Machine learning drives research and quality assurance
What if your material data could reveal which recipe works even before the first attempt? This article shows how R&D and QA teams use machine learning to make better decisions and shorten development times — with a clear five-step approach.
Too many attempts, too long development times, too much data without gaining knowledge: In research and quality assurance, teams face these challenges every day. What if targeted recommendations could be derived from existing data — automated, reliable and scalable? This is exactly where machine learning comes in.
Whether in recipe development, identifying process deviations or quality testing: Machine learning can help companies make concrete decisions from existing data — faster and more precisely than ever before. This article shows how to get started, what is important when it comes to data, models and platforms — and why laboratories and R&D teams in particular benefit.
What is machine learning — and what isn't?
Machine learning is a branch of artificial intelligence (AI). While AI generally describes machines that imitate human abilities, machine learning is about learning from data. An ML model recognizes patterns in large amounts of data, makes predictions and continuously improves — without having to program every step.
You know this from everyday life: spam filters, facial recognition and product recommendations have long been based on machine learning. These systems analyze data, adapt themselves automatically and deliver increasingly precise results.
Classic statistics vs. machine learning
In contrast to classical statistics, which assume relationships based on models, machine learning independently searches for patterns — even in complex, nonlinear or high-dimensional data structures. For example, while linear regression requires a clear formula, a neural network can recognize hidden interactions between many parameters.
Machine learning not only provides better predictions, but also opens up completely new opportunities to understand relationships that were previously hidden.
Especially in material development, where numerous influencing factors act simultaneously, this data-driven approach offers enormous advantages. ML not only provides better forecasts, but also opens up completely new opportunities to understand relationships that were previously hidden.
How machine learning is used in material development
In traditional product development, the trial and error principle often still dominates. Individual parameters are changed, results are analyzed and tested again — a laborious, time-consuming process. Alternatives such as statistical design of experiments (DoE) help, but quickly come with their limits as complexity increases.
Machine learning is taking a new, data-driven approach here. Instead of relying solely on experience and experiments, historical data — such as from tests, recipes or measurements — is used to identify relationships. A trained model can then predict, for example, how a specific material composition performs or which parameters have the greatest influence on quality.
A glimpse into practice
In a typical project, a company analyzed several thousand historical recipes and their performance values. The aim was to use machine learning in two ways: First, the model should predict how well a new recipe performs — even before it is tested in the laboratory. Second, it should provide suggestions for optimal recipes based on defined target values.
The project followed a structured approach that has proven effective in many industrial applications:
data generation
First, reliable data from previous experiments was collected and digitized — for example on compositions, process parameters and laboratory measurements such as viscosity or hardness.
data preparation
The raw data was cleaned, standardized and structured in such a way that it could be processed not only by people but also by algorithms.
Model training
Around 80% of the available data was used to train the model. In doing so, the system learned the relationships between input variables (e.g. recipe components) and target variables (e.g. performance measurements).
Model deployment
After successful validation, the model was integrated into a tool that was now able to provide concrete predictions of the performance of new recipes and suggestions for optimization.
model maintenance
The performance of the model was constantly monitored. New data from tests or customer feedback was regularly incorporated into the system in order to further improve it.
The remaining 20% of the data served as test data: They tested how well the model generalized to previously unknown recipes — and how reliable the predictions were in practice.
This structured procedure helped to significantly reduce the number of physical tests. The model provided targeted suggestions for promising approaches — and thus reduced development times and the ability to respond to new customer inquiries.
Other areas of application in research and quality assurance
In addition to recipe development, machine learning is also a powerful tool in quality assurance. Models can evaluate historical measurement data to identify deviations that indicate potential defects. In this way, problems can be prevented early on — before they affect production.
ML also provides valuable impetus in process optimization or as part of predictive maintenance. It recognizes patterns in process data that could indicate future failures and thus supports predictive maintenance — a real added value for manufacturing companies.
Technical insights: From linear models to neural networks
Depending on the objective and data situation, different types of models are used in machine learning. Simple linear models are particularly transparent and quickly trained. They show how strongly a specific factor — such as the concentration of an additive — influences the result. They are ideal for a first start or to confirm known connections.
More complex tasks — such as interacting several material parameters or predicting performance under specific process conditions — require more powerful models such as decision trees, random forests, or neural networks. So-called Bayesian neural networks are particularly suitable for taking uncertainties into account in forecasts. The “right” model is usually selected iteratively — with validations, comparisons, and combinations of several approaches.
An often underestimated advantage of the model: It not only helps to predict, but also to understand. Which characteristics have the biggest impact? What is the biggest lever for improvement? In this way, machine learning also becomes an analysis tool for daily development work.
What companies should consider when using machine learning
It is not only the algorithm that is decisive for success, but also the quality and structure of the data. Many projects fail not because of modelling, but because of data preparation. Excel tables with inconsistent names, different formats or missing structures significantly slow down progress. The cleaner the database, the better the model quality.
In addition, machine learning is not a unique project. Models must be continuously maintained and updated with new data — just like a garden that needs regular maintenance. This is the only way to improve results in the long term.
The knowledge of experts also remains essential. Because although the model learns from data, it needs the experience of development and quality teams to evaluate which characteristics are important — and which relationships are really relevant.
Implementation challenges in companies
Using machine learning is not only a technical decision, but also a cultural one. Many organizations don't lack data — but a clear strategy for how it should be used. Data is often scattered across different departments and systems, making meaningful merging difficult.
Dealing with uncertainties also needs to be learned: Machine learning does not provide final truths, but probabilities. This requires a different approach to results — and often also a rethink in decision-making.
Last but not least, acceptance among employees is required. When ML-based recommendations in the laboratory or in quality assurance are not comprehensible, trust decreases — and therefore the benefits. Transparent models, pilot projects with tangible added value and good communication between departments and data controllers help here.
Why a platform solution makes sense
For many companies, it is worthwhile not to start from scratch, but to rely on a specialized material intelligence platform. This combines data management, machine learning and an intuitive interface. This allows data from various sources to be integrated, processed and transferred directly into ML-based processes.
The advantage: Instead of laborious individual work to develop their own algorithms, teams can concentrate on interpreting and applying the results — exactly where the expertise lies.
The way to get started
Anyone who wants to use machine learning in material development or quality assurance should start with a specific question. Which tasks in everyday laboratory work or in formulation development could be better solved based on data? Once the goal is clear, it is a matter of checking existing data: Is it complete, structured and digitally available?
Getting started doesn't have to be complicated. Even simple models often provide valuable insights — a deep learning approach is not absolutely necessary. It is important to start with a clearly defined use case, to gain initial experience and to systematically scale from there.
conclusion
Machine learning opens up new perspectives for data-driven innovation — both in research and in quality assurance. Used correctly, it helps to increase efficiency, develop better materials and products, and make well-founded decisions. With the right platform, clear objectives, and well-structured data, getting started is easier than many think.