How to Achieve Process Control in Plastics Manufacturing
The power of sensXPERT lies in its direct connection to your shop floor machinery. At the forefront is the sensXPERT material characterization sensor, which uses dielectric analysis and 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 detect quality changes, control your processes, and optimize your plastics manufacturing 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:
Benefits of real-time process optimization
Dynamic Process Control in Plastics Manufacturing
In-mold processes often encounter material deviations. To address this, sensXPERT measures a vast amount of real-time data, including quality changes and deviations, and feeds them into machine learning models. These models accurately predict key parameters like degree of cure or crystallization. This predictive capability equips you with the information you need to dynamically control your plastics manufacturing processes. So, when a deviation or quality change is detected, your process can be promptly adapted to account for it, maintaining optimal performance and product quality.
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 in plastics manufacturing by up to 30%.
Scrap reduction
With up to 50% scrap reduction, sensXPERT revolutionizes plastics 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 scrap in manufacturing, enhancing cost savings, and promoting sustainability in production.
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 process 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.
Real-time quality control
Thanks to real-time, in-mold transparency, quality control during the plastics 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 and quality changes, 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 success stories with 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