Process optimization in real time

Control your production like never before

The power of sensXPERT lies in its direct connection to your shop floor machinery. At the forefront is the sensXPERT Dielectric Sensor, which maintains direct contact with the raw material inside the mold, continuously collecting real-time data about the material's behavior.

At the same time, the sensXPERT Edge Device takes this wealth of data and integrates it with pre-existing kinetic models, generating advanced predictive machine learning models.

These machine learning models are instrumental in forecasting properties like the degree of cure or crystallization, glass transition temperature, and other relevant thermal or mechanical attributes of the raw material in processing. If these predictions stray from the target value, the process can be dynamically fine-tuned until the predictions align with the desired value. This adaptability, fueled by real-time data and predictive insights, helps produce a higher quantity of quality parts while reducing waste.
Ultimately, this system boosts your ability to optimize your manufacturing process in a truly dynamic and adaptable way.

Relevant industry challenges

To understand the true potential of a feature such as real-time process optimization, it is important to revisit the various industry challenges it addresses. Below are several challenges or pain points that you may recognize:

CHALLENGE 01

Quality fluctuations

Manufacturing processes are often influenced by numerous factors impacting material behavior, altering the optimal degree of cure/crystallization and glass transition temperature. Traditionally, you would rely on upfront material simulations to pre-empt any issues. But these often misalign with in-mold material behavior due to real-world variables. Plus, conventional downstream quality control only detects defective parts post-production, without revealing the real cause of the error.

This approach not only results in wasted resources but also hinders preventative measures for future issues - ultimately leading to persistent quality fluctuations and process inefficiencies.

CHALLENGE 02

High scrap rates

High scrap rates, undoubtedly a major issue for yourself and other manufacturers, stem from a combination of variables. These could be internal variables such as mixing ratios, or external factors – namely humidity or aging. These factors, often unaccounted for in upfront material simulations, cause discrepancies in actual in-mold material behavior.

This leads to quality deviations and increased scrap, resulting in wasted resources and higher manufacturing costs. Inefficiencies like these, if unresolved, can pose significant operational and financial challenges.

CHALLENGE 03

Long cycle times

As a manufacturer, you may experience a lack of transparency during the processing or molding phase. This obscurity often leads to the addition of a safety buffer to your targeted cycle times, ensuring the optimum degree of cure or crystallization per part is reached. However, this practice inevitably extends your cycle times.

Consequently, your production processes may become less efficient and more costly than necessary. Extended cycles not only consume more resources but also potentially delay delivery timelines, affecting your bottom line and customer satisfaction.

Benefits of real-time process optimization

BENEFIT 01

Dynamic & adaptive process control

In-mold processes often encounter material deviations. To address this, sensXPERT measures a vast amount of real-time data, including these deviations, and feeds it into machine learning models. These models accurately predict key parameters like the point of curing or crystallization. This predictive capability equips you with the information you need to dynamically control your processes. So, when a deviation is detected, you can quickly adapt your process in real time to account for it, maintaining optimal performance and product quality.

BENEFIT 02

Cycle time reduction

By leveraging predictive analytics and real-time process monitoring, sensXPERT’s machine learning models can accurately predict material behavior during the manufacturing cycle. Quick identification of any potential issues not only helps avert delays but also allows you to actively adjust the process, to reduce the usual cycle time 'safety buffers’. More importantly, this can be done for every part produced. In fact, with sensXPERT the potential is there for cutting cycle times by up to 30%.

BENEFIT 03

Scrap reduction

With up to 50% scrap reduction, sensXPERT revolutionizes manufacturing by leveraging real-time data and machine learning. It gathers extensive material behavior data and combines it with material science knowledge. This information powers machine learning models, which accurately predict material behavior. These predictions allow manufacturers to dynamically control their processes, significantly reducing waste, enhancing cost savings, and promoting sustainability in production.

BENEFIT 04

Energy savings

Thanks to its predictive capability, sensXPERT also offers the potential for significant energy savings. By providing forecasts of material behavior during production, potential issues can be identified early. Consequently, and via adaptive control, you are now able to cut back on contingency cycle times. You’re also able to significantly reduce scrap production. The net result is a decrease in energy consumption on both fronts.

BENEFIT 05

Real-time quality control

Thanks to real-time, in-mold transparency, quality control during the manufacturing process is now also a reality. So, you no longer need to rely exclusively on post-process testing to check the quality of your manufactured parts. Even in the event of unforeseen material deviations, with this kind of unprecedented level of oversight you can still produce parts that perfectly satisfy your manufacturing requirements. This is partly down to sensXPERT’s machine learning models, which can predict any issues, allowing you to adapt the process reactively and dynamically and course-correct any deviations.

Explore real-world use cases for sensXPERT

Improving in-mold transparency in the electrical encapsulation industry

Find out how two companies successfully used sensXPERT on their reaction injection molding processes to boost in-mold transparency and reduce scrap production rates.

Find out more

Additional Relevant Resources

All resources