Mettler-Toledo, together with CSEM and ZHAW has developed AIWizard: An artificial intelligence (AI) option for their STARe software that will make it easier to interpret DSC curves for thermal analysis.
Currently, manufacturers have high expectations surrounding the performance of their materials. A sealing ring must not become brittle, a PET bottle cannot deform, and medications need to react within the body at exactly the right time. Across the material science domain, Mettler-Toledo’s dynamic Differential Scanning Calorimeter (DSC) has become an indispensable tool for many. Thermal analysis makes a valuable contribution from quality control to research and development of materials and chemical compounds.
Why Do We Need DSC?
All materials can absorb or release energy in the form of heat. DSC is used to record physical changes or chemical reactions of materials in quantitative terms by measuring the heat flow of a sample as a function of temperature or time, and as such, it is one of the most important analytical methods within thermal analysis.
DSC is not only highly sensitive and precise, and works with simple sample preparation, but it also provides automation options and short measuring times. DSC is used in all areas in which thermal parameters are determined, thermal processes are investigated, and materials are characterized or compared. Thus, it provides answers to questions surrounding the stability, usage and processing conditions, error detection, failure analysis, material identification, stability, reactivity, chemical safety, and purity of materials. For example, polymers such as thermoplastics, thermosets, elastomers, composite materials, and adhesives, but also food products, pharmaceuticals, and chemicals can be analyzed.
The curves obtained as a result are heat capacity as a function of temperature. The temperatures at which the heat capacity changes are of interest here – these ranges are called “effects”.
Effects are physical or chemical transitions, i.e. phase transitions such as crystallization, melting, glass transitions, or chemical reactions. Important information about the material properties can be derived from the exact form and characteristics of these effects. The shape of the curve and the temperature range contain information that the user employs to interpret the effects.
The evaluation of measured curves is time-consuming and challenging, even for experts. The project idea developed by Mettler-Toledo was to support users with AI.
Using AI to Advance DSC
Mettler-Toledo sent out a tender via the Data Innovation Alliance network to explore this idea’s potential, and several institutes and universities expressed interest. Mettler-Toledo created a consortium with leading research partners ZHAW and CSEM. ZHAW worked on the statistical methods and towards ensuring robustness through robust statistical significance analyses. CSEM contributed by drawing on its extensive knowledge and practical experience in deep learning within industrial applications.
Mettler-Toledo contributed its full domain expertise to the project and integrated the AI solution into their commercialized STARe software. Additionally, Mettler-Toledo processed a huge volume of expert data for the project based on numerous measurements that had previously been measured for publications and reference libraries.
ZHAW developed statistical methods for cleaning the data, analyzing expert data, identifying incorrect labels, and the robust automatic setting of measuring tangents. CSEM was tasked with developing the neural network and robust training algorithms including data augmentation and generation, as well as with validating and checking the results for plausibility.
The result of the project is the new software option AIWizard, which has already been integrated into the well-established STARe software. It facilitates the fully automated evaluation of thermoanalytical measurement curves of unknown samples.
AIWizard™ for Intelligent Evaluation
Video Credit: CSEM
Artificial Intelligence: The Path to Success
Particularly challenging is determining the effect limits (where an effect begins and where it ends). The transitions are fluid in many cases, but exact positioning is essential to obtaining information about the type of effect (e.g., melting, crystallization, glass transition). This is where AI comes into play. The aim was to automate the complex and often error-prone part of the effect determination and thereby combining the knowledge and experience of many experts from different domains.
Experienced employees were initially highly skeptical. They claimed that "automated evaluation is simply not possible". However, in the end, the solution proved to be convincing, and it was concluded that the results far exceeded their expectations.
Neural networks are data hungry, which is why large amounts of expert data were needed. To further enlarge the data set comprising hundreds of measurements, thousands of items of artificial data were generated – in a process known as data augmentation and data generation. However, it is important to consider the ongoing physical processes. Data generation refers to the modeling of processes or the recombination of real data to generate new data sets.
"We have learned a great deal about how new technologies are benefitting customers today. Without the specialist knowledge of the project partners, we would not have been able to implement the idea," concludes Urs Jörimann, Mettler-Toledo project manager.
This project was funded by the Swiss Innovation Agency, Innosuisse (project number: 35056.1 IP-ENG).