Roll

Everyone working in the mobility industry knows that the tire manufacturing process is divided up into five areas throughout a large manufacturing plant.

    1. Mixing
    2. Tire prep
    3. Tire build
    4. Curing and molds
    5. Final inspection

Naturally,  conveyors, material handling, and AGV processes throughout the whole plant.

All of these areas have opportunities for IO-Link components, and there are already some good success stories for some of these processes using IO-Link.

A major opportunity for IO-Link can be found in the curing press area. Typically, a manufacturing plant will have about 75 – 100 dual cavity curing presses, with larger plants having  even more. On these tire curing presses are many inputs and outputs in analog signals. These signals can be comprised of pressure switches, sensors, pneumatic, hydraulic, linear positioning, sensors in safety devices, thermo-couples and RTD, flow and much more.

IO-Link provides the opportunity to have all of those inputs, outputs and analog devices connected directly to an IO-Link master block and hub topography. This makes it not only easier to integrate all of those devices but allows you to easily integrate them into your PLC controls.

Machine builders in this space who have already integrated IO-Linked have discovered how much easier it is to lay out their machine designs, commission the machines, and decrease their costs on machine build time and installations.

Tire manufacturing plants will find that the visual diagnostics on the IO-Link masters and hubs, as well as alarms and bits in their HMIs, will quickly help them troubleshoot device problems. This decreases machine downtime and delivers predictive maintenance capabilities.

Recently a global tire manufacturer getting ready to design the curing presses for a new plant examined the benefits of installing IO-Link and revealed a cost savings of more than $10,000 per press. This opened their eyes to evaluating IO-Link technology even more.

Tire Manufacturing is a perfect environment to present IO-Link products. Many tire plants are looking to upgrade old machines and add new processes, ideal conditions for IO-Link. And all industries are interested in ways to stretch their budget.

 

How Cameras Keep Tire Manufacturers From Spinning Their Wheels

Tires being transported between the curing presses and the staging area before their final inspection often become clustered together. This jam up can cause imperfections to the tires and damage to the conveyors. To alleviate this problem, some tire manufacturers have installed vision systems on their conveyors to provide visual feedback to their production and quality teams, and alert them when the tires start to get too close together.

A vision system can show you alerts back in your HMI by using inputs and outputs built into the camera or use an IO-Link port on the camera to attach a visual display, for example a SmartLight with audible and flashing alerts enabled. Once you see these alerts, the PLC can easily fix the issue from the program or a maintenance worker or engineer can quickly respond to the alert.

Widespread use of smart vision cameras with various pixel options has become a trend in tire manufacturing. In additional to giving an early alert to bunching problems, vision systems can also capture pictures and data to verify that tires were cleared all the way into final inspection. Although tire machine builders are being asked to incorporate vision systems into their machines during the integration process, it is more likely for systems to be added in plants at the application level.

Vision systems can improve production throughput, quality issues and record production data about the process for analytics and analysis down the road. Remember a tire plant usually consists of these processes in their own large section of the plant and involves many machines in each section:

  • Mixing
  • Tire Prep
  • Tire Build
  • Curing
  • Final Inspection

Each one of these process areas in a plant can benefit from the addition of vision systems. Here are a few examples:

  • Mixing areas can use cameras as they mill rubber and detect when rubber sheets are off the rollers and to look for engraved information embedded in the rubber material for logistics and material flow to the proper processes.
  • Tire Prep can use cameras to ensure all the different strand colors of steel cords are embedded or painted on the rubber plies before going to tire build process.
  • Tire Build can use vision to detect the side-wall beads are facing the right direction and reading the embedded position arrows on the beads before tire plies are wrapped around them.
  • Curing area can use vision to monitor tire clusters on conveyors and make sure they are not too close to each other by using the measuring tool in the camera software.
  • Final Inspection can use vision to read barcodes, QR codes, detect colors of embossed or engraved serial numbers, detect different color markings and shape of the markings on the tire.

The use of machine vision systems can decrease quality issues by pinpointing errors before they make it through the entire production process without detection.

Improve Your Feeder Bowl System (and Other Standard Equipment) with IO-Link

One of the most common devices used in manufacturing is the tried and true feeder bowl system. Used for decades, feeder bowls take bulk parts, orients them correctly and then feeds them to the next operation, usually a pick-and-place robot. It can be an effective device, but far too often, the feeder bowl can be a source of cycle-time slowdowns. Alerts are commonly used to signal when a feed problem is occurring but lack the exact cause of the slow down.

feeder bowl

A feed system’s feed rate can be reduced my many factors. Some of these include:

  • Operators slow to add parts to the bowl or hopper
  • Hopper slow to feed the bowl
  • Speeds set incorrectly on hopper, bowl or feed track
  • Part tolerance drift or feeder tooling out of adjustment

With today’s Smart IO-Link sensors incorporating counting and timing functions, most of the slow-down factors can be easily seen through an IIoT connection. Sensors can now time how long critical functions take. As the times drift from ideal, this information can be collected and communicated upstream.

A common example of a feed system slow-down is a slow hopper feed to the bowl. When using Smart IO-Link sensors, operators can see specifically that the hopper feed time is too long. The sensor indicates a problem with the hopper but not the bowl or feed tracks. Without IO-Link, operators would simply be told the overall feed system is slow and not see the real problem. This example is also true for the hopper in-feed (potential operator problem), feed track speed and overall performance. All critical operations are now visible and known to all.

For examples of Balluff’s smart IO-Link sensors, check out our ADCAP sensor.

How flexible inspection capabilities help meet customization needs and deliver operational excellence

As the automotive industry introduces more options to meet the growing complexities and demands of its customers (such as increased variety of trim options) it has rendered challenges to the automotive manufacturing industry.

Demands of the market filter directly back to the manufacturing floor of tier suppliers as they must find the means to fulfill the market requirements on a flexible industrial network, either new or existing. The success of their customers is dependent on the tier supplier chain delivering within a tight timeline. Whereby, if pressure is applied upon that ecosystem, it will mean a more difficult task to meet the JIT (just in time) supply requirements resulting in increased operating costs and potential penalties.

Meeting customer requirements creates operational challenges including lost production time due to product varieties and tool change time increases. Finding ways to simplify tool change and validate the correct components are placed in the correct assembly or module to optimize production is now an industry priority. In addition, tracking and traceability is playing a strong role in ensuring the correct manufacturing process has been followed and implemented.

How can manufacturing implement highly flexible inspection capabilities while allowing direct communication to the process control network and/or MES network that will allow the capability to change inspection characteristics on the fly for different product inspection on common tooling?

Smart Vision Inspection Systems

Compact Smart Vision Inspection System technology has evolved a long way from the temperamental technologies of only a decade ago. Systems offered today have much more robust and simplistic intuitive software tools embedded directly in the Smart Vision inspection device. These effective programming cockpit tools allow ease of use to the end user at the plant providing the capability to execute fast reliable solutions with proven algorithm tools. Multi-network protocols such as EthernetIP, ProfiNet, TCP-IP-LAN (Gigabit Ethernet) and IO-LINK have now come to realization. Having multiple network capabilities delivers the opportunity of not just communicating the inspection result to the programmable logic controller (via process network) but also the ability to send image data independent of the process network via the Gigabit Ethernet network to the cloud or MES system. The ability to over-lay relevant information onto the image such as VIN, Lot Code, Date Code etc. is now achievable.  In addition, camera housings have become more industrially robust such as having aluminum housings with an ingress protection rating of IP67.

Industrial image processing is now a fixture within todays’ manufacturing process and is only growing. The technology can now bring your company a step closer to enabling IIOT by bringing issues to your attention before they create down time (predictive maintenance). They aid in reaching operational excellence as they uncover processing errors, reduce or eliminate scrap and provide meaningful feedback to allow corrective actions to be implemented.

Operational Excellence – How Can We Apply Best Practices Within the Weld Shop?

Reducing manufacturing costs is absolutely a priority within the automotive manufacturing industry. To help reduce costs there has been and continues to be pressure to lower MRO costs on high volume consumables such as inductive proximity sensors.

Traditionally within the MRO community, the strategy has been to drive down the unit cost of components from their suppliers year over year to ensure reduce costs as much as possible. Of course, cost optimization is important and should continue to be, but factors other than unit cost should be considered. Let’s explore some of these as it would apply to inductive proximity sensors in the weld shop.

Due to the aggressive manufacturing environment within weld cell, devices such as inductive proximity sensors are subjected to a variety of hostile factors such as high temperature, impact damage, high EMF (electromagnetic fields) and weld spatter. All of these factors drastically reduce the life of these devices.

There are  manufacturing costs associated with a failed device well beyond that of the unit cost of the device itself. These real costs can be and are reflected in incremental premium costs such as increased downtime (both planned and unplanned),  poor asset allocation, indirect inventory, expedited freight, outsourcing costs, overtime, increased manpower, higher scrap levels, and sorting & rework costs. All of these factors negatively affect a facility’s Overall Equipment Effectiveness (OEE).

Root Cause

In selection of inductive proximity sensors for the weld manufacturing environment there are root cause misconceptions and poor responses to the problem. Responses include: leave the sensor, mounting and cable selection up to the machine builder; bypass the failed sensor and keep running production until the failed device can be replaced; install multiple vending machines in the plant to provide easier access to spare parts (replace sensors often to reduce unplanned downtime);  and the sensors are going to fail anyway so just buy the cheapest device possible.

None of these address the root cause of the failure. They mask the root cause and exacerbate the scheduled and unscheduled downtime or can cause serious part contamination issues down stream, resulting in enormous penalties from their customer.

So, how can we implement a countermeasure to help us drive out these expensive operating costs?

  • Sensor Mounting – Utilize a fixed mounting system that will allow a proximity sensor to slide into perfect mounting position with a positive stop to prevent the device from being over extended and being struck by the work piece. This mounting system should have a weld spatter protective coating to reduce the adherence of weld spatter. This will also provide extra impact protection and a thermal barrier to further assist in protecting the sensing device asset.
  • The Sensor – Utilize a robust fully weld protective coated stainless steel body and face proximity sensor. For applications with the sensor in an “on state” during the weld cycle and/or to detect non-ferrous utilize a proper weld protective coated Factor 1 (F1) device.
  • Cabling – A standard cable will not withstand a weld environment such as MIG welding. Even a cable with protective tubing can have open areas vulnerable for weld berries to land and cause burn through on the cables resulting in a dead short. A proper weld sensor cord set with protective coating on the lock nut, high temp rated and weld resistant overmold to a weld resistant jacketed cable should be used.

By implementing a weld best practice total solution as described above, you will realize significant increases in your facilities OEE contributing to the profitability and sustainability of your organization.

Ask these 3 simple questions:

1) What is the frequency of failure

2) What is the Mean Time To Repair (MTTR)

3) What is the cost per minute of downtime.

Once you have that information you will know with your own metrics  what the problem is costing your facility by day/month/year. You may be surprised to see how much of a financial burden these issues are costing you. Investing in the correct best practice assets will allow you to realize immediate results to boost your company OEE.