In-mold dielectric analysis data (DEA)
Understand What’s Happening Inside Your Mold
The potential for raw material deviations during part production is generally unavoidable due to a lack of process transparency. Moreover, influencing factors are random at best and can range from fluctuating humidity and ambient temperatures to raw material aging and storage conditions.
The power of sensXPERT’s technology lies in its unique ability to intelligently monitor and adapt to these many complex variables in real-time. The starting point here is sensXPERT’s use of dielectric analysis (DEA), which is carried out using sensXPERT’s material characterization sensors.
How it works
Real-Time Material Characterization with Dielectric Sensors
Our Dielectric Sensors, directly embedded in molds, offer heightened sensitivity for real-time material characterization. These sensors, in direct contact with the material, induce molecular movement through an alternating electric field. As the material cures, variations in movement help evaluate changing properties, providing a rich source of real-time data. Paired with pressure transducers and thermocouples, the sensors capture crucial information like resin viscosity, cure progression, and crystallization, offering a comprehensive understanding of material behavior in the manufacturing process.
Influence the batch-to-batch and aging variations on the curing behavior
Batch-to-batch variations can significantly impact the curing behavior in manufacturing processes, introducing complexities that may affect the overall product quality and performance. Understanding and managing these variations are crucial for achieving consistent and reliable outcomes. Aging variations can exert a notable influence on curing behavior in manufacturing processes, introducing challenges that may impact the quality and reliability of the final product over time. Managing and mitigating the effects of aging are essential considerations for ensuring consistent and durable outcomes.
AI-Driven Precision, Dynamic Control, Simulation, and Process Modeling
Our machine learning models compute critical quality indicators like degree of cure, crystallization, glass transition temperature, and other key thermal/mechanical properties of the processed polymer. This transforms your manufacturing control, providing transparency and enabling dynamic cycle adjustments in response to deviations. By combining physics and mathematical models, our AI ensures robust validation with fully observed cycles. Extending data through simulation covers the entire process, elevating the accuracy and reliability of material property predictions for optimized processes.
Relevant Industry Challenges
To understand the potential of in-mold dielectric measurements, it is important to address the various industry challenges these measurements help combat. Below we outline a few challenges or pain points that you may recognize from your production:
A lack of process transparency
Currently, the plastics industry heavily relies on the usual mix of material characterization, modeling, and process simulation originating from a laboratory. That’s not to mention the additional requirement for an experienced workforce to translate that information within a real-world manufacturing scenario. Consequently, quality control for manufactured parts is only possible after they have been produced i.e., what happens inside the mold during the manufacturing process is invisible.
This means longer cycle times with safety contingencies built in, unnecessary scrap production, and inefficient energy consumption. This lack of process transparency also makes it difficult to pinpoint the cause of production errors, thereby increasing the time it takes to recommission a production line.
When processed, it is impossible to expect raw materials to behave consistently and predictably - even if the materials come from the same batch! In fact, there are many unpredictable influencing factors that can affect material behavior during manufacturing e.g., humidity, aging, storage conditions, seasonal changes etc. Therefore, the current practice of relying on material behavior simulations cannot possibly take all these unpredictable influencing factors into account.
The result is usually a case of reality (aka the manufacturing process in real time) not meeting predefined expectations (in line with simulations). These unforeseen deviations can lead to fluctuations in production quality, an increase in scrap production, and an increase in associated costs.
The manufacturing ‘black box’
Typically, a simulation of a material’s behavior is created ahead of the actual manufacturing process. Thereafter, the quality of the parts produced can only be assessed post-process. Thus, what happens in the middle is essentially a ‘black box’.
In-mold processing is invisible to processors and operators and there is no way of identifying a material’s behavior in real time – there are only assumptions based on the simulations and trials conducted in a laboratory environment beforehand. This lack of transparency on in-mold material behavior means that manufacturers are unable to detect any deviations. Therefore, they are limited in the actions they can take to optimize their manufacturing processes. Even if the manufacturing process remains the same, there is always a risk of producing parts that do not meet specific quality control requirements.
Benefits of In-Mold Dielectric Measurements
Material Behavior is Now Visible
Despite the many unknown and unforeseen factors that could potentially alter material behavior, sensXPERT can help you detect and monitor those changes thanks to a rich source of in-mold dielectric measurements. These measurements are collected with sensXPERT’s material characterization sensors. In fact, these once invisible material behavior deviations can now be visualized in real-time on the sensXPERT edge device interface.
Dynamic and Adaptive Process Control
Material deviations within the mold are unavoidable, but sensXPERT’s in-mold dielectric measurements play an important role in helping you mitigate their effects. Alongside data from third-party sensors, all of the measurements are fed into advanced machine learning models that can accurately predict the optimum point of cure or crystallization and other crucial parameters. Armed with these powerful insights, you can unlock an unprecedented level of dynamic and adaptive control over your manufacturing process.
Quality Control for Each Part Produced
Introducing sensXPERT directly into the mold reveals exactly what is happening to the material during its curing or crystallization phase. Therefore, even in the event of unexpected deviations in material behavior, this new level of process transparency allows you to reactively respond. In other words, you can dynamically adapt your process to ensure that the quality of the part produced meets your requirements, regardless of any deviation. Unlike what occurs post-process, this new kind of quality control happens directly in the mold, for each part produced, and in real time!
Reduce Cycle Times
Collecting such a large volume of real-time data during manufacturing processes also has significant impact on cycle times. t’s what allows sensXPERT’s machine learning models to predict the degree of cure or crystallization for each individual part produced. Unlike the past when manufacturers had to rely on cycle time ‘safety buffers’, this now gives you precise control over molding cycles without compromising quality. In fact, sensXPERT could potentially cut cycle times by up to 30%!
Reduce Scrap Production
sensXPERT's dielectric analysis data is also one of the key ingredients in reducing the output of defective parts by up to 50%. As with cycle times, by combining real time manufacturing data with information on material science, sensXPERT opens the possibility for you to dynamically control and adapt the process with absolute certainty. This includes your ability to maintain a consistent level of production quality, regardless of any potential deviations in the mold – and, as a result, minimizing scrap production like never before!
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