While RFID technology has been around for almost 70 years, the last decade has seen widespread acceptance, particularly in automated manufacturing. It is now deployed for various common applications, including automatic data transfer in machining operations, quality control in production, logistics traceability, and inventory control.
RFID has contributed significantly to the evolution of data collection and handling. With this evolution has come vast amounts of data which can be crucial for process improvement, quality assurance, and regulatory compliance. Nevertheless, many organizations grapple with the challenge of transforming this abundance of data into actional insights.
Key industry terms such as Industry 4.0 and the Industrial Internet of Things (IIOT) were once perceived as distant concepts crafted by marketing teams, seemingly disconnected from the practicalities of the plant floor. However, these buzzwords emerged as a response to manufacturing organizations worldwide recognizing the imperative for enhanced visibility into their operations. While automation hardware and supporting infrastructure have swiftly progressed in response to this demand, there remains a significant need for software that can effectively transform raw data into actionable data. This software must offer interactive feedback through reporting, dashboards, and real-time indicators.
Meeting the demand will bring vendors from various industries and start-ups, with a few established players in automation rising to the occasion. Competition serves as a motivator, but the crucial factor in bridging the software gap on the plant floor lies in partnering with a vendor attuned to the specific needs of that environment. The question then becomes: How do you discern the contenders from the pretenders? The following is a checklist to help.
Does the potential vendor have:
A solid understanding that downtime and scrap must be reduced or eliminated?
Core expertise in automation tailored for the plant floor?
Smart hardware devices such as RFID and condition monitoring sensors?
A comprehensive system solution capable of collecting, analyzing, and transporting data from the device to the cloud?
A user-friendly interface that allows interaction with mobile devices like tablets and phones?
The ability to generate customized reports tailored to your organization’s requirements?
A stellar industry reputation for quality and reliability?
A support chain covering pre-sales, installation, and post-sales support?
Demonstrated instances of successful system deployments?
A willingness to develop or adapt existing devices to address your specific needs?
If you can tick all of these, it’s a safe bet you are in good hands. Otherwise, you’re taking a chance.
In the present technological age, sensing technology is advancing at an unprecedented pace, transforming the way we monitor the manufacturing process. One of the newest innovations that will reshape various manufacturing and industries is the advent of smart sensor products. These “smart” sensing devices have permeated every aspect of our lives personally, let alone in manufacturing, and offer unparalleled advantages in information, efficiency, convenience, and sustainability. Let’s explore some of the compelling reasons why smart sensors will soon become indispensable in manufacturing and highlight the aspects they will impact.
Beyond single sensing
Smart sensor products are engineered to offer more than just a single sensing function, such as a photoeye sensor detecting the presence of a pallet. They can also detect and respond to various environmental inputs like internal temperature, cycle count, vibration, and even inclination changes. This enables significantly greater insight into a changing manufacturing environment, possibly even prompting the need for human intervention before a failure occurs.
Efficiency, automation, and cost savings
In manufacturing, sensors play a crucial role in improving production processes, reducing waste, and enhancing quality control. But today’s smart sensors can also provide greater efficiency and speed in changes to the manufacturing environment and automate not only the manufacturing process but the detection of changes as well. This increased efficiency and automation not only saves time and resources but also holds the potential for substantial long-term cost savings by minimizing waste.
Real-time insights for informed decisions
Smart sensors can collect and report significant real-time data, providing valuable insights into various phenomena, as mentioned above. In manufacturing, imagine detecting a rise in temperature on the production line that could potentially affect product quality or the efficiency of the manufacturing equipment. Or consider identifying changes in a sensor’s inclination, possibly because the device has come loose or shifts in the machine’s mounting – both of which can negatively affect product quality and productivity, lead to waste, and even unplanned downtime.
Smart sensors and environmental conservation
The ability to collect and analyze precise environment and device performance data empowers manufacturers and industries to make informed decisions, encourage innovation, and significantly improve problem-solving processes.
Smart sensor products can play a pivotal role in environmental conservation efforts. By monitoring conditions like vibration and even inclination, these sensors can detect problems in motors and drive systems that can have a direct impact on energy consumption. Typically, they tend to consume more power to compensate for the impending mechanical failures. By detecting these conditions sooner rather than later, smart sensors can help optimize energy usage in manufacturing industries, contributing to the global push for energy efficiency and reduced carbon emissions.
Safety enhancement
Smart sensor technologies can also bolster safety measures across various systems. In manufacturing, they can detect hazardous conditions like excessive heat buildup and vibration. This enables prompt interventions and helps prevent accidents that could jeopardize safety.
IoT with smart sensors
And finally, smart sensors are at the heart of the Industrial Internet of Things (IIoT) or Industry 4.0, connecting more devices and systems in seamless communication using protocols like IO-Link and Ethernet. This interconnectedness fosters innovation by enabling the development of new, more efficient manufacturing applications and services. For instance, smart sensors in industrial settings help predictive maintenance, which in turn reduces downtime, enhances overall productivity, and bolsters competitiveness. The integration of smart sensors is driving a wave of innovation, transforming ideas into tangible solutions.
Embracing the future: competitive advantage
The adoption of smart sensor products represents a paradigm shift in how we perceive and interact with machines in manufacturing. Their ability to enhance efficiency, improve data analysis, report on, and improve the environment, ensure safety, and foster innovation underscores the significance they can play in the modern manufacturing facility. As we continue to explore the boundless possibilities of interacting technology, embracing smart sensor products is not just a choice, but a competitive advantage. By integrating these intelligent devices into our machines and industries, we are paving the way for a future that is more productive, efficient, environmentally sustainable, and more interconnected. This marks another transformative leap toward a smarter and more interconnected manufacturing world.
The Industrial Internet of Things (IIoT) is reshaping the industrial automation landscape, offering unprecedented connectivity and data-driven insights. In this post, I will explore the current and future trends driving the adoption of IIoT, the challenges organizations face in its implementation, and the abundant opportunities it presents for enhancing operational efficiency and unlocking new possibilities.
Trends in the IIoT
Several key trends are pushing industries toward a more connected and efficient future. Some of these trends include:
Greater adoption: IIoT is experiencing a wave in adoption across industries as organizations recognize its power to revolutionize operations, boost productivity, and enable smarter decision-making.
5G optimization: The development of 5G networks promises to supercharge the IIoT by delivering ultra-low latency, high bandwidth, and reliable connectivity, empowering real-time data interpretation and response.
Increased flexibility: IIoT solutions are becoming more flexible, allowing seamless integration with existing infrastructure and offering scalability to accommodate evolving business needs.
Combining AI and duplicating datasets: The blending of artificial intelligence (AI) and duplicating datasets is unlocking new possibilities for the IIoT. By creating dataset replicas of physical assets, organizations can simulate, monitor, and optimize operations in real time, driving efficiency and advanced predictive maintenance.
Cyber security advancements: As the IIoT expands, cyber security advancements are necessary for safeguarding critical data and infrastructure. Robust measures such as encryption, authentication, and secure protocols are being refined to protect against potential threats.
Challenges in IIoT implementation
The implementation of IIoT comes with its fair share of challenges for industries.
Effectively managing and securing the vast amount of data generated by IIoT devices, for example, is a critical challenge. Organizations must enforce robust data storage, encryption, access control mechanisms, and data governance practices to ensure data integrity and privacy.
Reliable and seamless connectivity between devices, systems, and platforms is also crucial for the success of IIoT implementations. Organizations must address connectivity challenges such as network coverage, latency, and signal interference to ensure uninterrupted data flow.
Additionally, integrating IIoT technology with existing legacy infrastructure can be complex. Compatibility issues, interoperability challenges, and retrofitting requirements must be fully addressed to ensure painless integration and coexistence.
Opportunities in IIoT implementation
The implementation of IIoT presents vast opportunities for businesses, such as:
Real-time asset tracking: IIoT allows for real-time tracking of assets throughout the production process, ensuring location visibility and hardware traceability. By monitoring asset location, condition, and usage, organizations can optimize their use of assets, minimize losses, and boost operational efficiency.
Quality assurance enhancements: Engaging IIoT technologies such as sensors and data analytics, organizations can enhance quality assurance by continuously monitoring production parameters, deducing anomalies, and minimizing defects.
Proactive decision-making: IIoT enables real-time remote monitoring of manufacturing processes, allowing for proactive decision-making, reducing downtime, and optimizing resource allocation. Additionally, IIoT facilitates predictive maintenance by leveraging data from connected devices. By proactively revealing equipment failures and adjusting maintenance requirements, organizations can reduce or eliminate unplanned downtime and optimize maintenance schedules.
IIoT empowers real-time tracking of inventory levels, automating reordering processes, reducing stock outages, and optimizing inventory management practices, leading to improved profits and enhanced customer satisfaction.
Navigating the IIoT landscape presents both challenges and opportunities. As organizations adopt IIoT technologies, they need to address challenges related to secure data storage, connectivity, and integration with legacy infrastructure. However, by overcoming these challenges, organizations can unlock opportunities such as remote monitoring of operations, improved quality control, predictive maintenance, efficient inventory management, and enhanced asset tracking.
Click here for more on seizing the opportunities of the IIoT.
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.
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.
Imagine 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.
When looking at flexible manufacturing, what first comes to mind are the challenges of handling product changeovers. It is more and more common for manufacturers to produce multiple products on the same production line, as well as to perform multiple operations in the same space.
Accomplishing this and making these machines more flexible requires changing machine parts to allow for different stages in the production cycle. These interchangeable parts are all throughout a plant: die changes, tooling changes, fixture changes, end-of-arm tooling, and more.
When swapping out these interchangeable parts it is crucial you can identify what tooling is in place and ensure that it is correct.
ID without RFID
When it comes to identifying assets in manufacturing today, typically the first option companies consider is Radio-Frequency Identification (RFID). Understandably so, as this is a great solution, especially when tooling does not need an electrical connection. It also allows additional information beyond just identification to be read and written on the tag on the asset.
It is more and more common in changeover applications for tooling, fixtures, dies, or end-of-arm tooling to require some sort of electrical connection for power, communication, I/O, etc. If this is the case, using RFID may be redundant, depending on the overall application. Let’s consider identifying these changeable parts without incurring additional costs such as RFID or barcode readers.
Hub ID with IO-Link
In changeover applications that use IO-Link, the most common devices used on the physical tooling are IO-Link hubs. IO-Link system architectures are very customizable, allowing great flexibility to different varieties of tooling when changeover is needed. Using a single IO-Link port on an IO-Link master block, a standard prox cable, and hub(s), there is the capability of up to:
30 Digital Inputs/Outputs or
14 Digital Inputs/Outputs and Valve Manifold Control or
8 Digital Inputs/Outputs and 4 Analog Voltage/Current Signals or
8 Analog Input Signals (Voltage/Current, Pt Sensor, and Thermocouple)
When using a setup like this, an IO-Link 1.1 hub (or any IO-Link 1.1 device) can store unique identification data. This is done via the Serial Number Parameter and/or Application Specific Tag Parameter. They act as a 16- or 32-byte memory location for customizable alphanumeric information. This allows for tooling to have any name stored within that memory location. For example, Fixture 44, Die 12, Tool 78, EOAT 123, etc. Once there is a connection, the controller can request the identification data from the tool to ensure it is using the correct tool for the upcoming process.
By using IO-Link, there are a plethora of options for changeover tooling design, regardless of various I/O requirements. Also, you can identify your tooling without adding RFID or any other redundant hardware. Even so, in the growing world of Industry 4.0 and the Industrial Internet of Things, is this enough information to be getting from your tooling?
In addition to the diagnostics and parameter setting benefits of IO-Link, there are now hub options with condition monitoring capabilities. These allow for even more information from your tooling and fixtures like:
Vibration detection
Internal temperature monitoring
Voltage and current monitoring
Operating hours counter
Flexible manufacturing is no doubt a challenge and there are many more things to consider for die, tooling and fixture changes, and end-of-arm tooling outside of just ID. Thankfully, there are many solutions within the IO-Link toolbox.
The Industrial Internet of Things (IIoT) is becoming an indispensable part of the manufacturing industry, leading to real-time monitoring and an increase in overall equipment effectiveness (OEE) and productivity. Since the machines are being connected to the intranet and sometimes to the Internet for remote monitoring, this brings a set of challenges and security concerns for these now-connected devices.
What causes security to be so different between OT and IT?
Operational Technology (OT) manufacturing equipment is meant to run 24/7. So, if a bug is found that requires a machine to be shut down for an update, that stop causes a loss in productivity. So, manufacturers can’t rely on updating operational equipment as frequently as their Information Technology (IT) counterparts.
Additionally, the approach of security for OT machines has largely been “security through obscurity.” If, for example, a machine is not connected to the network, then the only way to access the hardware is to access it physically.
Another reason is that OT equipment can have a working lifetime that spans decades, compared to the typical 2-5-year service life of IT equipment. And when you add new technology, the old OT equipment becomes almost impossible to update to the latest security patches without the effort and expense of upgrading the hardware. Since OT equipment is in operation for such a long time, it makes sense that OT security focuses on keeping equipment working continuously as designed, where IT is more focused on keeping data available and protected.
These different purposes makes it hard to implement the IT standard on OT infrastructure. But that being said, according to Gartner’s 80/20 rule-of-thumb, 80 percent of security issues faced in the OT environment are the same faced by IT, while 20 percent are domain specific on critical assets, people, or environment. With so many security issues in common, and so many practical differences, what is the best approach?
The solution
The difference in operation philosophy and goals between IT and OT systems makes it necessary to consider IIoT security when implementing the systems carefully. Typical blanket IT security systems can’t be applied to OT systems, like PLCs or other control architecture, because these systems do not have built-in security features like firewalls.
We need the benefits of IIoT, but how do we overcome the security concerns?
The best solution practiced by the manufacturing industry is to separate these systems: The control side is left to the existing network infrastructure, and IT-focused work like monitoring is carried out on a newly added infrastructure.
The benefit of this method is that the control side is again secured by the method it was designed for – “security by obscurity” – and the new monitoring infrastructure can take advantage of the faster developments and updates of the IT lifecycle. This way, the operations and information technology operations don’t interfere with each other.
With more and more customers getting onboard with IIoT applications in their plants, a new era of efficiency is lurking around the corner. Automation for maintenance is on the rise thanks to a shortage of qualified maintenance techs coinciding with a desire for more efficient maintenance, reduced downtime, and the inroads IT is making on the plant floor. Predictive Maintenance and Predictive Analytics are part of almost every conversation in manufacturing these days, and often the words are used interchangeably.
This blog is intended to make the clear distinction between these phrases and put into perspective the benefits that maintenance automation brings to the table for plant management and decision-makers, to ensure they can bring to their plants focused innovation and boost efficiencies throughout them.
Before we jump into the meat of the topic, let’s quickly review the earlier stages of the maintenance continuum.
Reactive and Preventative approaches
The Reactive and Preventative approaches are most commonly used in the maintenance continuum. With a Reactive approach, we basically run the machine or line until a failure occurs. This is the most efficient approach with the least downtime while the machine or line runs. Unfortunately, when the machine or line comes to a screeching stop, it presents us with the most costly of downtimes in terms of time wasted and the cost of machine repairs.
The Preventative approach calls for scheduled maintenance on the machine or line to avoid impending machine failures and reduce unplanned downtimes. Unfortunately, the Preventative maintenance strategy does not catch approximately 80% of machine failures. Of course, the Preventative approach is not a complete waste of time and money; regular tune-ups help the operations run smoother compared to the Reactive strategy.
Predictive Maintenance vs. Predictive Analytics
As more companies implement IIoT solutions, data has become exponentially more important to the way we automate machines and processes within a production plant, including maintenance processes. The idea behind Predictive Maintenance (PdM), aka condition-based maintenance, is that by frequently monitoring critical components of the machine, such as motors, pumps, or bearings, we can predict the impending failures of those components over time. Hence, we can prevent the failures by scheduling planned downtime to service machines or components in question. We take action based on predictive conditions or observations. The duration between the monitored condition and the action taken is much shorter here than in the Predictive Analytics approach.
Predictive Analytics, the next higher level on the maintenance continuum, refers to collecting the condition-based data over time, marrying it with expert knowledge of the system, and finally applying machine learning or artificial intelligence to predict the event or failure in the future. This can help avoid the failure altogether. Of course, it depends on the data sets we track, for how long, and how good our expert knowledge systems are.
So, the difference between Predictive Maintenance and Predictive Analytics, among other things, is the time between condition and action. In short, Predictive Maintenance is a stepping-stone to Predictive Analytics. Once in place, the system monitors and learns from the patterns to provide input on improving the system’s longevity and uptime. Predictive Maintenance or Preventative Maintenance does not add value in that respect.
While Preventative Maintenance and Predictive Maintenance promises shorter unplanned downtimes, Predictive Analytics promises avoidance of unplanned downtime and the reduction of planned downtime.
The first step to improving your plant floor OEE is with monitoring the conditions of the critical assets in the factory and collecting data regarding the failures.
If you have ever walked through a stamping department at a metal forming facility, you have heard the rhythmic sound of the press stamping out parts, thump, thump. The stamping department is the heart manufacturing facility, and the noise you hear is the heartbeat of the plant. If it stops, the whole plant comes to a halt. With increasing demands for higher production rates, less downtime, and reduction in bad parts, stamping departments are under ever-increasing pressure to optimize the press department through die protection and error-proofing programs.
The die-protection risk assessment team
The first step in implementing or optimizing a die protection program is to perform a die-protection risk assessment. This is much like risk assessments conducted for safety applications, except they are done for each die set. To do this, build a team of people from various positions in the press department like tool makers, operators, and set-up teams.
Once this team is formed, they can help identify any incidents that could occur during the stamping operations for each die set and determine the likelihood and the severity of possible harm. With this information, they can identify which events have a higher risk/severity and determine what additional measures they should implement to prevent these incidents. An audit is possible even if there are already some die protection sensors in place to determine if there are more that should be added and verify the ones in place are appropriate and effective.
The top 4 die processes to check
The majority of quality and die protection problems occur in one of these three areas: material feed, material progression, and part- and slug-out detections. It’s important to monitor these areas carefully with various sensor technologies.
Material feed
Material feed is perhaps the most critical area to monitor. You need to ensure the material is in the press, in the correct location, and feeding properly before cycling the press. The material could be feeding as a steel blank, or it could come off a roll of steel. Several errors can prevent the material from advancing to the next stage or out of the press: the feed can slip, the stock material feeding in can buckle, or scrap can fail to drop and block the strip from advancing, to name a few. Inductive proximity sensors, which detect iron-based metals at short distances, are commonly used to check material feeds.
Material progression
Material progression is the next area to monitor. When using a progressive die, you will want to monitor the stripper to make sure it is functioning and the material is moving through the die properly. With a transfer die, you want to make sure the sheet of material is nesting correctly before cycling the press. Inductive proximity sensors are the most common sensor used in these applications, as well.
Here is an example of using two inductive proximity sensors to determine if the part is feeding properly or if there is a short or long feed. In this application, both proximity sensors must detect the edge of the metal. If the alignment is off by just a few millimeters, one sensor won’t detect the metal. You can use this information to prevent the press from cycling to the next step.
Short feed, long feed, perfect alignment
Part-out detection
The third critical area that stamping departments typically monitor is part-out detection, which makes sure the finished part has come out of the stamping
area after the cycle is complete. Cycling the press and closing the tooling on a formed part that failed to eject can result in a number of undesirable events, like blowing out an entire die section or sending metal shards flying into the room. Optical sensors are typically used to check for part-out, though the type of photoelectric needed depends on the situation. If the part consistently comes out of the press at the same position every time, a through-beam photo-eye would be a good choice. If the part is falling at different angles and locations, you might choose a non-safety rated light grid.
Slug-ejection detection
The last event to monitor is slug ejection. A slug is a piece of scrap metal punched out of the material. For example, if you needed to punch some holes in metal, the slug would be the center part that is knocked out. You need to verify that the scrap has exited the press before the next cycle. Sometimes the scrap will stick together and fail to exit the die with each stroke. Failure to make sure the scrap material leaves the die could affect product quality or cause significant damage to the press, die, or both. Various sensor types can ensure proper scrap ejection and prevent crashes. The picture below shows a die with inductive ring sensors mounted in it to detect slugs as they fall out of the die.
Just like it is important to get regular checkups at the doctor, performing regular die-protection assessments can help you make continuous improvements that can increase production rates and reduce downtime. Material feed, material progression, part-out and slug-out detection are the first steps to optimize, but you can expand your assessments to include areas like auxiliary equipment. You can also consider smart factory solutions like intelligent sensors, condition monitoring, and diagnostics over networks to give you more data for preventative maintenance or more advanced error-proofing. The key to a successful program is to assemble the right team, start with the critical areas listed above, and learn about new technologies and concepts that are becoming available to help you plan ways to improve your stamping processes.
The rise of many players in manufacturing automation, along with factories’ growing adoption of Industrial Internet of Things (IIoT) and automation solutions, present a suitable environment for open-source software. This software is a value-adding solution for manufacturers, regardless of their operation technology and management requirements, due to the customization, resiliency, scalability, accessibility, cost-effectiveness, and quality it allows.
Customization
Software developers who use open-source code provide software with a core code that establishes specific features and allows users to access it and make changes as necessary. The process is much like being able to complete an author’s writing prompt or change the end of a story. Unlike a closed system that locks users in, open-source allows them to adapt and modify the code to meet a particular need or application.
This add-on coding system provides endless customization. It enables communities (i.e., users) to add or remove features beneficial in an integration phase, such as features for user testing or to find the best solution for a machine.
Customization is also valuable regarding data visualizations; users can develop dashboards and visuals that best describe their operations. Suppose a sensor provides real-time condition monitoring data over a particular machine. In that case, it’s possible to customize the code supporting the software that gathers and processes the data for specific parameters or to calculate specific values.
Resiliency
Additionally, open-source code is resilient to change because it can be modified quickly. The ability to quickly add or remove features and adapt to cyber environments or specific applications also makes it volatile. Like exposure to pathogens can help strengthen an immune response to said pathogens, so can an open-source code be made stronger by its exposure to different environments and applications to be ready to face cybersecurity threats. Implementing an open code isn’t any less risky (cybersecurity-wise) than closed codes due to the testing and enhancements made by so many coders or programmers. However, it is up to the implementer to use the same rules that apply to other closed source software. The implementer must be aware of the code’s source and avoid code from non-reputable sources who could have modified it with negative intentions. Overall, the code is resilient, adaptable, and agile to adapt given a new environment.
Scalability
The add-on and customization aspects of open-source also allow the code to be highly scalable. This scalable implementation happens in two dimensions: adoption timeline and application-based. Both are important to guarantee user acceptance and that it meets the operation and application requirements. Regarding the adoption timeline, scalability allows modification of the software and code to meet users’ expectations. Open-sourced code enables the implementation of features for user testing and feedback. The ultimate solution will include multiple iterations to meet the users’ needs and fulfill operation expectations.
On the other hand, this code is scalable based on the application(s), such as working on different machines, multiples of the same machine with different purposes, or adding/dropping features for specific uses. Say, for example, there are three of the same machine (A, B, and C), but they are in different environments. Machine A is in an environment that is 28°F , B is at room temperature, and C is exposed to constant wash-down. In this case, the condition monitoring software defines the acceptable parameters for each scenario, avoiding false alarms from erroneous triggers. In this example, the base code is adapted to include specific features based on the application.
Accessibility
In general, cost-effective and high-quality open-source code is available online. There are additional resources such as free coding tutorials that don’t require any licenses as well. Moreover, when programmers update an open code, they must make the new version available again, ensuring that the code is accessible and up to date.
Cost-effectiveness and quality
Regarding cost-effectiveness, using community open-source code significantly reduces the cost of developing, integrating, and testing software built in-house. It also reduces the implementation time and makes for better production operations. Essentially, it is high-quality, reliable code created by trusted sources for multiple coders and users.
“The application drives the technology” mantra is at the heart of open-source software development—a model where source code is available for community members to use, modify, and share. IIoT enablers and providers in the manufacturing industry own a particular solution that is then available for manufacturers to adapt to their specific operational requirements. With the increasing adoption of data-collecting technologies, it is in manufacturers’ best interest to seek software providers who grant them the flexibility to adjust software solutions to meet their specific needs. Automation is a catalyst for data-driven operation and maintenance.
While RFID technology has been around for almost 70 years, the last decade has seen widespread acceptance, particularly in automated manufacturing. It is now deployed for various common applications, including automatic data transfer in machining operations, quality control in production, logistics traceability, and inventory control.
Key industry terms such as Industry 4.0 and the Industrial Internet of Things (IIOT) were once perceived as distant concepts crafted by marketing teams, seemingly disconnected from the practicalities of the plant floor. However, these buzzwords emerged as a response to manufacturing organizations worldwide recognizing the imperative for enhanced visibility into their operations. While automation hardware and supporting infrastructure have swiftly progressed in response to this demand, there remains a significant need for software that can effectively transform raw data into actionable data. This software must offer interactive feedback through reporting, dashboards, and real-time indicators.
