How Smart Plastics Are Changing Industrial Automation

Introduction

Unplanned downtime remains one of the most expensive challenges in industrial manufacturing. A machine failure does not only mean replacing a damaged component. It can interrupt an entire production line, delay deliveries, require emergency maintenance, create additional labour costs, and affect overall equipment effectiveness.

As factories become more automated, the consequences of component failure can become even greater. Modern production systems may contain hundreds of moving components operating continuously, often across multiple shifts. Identifying a potential failure before it stops production is therefore becoming an important objective for machine builders and plant operators.

This is where predictive maintenance and connected motion components are gaining attention.

igus® has developed its smart plastics approach around the idea of combining motion components with sensors, data, and software. The objective is to move maintenance from a reactive process toward a more predictable and data-driven approach.

Why Traditional Maintenance Strategies Are Changing

Industrial maintenance has traditionally followed three broad approaches.

The first is reactive maintenance: a component fails, and the maintenance team repairs or replaces it.

The second is preventive maintenance: components are serviced or replaced according to a fixed schedule, regardless of their actual condition.

The third is predictive maintenance: information about the component is continuously evaluated so that maintenance can be planned according to actual operating conditions.

Reactive maintenance can result in unexpected downtime. Preventive maintenance is more predictable, but it can sometimes mean replacing components before they have reached the end of their useful service life.

Predictive maintenance attempts to address both challenges by using operating data to determine when intervention is actually required.

According to igus®, its smart maintenance approach combines condition monitoring with predictive maintenance. Its i.Sense system can monitor component conditions, while i.Cee software can use sensor and operating data to calculate service life and determine appropriate maintenance times.

What Are Smart Plastics?

The term "smart plastics" refers to motion components that incorporate sensing and digital capabilities.

Traditional motion components perform a mechanical function. A bearing supports movement. An energy chain guides cables. A cable transfers power or data.

Smart versions of these components can additionally provide information about their condition.

Sensors can monitor parameters such as forces, temperature, vibration, or other indicators depending on the product and application. If predefined limits are exceeded, the system can generate an alert or provide information to the machine operator.

This creates a connection between the physical machine and its digital monitoring system.

Instead of waiting for a component to fail, manufacturers can receive information that helps them understand what is happening while the machine is operating.

Condition Monitoring vs Predictive Maintenance

These two terms are often used together, but they are not exactly the same.

Condition Monitoring

Condition monitoring focuses on the current condition of a component.

For example, sensors may detect that a force or vibration level has exceeded a defined threshold. The system can then issue an alert so that an operator or maintenance team can investigate.

This approach provides visibility into what is happening at the component level.

Predictive Maintenance

Predictive maintenance goes one step further.

Instead of simply reporting that a component has reached a warning threshold, predictive systems can combine sensor information with operating data to estimate remaining service life and recommend an appropriate maintenance time.

igus® refers to its predictive maintenance system as i.Cee. The company describes i.Cee as software that calculates the maximum service life of relevant products and determines an appropriate service time.

The distinction is important because the objective is not simply to generate more alerts.

The objective is to generate useful information that supports better maintenance decisions.

How Predictive Maintenance Can Reduce Unplanned Downtime

Consider an automated production line that operates continuously.

A cable carrier or energy supply system may experience repeated movement every time the machine completes a production cycle. Over time, mechanical wear can develop.

With a conventional maintenance strategy, the component may be inspected periodically or replaced after a predetermined number of operating hours.

With a smart maintenance system, the component can provide information about its operating condition.

If the system identifies a developing problem, maintenance personnel can potentially schedule an intervention before the component reaches a critical failure condition.

This changes the maintenance workflow.

Instead of:

Failure → Emergency stop → Diagnosis → Replacement → Restart

the process can become:

Condition monitoring → Early warning → Planned maintenance → Controlled replacement

That difference can be particularly valuable in automated production environments where unexpected downtime can affect multiple downstream processes.

The Role of i.Cee in Predictive Maintenance

igus®' i.Cee platform is designed to combine sensor information with software-based service-life calculations.

The system can collect information from smart components and use operating data to determine remaining service life. According to igus®, the technology can provide early warnings, help avoid unplanned downtime, reduce maintenance costs, and support greater utilisation of the service life of components.

The concept is particularly relevant for applications where components operate continuously or where failure would have significant consequences.

Automotive production is one example. Robotic systems, welding equipment, assembly machines, and energy supply systems may operate continuously in highly automated environments.

In such applications, maintenance planning is not simply about replacing a component.

It is about coordinating maintenance with the production schedule.

Why Data Alone Is Not Enough

One of the common misconceptions about predictive maintenance is that installing sensors automatically creates a predictive maintenance system.

It does not.

Sensors are only the first part of the process.

The system also needs to understand what the sensor data means.

A vibration level, temperature increase, or force measurement has to be interpreted in relation to the component, operating conditions, movement profile, and expected service life.

This is where application-specific engineering becomes important.

A machine operating slowly under a light load may have a very different wear pattern from an identical component operating continuously at high speed and load.

Predictive maintenance therefore requires a combination of:

• Appropriate sensors

• Reliable data collection

• Component-specific knowledge

• Service-life calculations

• Software analysis

• Clearly defined maintenance actions

The value comes from connecting these elements together.

Smart Plastics and Industry 4.0

Industry 4.0 is built around connected manufacturing systems.

Machines generate data. Sensors collect information. Software analyses operating conditions. Production systems communicate with one another.

Smart motion components can become part of this larger digital ecosystem.

igus® describes smart plastics as a technology that supports condition monitoring and predictive maintenance and can be integrated into Industry 4.0 and IIoT concepts. The company's smart maintenance portfolio includes intelligent energy chains, cables, bearings, and other motion components.

This means that motion components no longer have to be isolated mechanical parts.

They can become data sources within a connected manufacturing environment.

The Economic Argument for Predictive Maintenance

The business case for predictive maintenance is not simply about technology.

It is about economics.

Consider the potential costs associated with an unexpected machine failure:

• Production downtime

• Emergency maintenance labour

• Replacement components

• Lost production capacity

• Delayed deliveries

• Additional inventory requirements

• Potential damage to connected equipment

The actual financial impact depends heavily on the industry and application, but the principle remains the same: preventing a failure can be significantly more valuable than simply replacing a component after it fails.

Predictive maintenance can also help maintenance teams plan their activities more efficiently.

Instead of maintaining every component according to the same fixed schedule, teams can prioritise equipment based on condition and predicted service requirements.

Extending Component Life

Another important advantage of predictive maintenance is that it can help manufacturers use components for their appropriate service life.

Preventive replacement schedules may result in components being replaced even though they still have useful operating life remaining.

Predictive systems can provide additional information about component condition, potentially allowing maintenance teams to make more informed replacement decisions.

igus® specifically identifies maximising component service life as one of the goals of its i.Cee predictive maintenance technology.

This can contribute to reduced component consumption and better maintenance planning.

Smart Maintenance Is Not Only for New Machines

Predictive maintenance does not necessarily require manufacturers to replace their entire production infrastructure.

One of the advantages of modular smart plastics technology is the possibility of integrating monitoring solutions into existing applications.

igus® describes its smart plastics systems as modular solutions that can be used for new Industry 4.0 projects as well as efficiency-focused retrofits.

This is particularly relevant for manufacturers that already have functioning machinery but want to improve its monitoring capabilities.

Instead of replacing an entire machine, selected components can potentially be upgraded with sensing and monitoring technology where appropriate.

What Engineers Should Consider

Before implementing predictive maintenance, engineers and plant managers should first identify where it will create the greatest value.

Not every component needs continuous monitoring.

The best candidates are typically components where:

• Failure can cause significant downtime

• Maintenance access is difficult

• Components operate continuously

• Wear is difficult to observe manually

• Failure can affect other machine systems

• Maintenance costs are significant

• Production schedules make unexpected intervention expensive

The objective should be to focus monitoring resources where they can produce measurable operational benefits.

The Future of Industrial Maintenance

The direction of industrial maintenance is becoming increasingly clear.

Machines are becoming more connected, components are becoming more intelligent, and maintenance decisions are becoming increasingly data-driven.

The future factory is unlikely to depend entirely on fixed maintenance intervals or emergency repairs.

Instead, maintenance will increasingly involve a combination of:

Mechanical engineering + sensors + software + data + human expertise.

Motion components are an important part of this transition because they are directly involved in the physical movement of machines.

When these components can also provide information about their condition, manufacturers gain another layer of visibility into the health of their equipment.

Conclusion

Predictive maintenance represents a fundamental change in the way manufacturers think about machine reliability.

Instead of waiting for a component to fail or replacing it simply because a calendar says it is time, manufacturers can use condition data and service-life calculations to make maintenance decisions based on actual operating conditions.

Smart plastics provide one way of bringing this approach directly into motion components.

With sensor-based condition monitoring and software-supported predictive maintenance, technologies such as i.Sense and i.Cee can help manufacturers identify potential problems earlier, plan maintenance more effectively, reduce unexpected downtime, and make better use of component service life.

For companies investing in Industry 4.0, the lesson is broader than simply adding more sensors.

The real value of connected manufacturing comes from turning machine data into better decisions.

As automation continues to expand, intelligent motion components could become an increasingly important part of that equation—helping manufacturers move from reactive maintenance toward a more predictable, connected, and efficient production environment.

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