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Driving Efficiency and Reliability in Automotive Manufacturing

In the days of Henry Ford – when you could get a car in any color as long it was black – the assembly line involved grabbing a part and putting it on the car. Today, there are literally thousands of iterations of car options, drastically increasing the need for tracking and traceability of all parts that go into the cars. How do you ensure that the components going into vehicles are the correct ones?

Limitations of traditional barcode stickers

The answer is ever-evolving. At first, automotive companies were printing off one-dimensional barcodes on stickers – a time-consuming, labor-intensive, and often wasteful process. It was necessary for an individual to print a stack of stickers hoping that they were correct and in the right order, manually put them on the parts, and hope they wouldn’t fall off. Unfortunately, many times they did fall off, leaving the operators without a way to track the parts. And once the part hit the assembly line, the operator had to manually scan the barcode, which typically took six to 10 seconds.

The power of optical identification sensors

Modern automotive companies are automating this process with sensors for optical identification. They can reliably and precisely read both 1D and 2D bar codes. This two-step process includes:

    1. Using lasers (CO2 for plastic or Fiber for metal), a Direct Part Mark (DPM) is permanently etched onto the component. This DPM remains readable throughout the component’s lifespan.
    2. Once marked, a nest is created on the component, equipped with two to four cameras. These cameras capture visible 2D data matrices or 1D sticker barcodes from up to 600mm away. All data is transmitted via IO-Link to the PLC. This process eliminates scanning errors and reduces scrap.

Advanced condition monitoring for quality and efficiency

In addition to code reading functions, advanced condition monitoring capabilities have become an essential part of ensuring quality and efficiency in automotive manufacturing. These capabilities enable the continuous monitoring of various parameters related to the components and their operational conditions. Sensors equipped with advanced condition monitoring features, such as temperature sensors, vibration sensors, humidity sensors, inclination sensors, signal quality sensors, and operating time sensors, are deployed alongside the code reading sensors.

Overall, the combination of code reading sensors and advanced condition monitoring capabilities ensures not only the correct identification and traceability of components but also enhances overall quality control, reduces downtime, minimizes scrap, and improves the reliability and performance of the final products.

Click here for more information on optical code readers with IO-Link and condition monitoring.

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Who Moved My Data? Outsourcing Condition Monitoring

This is the first in a three-part blog series on condition monitoring.

 

Critical assets are the lifeblood of the manufacturing plant. They are the devices, machines, and systems that when broken down or not performing to expected standards, can cause downtimes and production or quality losses resulting in rejects. If not maintained at the optimal levels of performance, these assets can damage the overall reputation of the brand. Some examples include evaporate fans, presses, motors, conveyor lines, mixers, grinders, and pumps.

Most manufacturing plants maintain critical assets on a periodic schedule, also known as preventative maintenance. However, in recent years, condition-based maintenance strategies, made possible with advancements in sensor and communications technologies, further improve the uptime, lower the overall cost of maintenance, and enhance the life of critical assets. Condition-based maintenance relies on continuous monitoring of key parameters of these assets.

Once the plant decides to adopt predictive maintenance (PdM) strategies for maintaining the assets, they face an important decision: to implement the condition monitoring strategy in-house or to outsource it to a third party – new term – continuous condition monitoring as a service (CCMAAS).

The bipartisan view expressed in this three-part blog series explores these options to help plant managers make the best, most appropriate decision for their plants. Just a hint: the decision for the most part is based on who controls the data regarding your plant’s critical assets.

In this part, we will delve a little deeper into the advantages and disadvantages of the CCMAAS option.

The advancements in cloud-based data management enable businesses to offer remote monitoring of the data related to the assets. In a nutshell, the service providers will audit the plant’s needs and deploy sensors and devices in the plant. Then, using IoT gateways, they transfer the critical parameters about the assets, such as vibration, temperature, humidity, and other related parameters to the cloud-based storage. The service provider’s proprietary algorithms and expertise would synthesize the data and send the plant’s maintenance personnel alerts about maintenance.

Advantages of outsourcing condition monitoring:

    1. Expertise and support: By outsourcing data management to a specialized provider, the plant has access to a team of experts who possess in-depth knowledge of condition monitoring and data analytics. These professionals can provide valuable insights, guidance, and technical support.
    2. Scalability and flexibility: Outsourced solutions offer greater scalability, allowing businesses to easily accommodate changing monitoring requirements and fluctuating data volumes.
    3. Cost reduction: Outsourcing eliminates the need for upfront investments in hardware and infrastructure, significantly reducing capital expenses. Instead, companies pay for services based on usage, making it a more predictable and manageable operational expense.

Disadvantages of outsourcing condition monitoring:

    1. Data security concerns: Entrusting critical data to a third-party provider raises concerns about data security and confidentiality. Plants must thoroughly assess the provider’s security protocols, data handling practices, and compliance with industry regulations to mitigate these risks.
    2. Dependency on service providers: Outsourcing data management means relying on external entities. If the service provider has technical difficulties, interruptions in service, or business-related issues, it may impact the organization’s operations and decision-making.
    3. Potential data access and control limitations: Plants may face limitations in accessing and controlling their data in real time. Reliance on a service-level agreement with the provider for data access, retrieval, or system upgrades can introduce delays or restrict autonomy.

Just like critical assets are the lifeblood of the manufacturing plants, in the near future data that is being generated every second in the plant will also be equally important. Outsourcing does allow manufacturing plants to adapt quickly to the new normal in the industry.  I would not completely discount outsourcing based on the control of data. The option does have its place. You will just have to wait for my concluding blog on this topic.

In the meantime, your feedback is always welcome.

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Using MQTT Protocol for Smarter Automation

In my previous blog post, “Edge Gateways to Support Real-Time Condition Monitoring Data,” I talked about the importance of using an edge gateway to gather the IoT data from sensors in parallel with a PLC. This was because of the large data load and the need to avoid interfering with the existing machine communications. In this post, I want to delve deeper into the topic and explain the process of implementing an edge gateway.

Using the existing Ethernet infrastructure

One way to collect IoT data with an edge gateway is by using the existing Ethernet infrastructure. With most devices already communicating on an industrial Ethernet protocol, an edge gateway can gather the data on the same physical Ethernet port but at a separate software-defined number associated to a network protocol communication.

Message Queue Telemetry Transport (MQTT)

One of the most commonly used IoT protocols is Message Queue Telemetry Transport (MQTT). It is an ISO standard and has a dedicated software Ethernet port of 1883 and 8883 for secure encrypted communications. One reason for its popularity is that it is designed to be lightweight and efficient. Lightweight means that the protocol requires a minimum coding and it uses low-bandwidth connections.

Brokers and clients

The MQTT protocol defines two entities: a broker and client. The edge gateway typically serves as a message broker that receives client messages and routes them to the appropriate destination clients. A client is any device that runs an MQTT library and connects to an MQTT broker.

MQTT works on a publisher and subscriber model. Smart IoT devices are set up to be publishers, where they publish different condition data as topics to an edge gateway. Other clients, such as PC and data centers, can be set up as subscribers. The edge gateway, serving as a broker receives all the published data and forwards it only to the subscribers interested in that topic.

One client can publish many different topics as well as be a subscriber to other topics. There can also be many clients subscribing to the same topic, making the architecture flexible and scalable.

The edge gateway serving as the broker makes it possible for devices to communicate with each other if the device supports the MQTT protocol. MQTT can connect a wide range of devices, from sensors to actuators on machines to mobile devices and cloud servers. While MQTT isn’t the only way to gather data, it offers a simple and reliable way for customers to start gathering that data with their existing Ethernet infrastructures.

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Improving Overall Equipment Effectiveness

Overall equipment effectiveness (OEE) is a critical metric for measuring the efficiency of manufacturing operations. It considers three factors – availability, performance, and quality – to determine the effective use of equipment.

Where do we focus to win the biggest improvements?

To improve OEE, it’s important to focus on these five key areas:

    1. Equipment maintenance: Ensuring equipment is well-maintained is critical to achieving high OEE. Regular inspections, preventive maintenance or, even better, “predictive maintenance,” and prompt repairs can help minimize downtime from unexpected breakdowns. Condition monitoring sensors and the data they generate can predict where failures may to occur so action can be taken to avoid such downtimes.
    2. Production planning: Effective production planning can help optimize production schedules, minimize set-up time, and reduce changeover time, as well as help increase equipment utilization and reduce downtime. Software solutions are available that provide operators with guidance and optimize changeovers between different set-ups or formats.
    3. Process optimization: Analyzing and optimizing production processes can help identify bottlenecks, reduce waste, and improve overall efficiency. This can involve implementing process improvements, such as reducing cycle times or optimizing material flow.
    4. Workforce training: A well-trained workforce can help minimize errors, reduce downtime, and improve overall quality. Providing employees with the necessary skills and training can also help increase productivity and equipment utilization. Operator guidance, including digital work instruction, which is available in a digital format, is increasingly familiar to the newer members of the workforce.
    5. Data analysis: Collecting and analyzing OEE and downtime data, and other key metrics can help identify areas for improvement and guide decision-making on where to focus. Implementing real-time monitoring and analysis can help detect issues early, well before a failure, and thus, minimize the impact on production.

By focusing on and ranking the areas outlined above, manufacturers can improve overall equipment effectiveness and achieve greater efficiency, productivity, and, most importantly, profitability.

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Automated Welding With IO-Link

IO-Link technologies have been a game-changer for the welding industry. With the advent of automation, the demand for increasingly sophisticated and intelligent technologies has increased. IO-Link technologies have risen to meet this demand. Here I explain the concepts and benefits of I-O Link technologies and how they integrate into automated welding applications.

What are IO-Link technologies?

IO-Link technologies refer to an advanced communication protocol used in industrial automation. The technology allows data transfer, i.e., the status of sensors, actuators, and other devices through a one-point connection between the control system and individual devices. Also, it enables devices to communicate among themselves quickly and efficiently. IO-Link technologies provide real-time communication, enabling continuous monitoring of devices to ensure optimal performance.

Benefits of IO-Link technologies

    • Enhanced data communication: IO-Link technologies can transfer data between the control system and sensors or devices. This communication creates an open and transparent network of information, reflecting the real-time status of equipment and allowing for increased reliability and reduced downtime.
    • Cost-efficiency: IO-Link technologies do not require complicated wiring and can significantly reduce material costs compared to traditional hardwired solutions. Additionally, maintenance is easier and more efficient with communication between devices, and there is less need for multiple maintenance employees to manage equipment.
    • Flexibility: With IO-Link technologies, the control system can control and monitor devices even when not attached to specific operator workstations. It enables one control system to manage thousands of devices without needing to rewrite programming to accommodate different machine types.
    • Real-time monitoring: IO-Link technologies provide real-time monitoring of devices, allowing control systems to monitor failures before they occur, making it easier for maintenance teams to manage the shop floor.

How are IO-Link technologies used in automated welding applications?

Automated welding applications have increased efficiencies and continuity in processes, and IO-Link technologies have accelerated these processes further. Automated welding applications have different stages, and each step requires real-time monitoring to ensure the process is efficient and effective. IO-Link technologies have been integrated into various parts of the automated welding process, some of which include:

    1. Positioning and alignment: The welding process starts with positioning and aligning materials such as beams, plates, and pipes. IO-Link sensors can detect the height and gap position of the material before the welding process begins. The sensor sends positional data to the control system as a feedback loop, which then adjusts the positioning system using actuators to ensure optimal weld quality.
    2. Welding arc monitoring: The welding arc monitoring system is another critical application for IO-Link technologies. Monitoring the arc ensures optimal weld quality and runs with reduced interruptions. IO-Link temperature sensors attached to the welding tip help control and adjust the temperature required to melt and flow the metal, ensuring that the welding arc works optimally.
    3. Power supply calibration: IO-Link technologies are essential in calibrating the power output of welding supplies, ensuring consistent quality in the welding process. Detectors attached to the power supply record the energy usage, power output and voltage levels, allowing the control system to adjust as necessary.
    4. Real-time monitoring and alerting: Real-time monitoring and alerting capabilities provided by IO-Link technologies help to reduce downtime where machine health is at risk. The sensors monitor the welding process, determining if there are any deviations from the set parameters. They then communicate the process condition to the control system, dispatching alerts to maintenance teams if an issue arises.

Using IO-Link technologies in automated welding applications has revolutionized the welding industry, providing real-time communication, enhanced data transfer, flexibility, and real-time monitoring capabilities required for reliable processes. IO-Link technologies have been integrated at various stages of automated welding, including positioning and alignment, welding arc monitoring, power supply calibration, and real-time monitoring and alerting. There is no doubt that the future of automated welding is bright. With IO-Link technologies, the possibilities are endless, forging ahead to provide more intelligent, efficient, and reliable welding applications.

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Why Choose an IO-Link Ecosystem for Your Next Automation Project?

By now we’ve all heard of IO-Link, the device-level communication protocol that seems magical. Often referred to as the “USB of industrial automation,” IO-Link is a universal, open, and bi-directional communication technology that enables plug-and-play device replacement, dynamic device configuration, centralized device management, remote parameter setting, device level diagnostics, and uses existing sensor cabling as part of the IEC standard accepted worldwide.

But what makes IO-Link magical?

If the list above doesn’t convince you to consider using IO-Link on your next automation project, let me tell you more about the things that matter beyond its function as a communications protocol.

Even though these benefits are very nice, none of them mean anything if the devices connected to the network don’t provide meaningful, relevant, and accurate data for your application.

Evolution of the IO-Link

IO-Link devices, also known as “smart devices,” have evolved significantly over the years. At first, they were very simple and basic, providing data such as the status of your inputs and outputs and maybe giving you the ability to configure a few basic parameters, such as port assignment as an input or an output digitally over IO-Link. Next, came the addition of functions that would improve the diagnostics and troubleshooting of the device. Multi-functionality followed, where you have one device under one part number, and can configure it in multiple modes of operation.

Nothing, however, affected the development of smart devices as much as the introduction of IIoT (Industrial Internet of Things) and the demand for more real-time information about the status of your machine, production line, and production plant starting at a device level. This demand drove the development of smart devices with added features and benefits that are outside of their primary functions.

Condition monitoring

IO-Link supplies both sensor/actuator details and secure information
IO-Link supplies both sensor/actuator details and secure information

One of the most valuable added features, for example, is condition monitoring. Information such as vibration, humidity, pressure, voltage and current load, and inclination – in addition to device primary function data – is invaluable to determine the health of your machine, thus the health of your production line or plant.

IO-Link offers the flexibility to create a controls architecture independent of PLC manufacturer or higher-level communications protocols. It enables you to:

    • use existing low-cost sensor cabling
    • enhance your existing controls architecture by adding devices such as RFID readers, barcode and identification vision sensors, linear and pressure transducers, process sensors, discrete or analog I/O, HMI devices, pneumatic and electro-mechanical actuators, condition monitoring, etc.
    • dynamically change the device configuration, auto-configure devices upon startup, and plug-and-play replacement of devices
    • enable IIOT, predictive maintenance, machine learning, and artificial intelligence

There is no other device-level communications protocol that provides as many features and benefits and is cost-effective and robust enough for industrial automation applications as IO-Link.

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Demystifying Machine Learning

Machine learning can help organizations improve manufacturing operations and increase efficiency, productivity, and safety by analyzing data from connected machines and sensors, machine. For example, its algorithms can predict when equipment will likely fail, so manufacturers can schedule maintenance before problems occur, thereby reducing downtime and repair costs.

How machine learning works

Machine learning teaches computers to learn from data – to do things without being specifically told how to do them. It is a type of artificial intelligence that enables computers to automatically learn or improve their performances by learning from their experiences.

machine learning stepsImagine you have a bunch of toy cars and want to teach a computer to sort them into two groups: red and blue cars. You could show the computer many pictures of red and blue cars and say, “this is a red car” or “this is a blue car” for each one.

After seeing enough examples, the computer can start to guess which group a car belongs in, even if it’s a car that it hasn’t seen before. The machine is “learning” from the examples you show to make better and better guesses over time. That’s machine learning!

Steps to translate it to industrial use case

As in the toy car example, we must have pictures of each specimen and describe them to the computer. The image, in this case, is made up of data points and the description is a label. The sensors collecting data can be fed to the machine learning algorithm in different stages of the machine operation – like when it is running optimally, needs inspection, or needs maintenance, etc.

Data taken from vibration, temperature or pressure measures, etc., can be read from different sensors, depending on the type of machine or process to monitor.

In essence, the algorithm finds a pattern for each stage of the machine’s operation. It can notify the operator about what must be done given enough data points when it starts to veer toward a different stage.

What infrastructure is needed? Can my PLC do it?

The infrastructure needed can vary depending on the algorithm’s complexity and the data volume. Small and simple tasks like anomaly detection can be used on edge devices but not on traditional automation controllers like PLCs. Complex algorithms and significant volumes of data require more extensive infrastructure to do it in a reasonable time. The factor is the processing power, and as close to real-time we can detect the machine’s state, the better the usability.

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Using Guided Changeover to Reduce Maintenance Costs, Downtime

A guided changeover system can drastically reduce the errors involved with machine operation, especially when added to machines using fully automated changeovers. Processing multiple parts and recipes during a production routine requires a range of machines, and tolerances are important to quantify. Only relying on the human element is detrimental to profits, machine maintenance, and production volumes. Implementing operator assistance to guide visual guidance will reveal inefficiencies and allow for vast improvements.

Removing human error

Unverified manual adjustments may cause machine fatigue or failure. In a traditional manual changeover system, the frequency of machine maintenance is greater if proper tolerances are not observed at each changeover. Using IO-Link can remove the variable of human error with step-by-step instructions paired with precise sensors in closed-loop feedback. The machine can start up and run only when all parts are in the correct position.

Preventative maintenance and condition monitoring

Preventative maintenance is achievable with the assistance of sensors, technology, and systems. Using condition monitoring for motors, pumps and critical components can help prevent the need for maintenance and notably improve the effectiveness of maintenance with custom alerts and notifications with a highly useful database and graphing function.

A repeatable maintenance routine based on condition monitoring data and using a system to guide machine changeover will prolong machine life and potentially eliminate downtime altogether.

For more, read this real-world application story, including an automated format change to eliminate human error, reduce waste and decrease downtime.

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Automation Insights: Top Blogs From 2022

It’s an understatement to say 2022 had its challenges. But looking back at the supply chain disruptions, inflation, and other trials threatening success in many industries, including manufacturing, there were practical insights we can benefit from as we dive into 2023. Below are the most popular blogs from last year’s Automation Insights site.

    1. Evolution of Pneumatic Cylinder Sensors

Top 2022 Automation Insights BlogsToday’s pneumatic cylinders are compact, reliable, and cost-effective prime movers for automated equipment. They’re used in many industrial applications, such as machinery, material handling, assembly, robotics, and medical. One challenge facing OEMs, integrators, and end users is how to detect reliably whether the cylinder is fully extended, retracted, or positioned somewhere in between before allowing machine movement.

Read more.

    1. Series: Condition Monitoring & Predictive Maintenance 

Top 2022 Automation Insights BlogsBy analyzing which symptoms of failure are likely to appear in the predictive domain for a given piece of equipment, you can determine which failure indicators to prioritize in your own condition monitoring and predictive maintenance discussions.

Read the series, including the following blogs:

    1. Know Your RFID Frequency Basics

Top 2022 Automation Insights BlogsIn 2008 I purchased my first toll road RFID transponder, letting me drive through and pay my toll without stopping at a booth. This was my first real-life exposure to RFID, and it was magical. Back then, all I knew was that RFID stood for “radio frequency identification” and that it exchanged data between a transmitter and receiver using radio waves. That’s enough for a highway driver, but you’ll need more information to use RFID in an industrial automation setting. So here are some basics on what makes up an RFID system and the uses of different radio frequencies.

Read more.

    1. IO-Link Event Data: How Sensors Tell You How They’re Doing

Top 2022 Automation Insights BlogsI have been working with IO-Link for more than 10 years, so I’ve heard lots of questions about how it works. One line of questions I hear from customers is about the operating condition of sensors. “I wish I knew when the IO-Link device loses output power,” or, “I wish my IO-Link photoelectric sensor would let me know when the lens is dirty.” The good news is that it does give you this information by sending Event Data. That’s a type of data that is usually not a focus of users, although it is available in JSON format from the REST API.

Read more.

    1. Converting Analog Signals to Digital for Improved Performance

Top 2022 Automation Insights BlogsWe live in an analog world, where we experience temperatures, pressures, sounds, colors, etc., in seemingly infinite values. There are infinite temperature values between 70-71 degrees, for example, and an infinite number of pressure values between 50-51 psi.

Read more.

We appreciate your dedication to Automation Insights in 2022 and look forward to growth and innovation in 2023.