Does Your Stamping Department Need a Checkup? Try a Die-Protection Risk Assessment

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.

Maximize the Benefits of Open-Source Code in Manufacturing Software

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.

Control Meets IIoT, Providing Insights into a New World

In manufacturing and automation control, the programmable logic controller (PLC) is an essential tool. And since the PLC is integrated into the machine already, it’s understandable that you might see the PLC as all that you need to do anything in automation on the manufacturing floor.

Condition monitoring in machine automation

For example, process or condition monitoring is emerging as an important automation feature that can help ensure that machines are running smoothly. This can be done by monitoring motor or mechanical vibration, temperature or pressure. You can also add functionality for a machine or line configuration or setup by adding sensors to verify fixture locations for machine configuration at changeovers.

One way to do this is to wire these sensors to the PLC and modify its code and use it as an all-in-one device. After all, it’s on the machine already. But there’s a definite downside to using a PLC this way. Its processing power is limited, and there are limits to the number of additional processes and functions it can run. Why risk possible complications that could impact the reliability of your control systems? There are alternatives.

External monitoring and support processes

Consider using more flexible platforms, such as an edge gateway, Linux, and IO-Link. These external sources open a whole new world of alternatives that provide better reliability and more options for today and the future. It also makes it easier to access and integrate condition monitoring and configuration data into enterprise IT/OT (information technology/operational technology) systems, which PLCs are not well suited to interface with, if they can be integrated at all.

Here are some practical examples of this type of augmented or add-on/retrofit functionality:

      • Motor or pump vibration condition monitoring
      • Support-process related pressure, vibration and temperature monitoring
      • Monitoring of product or process flow
      • Portable battery based/cloud condition monitoring
      • Mold and Die cloud-based cycle/usage monitoring
      • Product changeover, operator guidance system
      • Automatic inventory monitoring warehouse system

Using external systems for these additional functions means you can readily take advantage of the ever-widening availability of more powerful computing systems and the simple connectivity and networking of smart sensors and transducers. Augmenting and improving your control systems with external monitoring and support processes is one of the notable benefits of employing Industrial Internet of Things (IIoT) and Industry 4.0 tools.

The ease of with which you can integrate these systems into IT/OT systems, even including cloud-based access, can dramatically change what is now available for process information-gathering and monitoring and augment processes without touching or effecting the rudimentary control system of new or existing machines or lines. In many cases, external systems can even be added at lower price points than PLC modification, which means they can be more easily justified for their ROI and functionality.

How Industrial RFID Can Reduce Downtime in Your Stamping Department

The appliance industry is growing at record rates. The increase in consumer demand for new appliances is at an all-time high and is outpacing current supply. Appliance manufacturers are increasing production to catch up with this demand. This makes the costs associated with downtime even higher than normal. But using industrial RFID can allow you to reduce downtime in your stamping departments and keep production moving.

Most major household appliance manufacturers have large stamping departments as part of their manufacturing process. I like to think of the stamping department as the heart of the manufacturing plant. If you have ever been in a stamping department while they are stamping out metal parts, then you understand. The thumping and vibration of the press at work is what feeds the rest of the plant.  I was in a plant a few weeks ago meeting with an engineer in the final assembly area. It was oddly quiet in that area, so I asked what was going on. He said they’d sent everyone home early because one of their major press lines went down unexpectedly. Every department got sent home because they did not have the pieces and parts needed to make the final product. That is how critical the stamping departments are at these facilities.

In past years, this wasn’t as critical, because they had an inventory of parts and finished product. But the increase in demand over the last two years depleted that inventory. They need ways to modernize the press shop, including implementing smarter products like devices with Industry 4.0 capabilities to get real-time data on the equipment for things like analytics, OEE (Overall Equipment Effectiveness), preventative maintenance, downtime, and more error proofing applications.

Implementing Industrial RFID

One of the first solutions many appliance manufacturers implement in the press department is traceability using industrial RFID technology. Traceability is typically used to document and track different steps in a process chain to help reduce the costs associated with non-conformance issues. This information is critical when a company needs to provide information for proactive product recalls, regulatory compliance, and quality standards. In stamping departments, industrial RFID is often used for applications like asset tracking, machine access control, and die identification. Die ID is not only used to identify which die is present, but it can also be tied back to the main press control system to make sure the correct job is loaded.

need for RFID in appliance stamping
This shows an outdated manual method using papers that are easily lost or destroyed.
appliance stamping can be improved by RFID
This image shows an identification painted on a die, which can be easily destroyed.

Traditionally, most companies have a die number either painted on the die or they have a piece of paper with the job set up attached to the die. I cannot tell you how many times I have seen these pieces of paper on the floor. Press departments are pretty nasty environments, so these pieces of paper get messed up pretty quickly. And the dies take a beating, so painted numbers can easily get rubbed or scratched off.

Implementing RFID for die ID is a simple and affordable solution to this problem. First, you would attach an RFID tag with all of the information about the job to each die. You could also write maintenance information about the die to this tag, such as when the die was last worked on, who last worked on it, or process information like how many parts have been made on this die.
Next, you need to place an antenna. Most people mount the antenna to one of the columns of the press where the tag would pass in front of it as it is getting loaded into the die. The antenna would be tied back to a processor or IO-Link master if using IO-Link. The processor or IO-Link master would communicate with the main press control system. As the die is set in the press, the antenna reads the tag and tells the main control system which die is in place and what job to load.

In a stamping department you might find several large presses. Each press will have multiple dies that are associated with each press. Each die is set up to form a particular part. It is unique to the part it is forming and has its own job, or recipe, programmed in the main press control system. Many major stamping departments still use manual operator entry for set up and to identify which tools are in the press. But operators are human, so it is very easy to punch in the wrong number, which is why RFID is a good, automated solution.

In conclusion

When I talk with people in stamping departments, they tell me one of the main reasons a crash occurs is because information was entered incorrectly by the operator during set up. Crashes can be expensive to repair because of the damage to the tooling or press, but also because of the downtime associated. Establishing a good die setup process is critical to a stamping department’s success and implementing RFID can eliminate many of these issues.

Choosing the Right Sensor for Your Welding Application

Automotive structural welding at tier suppliers can destroy thousands of sensors a year in just one factory. Costs from downtime, lost production, overtime, replacement time, and material costs  eat into profitability and add up to a big source of frustration for automated and robotic welders. When talking with customers, they often list inductive proximity sensor failure as a major concern. Thousands and thousands of proxes are being replaced and installations are being repaired every day. It isn’t particularly unusual for a company to lose a sensor on every shirt in a single application. That is three sensors a day  — 21 sensors a week — 1,100 sensors a year failing in a single application! And there could be thousands of sensor installations in an  automotive structural assembly line. When looking at the big picture, it is easy to see how this impacts the bottom line.

When I work with customers to improve this, I start with three parts of a big equation:

  • Sensor Housing
    Are you using the right sensor for your application? Is it the right form factor? Should you be using something with a coating on the housing? Or should you be using one with a coating on the face? Because sensors can fail from weld spatter hitting the sensor, a sensor with a coating designed for welding conditions can greatly extend the sensor life. Or maybe you need loading impact protection, so a steel face sensor may be the best choice. There are more housing styles available now than ever. Look at your conditions and choose accordingly.
  • Bunkering
    Are you using the best mounting type? Is your sensor protected from loading impact? Using a protective block can buffer the sensor from the bumps that can happen during the application.
  • Connectivity
    How is the sensor connected to the control and how does that cable survive? The cable is often the problem but there are high durability cable solutions, including TPE jacketed cables, or sacrificial cables to make replacement easier and faster.

When choosing a sensor, you can’t only focus on whether it can fulfill the task at hand, but whether it can fulfill it in the environment of the application.

For more information, visit Balluff.com

Getting Condition Data From The Shop Floor to Your Software

IIoT (Industrial Internet of Things)  is becoming more mainstream, leading to more vendors implementing innovative monitoring capabilities in the new generation of sensors. These sensors are now multifunctional and provide a host of additional features such as self-monitoring.

With these intelligent sensors, it is possible to set up a system that enables continuous monitoring of the machines and production line. However, the essential requirement to use the provided data for analysis and condition monitoring for preventative and predictive maintenance is to get it from the shop floor to the MES, ERP, or other analysis software suites.

There are a variety of ways this can be done. In this post we will look at a few popular ways and methods to do so.

The most popular and straightforward implementation is using a REST API(also known as RESTful API). This has been the de facto standard in e consumer space to transport data. It allows multiple data formats to be transferred, including multimedia and JSON (Javascript Object Notation)

This has certain disadvantages like actively polling for the data, making it unsuitable for a spotty network, and having high packet loss.

MQTT(Message Queuing Telemetry Transport) eliminates the above problem. It’s very low bandwidth and works excellent on unreliable networks as it works on a publish/subscribe model. This allows the receiver to passively listen for the data from the broker. The broker only notifies when there is a change and can be configured to have a Quality of Service(QoS) to resend data if one of them loses connection. This has been used in the IoT world for a long time has become a standard for data transport, so most of software suits have this feature inbuilt.

The third option is to use OPCUA, which is the standard for M2M communication. OPCUA provides additional functionality over MQTT as it was developed with machine communication in mind. Notably, inbuilt encryption allows for secure and authenticated communication.

In summary, below is a comparison of these protocols.

A more detailed explanation can be found for these standards :

REST API : https://www.redhat.com/en/topics/api/what-is-a-rest-api

MQTT : https://mqtt.org/

OPCUA : https://opcfoundation.org/about/opc-technologies/opc-ua/

Turning Big Data into Actionable Data

While RFID technology has been available for almost seventy years, the last decade has seen widespread acceptance, specifically in automated manufacturing. Deployed for common applications like automatic data transfer in machining operations, quality control in production, logistics traceability and inventory control, RFID has played a major role in the evolution of data collection and handling. With this evolution has come massive amounts of data that can ultimately hold the key to process improvement, quality assurance and regulatory compliance. However, the challenge many organizations face today is how to turn all that data into actionable data.

Prominent industry buzzwords like Industry 4.0 and the Industrial Internet of Things (IIOT) once seemed like distant concepts conjured up by a marketing team far away from the actual plant floor, but those buzzwords are the result of manufacturing organizations around the globe identifying the need for better visibility into their operations. Automation hardware and the infrastructure that supports it has advanced rapidly due to this request, but software that turns raw data into actionable data is still very much in demand. This software needs to provide interactive feedback in the form of reporting, dashboards, and real time indicators.

The response to the demand will bring vendors from other industries and start-ups, while a handful of familiar players in automation will step up to the challenge. Competition keeps us all on our toes, but the key to filling the software gap in the plant is partnering with a vendor who understands the needs on the plant floor. So, how do you separate the pretenders from the contenders? I compiled a check list to help.

Does the prospective vendor have:

  • A firm understanding that down time and scrap need to be reduced or eliminated?
  • A core competency in automation for the plant floor?
  • Smart hardware devices like RFID and condition monitoring sensors?
  • A system solution that can collect, analyze, and transport data from the device to the cloud?
  • A user-friendly interface that allows interaction with mobile devices like tablets and phones?
  • The capability to provide customized reports to meet the needs of your organization?
  • A great industry reputation for quality and dependability?
  • A chain of support for pre-sales, installation, and post-sales support?
  • Examples of successful system deployments?
  • The willingness to develop or modify current devices to address your specific needs?

If you can check the box for all of these, it is a safe bet you are in good hands. Otherwise, you’re rolling the dice.

How Condition Monitoring has Evolved and Its Role in IIoT

In recent years, as IIoT and Industry 4.0 have become part of our everyday vocabulary, we’ve also started hearing more about condition monitoring, predictive maintenance (PdM) and predictive analytics. Sometimes, we use these terms interchangeably as well. Strictly speaking, condition monitoring is a root that enables both predictive maintenance and predictive analytics. In today’s blog we will brush up a little on condition monitoring and explore its lineage.

Equipment failures have been around since the beginning of time. Over the years, through observation (collecting data) and brute-force methods, we learned that from time-to-time every piece of equipment needs some TLC. Out of this understanding, maintenance departments came to existence, and there we started having experts that could tell based on touch, smell and noise what is failing or what has gone wrong.

Figure 1: Automation Pyramid

Then we started automating the maintenance function either as a preventative measure (scheduled maintenance) or through some automated pieces of equipment that would collect data and provide alerts about a failure. We proudly call these SCADA systems – Supervisory Control and Data Acquisition. Of course, these systems did not necessarily prevent failures, but help curtail them.  If we look at the automation pyramid, the smart system at the bottom is a PLC and all the sensors are what we call “dumb sensors”. So, that means, whatever information the SCADA system gets would be filtered by the PLC. PLCs were/have been/ and are always focused on the process at hand; they are not data acquisition equipment. So, the data we receive in the SCADA system is only as good as the PLC can provide. That means the information is primarily about processes. So, the only alerts maintenance receives is when the equipment fails, and the process comes to a halt.

With the maintenance experts who could sense impending failures becoming mythological heroes, and  SCADA systems that cannot really tell us the story about the health of the machines, once again, we are looking at condition monitoring with a fresh set of eyes.

Sensors are at the grass root level in the automation pyramid, and until the arrival of IO-Link technology, these sensors were solely focused on their purpose of existence; object detection, or measurement of some kind. The only information one could gather from these sensors was ON/OFF or a signal of 4-20mA, 0-10V, and so on. Now, things are different, these sensors are now becoming pretty intelligent and they, like nosy neighbors, can collect more information about their own health and the environment. These intelligent sensors can utilize IO-Link as a communication to transfer all this information via a gateway module (generally known as IO-Link master) to whomever wants to listen.

Figure 2: IO-Link enabled Balluff photo-eye

The new generation of SCADA systems can now collect information not only from PLCs about the process health, but also from individual devices. For example, a photo-eye can measure the intensity of the reflected light and provide an alert if the intensity drops beyond a certain level, indicating a symptom of pending failure. Or a power supply inside the cabinet providing an alert to the supervisory control about adverse conditions due to increase temperature or humidity in the cabinet. These types of alerts about the symptoms help maintenance prevent unplanned downtime on the plant floor and make factories run more efficiently with reduced scrap, reduced down-time and reduced headaches.

Figure 3: The Next Generation Condition Monitoring

There are many different condition monitoring architectures that can be employed, and we will cover that in my next blog.

Improve OEE, Save Costs with Condition Monitoring Data

When it comes to IIOT (Industrial Internet of Things) and the fourth industrial revolution, data has become exponentially more important to the way we automate machines and processes within a production plant. There are many different types of data, with the most common being process data. Depending on the device or sensor, process data may be as simple as the status of discrete inputs or outputs but can be as complex as the data coming from radio frequency identification (RFID) data carriers (tags). Nevertheless, process data has been there since the beginning of the third industrial revolution and the beginning of the use of programmable logic controllers for machine or process control.

With new advances in technology, sensors used for machine control are becoming smarter, smaller, more capable, and more affordable. This enables manufacturers of those devices to include additional data essential for IIOT and Industry 4.0 applications. The latest type of data manufacturers are outputting from their devices is known as condition monitoring data.

Today, smart devices can replace an entire system by having all of the hardware necessary to collect and process data, thus outputting relative information directly to the PLC or machine controller needed to monitor the condition of assets without the use of specialized hardware and software, and eliminating the need for costly service contracts and being tied to one specific vendor.

A photo-electric laser distance sensor with condition monitoring has the capability to provide more than distance measurements, including vibration detection. Vibration can be associated with loose mechanical mounting of the sensor or possible mechanical issues with the machine that the sensor is mounted. That same laser distance sensor can also provide you with inclination angle measurement to help with the installation of the sensor or help detect when there’s a problem, such as when someone or something bumps the sensor out of alignment. What about ambient data, such as humidity? This could help detect or monitor for moisture ingress. Ambient pressure? It can be used to monitor the performance of fans or the condition of the filter elements on electrical enclosures.

Having access to condition monitoring data can help OEMs improve sensing capabilities of their machines, differentiating themselves from their competition. It can also help end users by providing them with real time monitoring of their assets; improving overall equipment efficiency and better predicting  and, thereby, eliminating unscheduled and costly machine downtime. These are just a few examples of the possibilities, and as market needs change, manufacturers of these devices can adapt to the market needs with new and improved functions, all thanks to smart device architecture.

Integrating smart devices to your control architecture

The most robust, cost effective, and reliable way of collecting this data is via the IO-Link communication protocol; the first internationally accepted open, vendor neutral, industrial bi-directional communications protocol that complies with IEC61131-9 standards. From there, this information can be directly passed to your machine controller, such as PLC, via fieldbus communication protocols, such as EtherNET/Ip, ProfiNET or EtherCAT, and to your SCADA / GUI applications via OPC/UA or JSON. There are also instances where wireless communications are used for special applications where devices are placed in hard to reach places using Bluetooth or WLAN.

In the fast paced ever changing world of industrial automation, condition monitoring data collection is increasingly more important. This data can be used in predictive maintenance measures to prevent costly and unscheduled downtime by monitoring vibration, inclination, and ambient data to help you stay ahead of the game.

Injection Molding: Ignore the Mold, Pay the Price

Are you using a contract molding company to make your parts? Or are you doing it in house, but with little true oversight and management reporting on your molds? As a manufacturer, you can spend as much on a mold as you might for an economy, luxury or even a high-performance car. The disappointing difference is that YOU get to drive the car, while your molder or mold shop gets to drive your mold. How do you know if your mold is being taken care of as a true tooling investment and not being used as though it were disposable, or like the car analogy, like the Dukes of Hazzard used the General Lee?

What steps can you take in regard to using and maintaining a mold in production that can help guarantee your company’s ROI? How can you ensure your mold is going to produce the needed parts and provide or exceed the longevity required?

It is important for any manufacturer to understand the need for the cleaning and repair required for proper tool maintenance. The condition of your injection mold affects the quality of the plastic components produced. To keep a mold in the best working order, maintenance is critical not only when issues arise, but also routinely over time.

In the case of injection molds specifically, there are certain checks and procedures that should be performed regularly. An example being that mold cavities and gating should be routinely inspected for wear or damage. This is as important as keeping the injection system inspected and lubricated, and ensuring all surfaces are cleaned and sprayed with a rust preventative.

Figure 1 An example of the mold usage process.

The unfortunate reality is that some molders wait until part quality problems arise or the tool becomes damaged to do maintenance. One of the biggest challenges with injection molders is being certain that your molds are being run according to the maintenance requirements. Running a mold too long and waiting until problems arise to perform routine maintenance or refurbish a mold can result in added expense, supply/stock issues, longer time to market and even loss of the mold. However, when molders have a clear indication of maintenance and production timing, and follow the maintenance procedures in place, production times and overall costs can decrease.

Figure 2 Balluff add-on Mold ID monitoring and traceability system.

Creating visibility and accuracy into this maintenance timing is something today’s automation technology can now address. With todays modern, industrial automation technology, visibility and traceability can be added to any mold machine, regardless of machine age, manufacturer and manufacturing environment.

With the modern networked IIoT (industrial internet of things)-based monitoring and traceability system solutions available today, the mold can be monitored on the machine in real-time and every shot is recorded and kept on the mold itself using, for example, an assortment of industrial RFID tag options mounted directly on the mold. Mold shot count information can be tracked and kept on the mold and can be reported to operations or management using IIoT-based software running at the molder or even remotely using the internet at your own facility, giving complete visibility and insight into the mold’s status.

Figure 3 Balluff IIoT-based Connected Mold ID reporting and monitoring software screens.

Traceability systems record not only the shot count but can provide warning and alarm shot count statuses locally using visual indicators, such as a stack light, as the mold nears its maintenance time. Even the mold’s identification information and dynamic maintenance date (adjusted continuously based on current shot count) are recorded on the RFID tag for absolute tracability and can be reported in near real-time to the IIoT-based software package.

Advanced automation technology can bring new and needed insights into your mold shop or your molder’s treatment of your molds. It adds a whole new level of reliability and visibility into the molding process. And you can use this technology to improve production up-time and maximize your mold investments.

For more information, visit https://www.balluff.com/en/de/industries-and-solutions/solutions-and-technologies/mold-id/connected-mold-id/