Machine Learning for R&D: Data Analysis & Process Intelligence

R&D Data Landscape and ML Opportunities

Experimental, laboratory, process and simulation data · Structured vs. unstructured scientific data · Variables, parameters, responses and experimental conditions · From experimental data to data-driven decisions · Identifying R&D problems suited to Machine Learning.

Use cases: Predicting material properties and chemical/process yield · Identifying factors affecting product quality · Detecting abnormal laboratory or process measurements · Predicting fibre strength or textile performance.

Hands-on exercise: Understand a representative R&D dataset, define scientific questions, and identify target variables, features and potential ML problems.

Data Preparation and Cleaning

Missing and inconsistent values · Duplicate and erroneous observations · Outlier identification · Data types and transformations · Feature selection · Scaling and normalisation · Preparing experimental datasets for ML.

Hands-on lab: Clean an experimental/process dataset, identify anomalies and outliers, prepare features and target variables, and create an ML-ready dataset.

Exploratory Data Analysis and Visualisation

Distributions and variability · Correlation analysis · Feature relationships · Scatter plots, histograms and box plots · Heatmaps and multivariate analysis · Identifying patterns and unusual observations.

R&D use case: Analyse the relationship between process parameters and material properties to identify which parameters relate most strongly to product performance.

Hands-on lab: Investigate a representative materials/process dataset and generate insights before building an ML model.

Statistical Analysis for R&D

Descriptive statistics: mean, median, standard deviation and variance · Correlation vs. causation · Statistical relationships · Experimental variability · Confidence and uncertainty · Using statistical findings to guide modelling.

Mini case study, process parameter analysis: Analyse experimental data to determine which temperature, pressure, composition and processing parameters are associated with changes in product quality.

Machine Learning Fundamentals

What is Machine Learning? · Supervised vs. unsupervised learning · Regression, classification and clustering · Features and target variables · Training and testing datasets · Model development workflow · Avoiding data leakage and overfitting.

Hands-on exercise: Convert real R&D questions into ML problems: "What will be the material strength?" → Regression · "Will the product meet quality specifications?" → Classification · "Which experimental samples behave similarly?" → Clustering.

Regression for Scientific and Process Prediction

Linear, multiple and tree-based regression · Model training and prediction · Model performance metrics · Interpreting important variables.

Case study, material property prediction: Predict a property such as tensile strength, hardness, viscosity or thermal performance from composition and process parameters.

Hands-on lab: Prepare training data, build regression models, compare actual vs. predicted values, identify influential parameters and interpret results for R&D decision-making.

Classification for Quality and Process Decisions

Binary and multi-class classification · Decision trees · Random Forest · Confusion matrix · Precision, recall and F1-score.

Use cases: Pass/fail quality prediction · Defect classification · Product grade classification · Chemical batch quality classification · Acceptable vs. unacceptable process conditions.

Hands-on case study, quality prediction: Develop a model that predicts whether an experimental batch will meet defined quality specifications, based on process and material parameters.

Clustering, Anomaly Detection and Pattern Identification

Why unsupervised learning is useful in R&D · K-Means clustering · Grouping similar experimental samples · Identifying unusual experiments · Anomaly detection concepts · Pattern discovery without predefined labels.

Case study, experimental sample segmentation: Cluster observations by material composition and measured properties to find groups with similar behaviour and investigate unusual samples.

Model Validation and Interpretation

Train/test split · Cross-validation concepts · Model performance · Overfitting and underfitting · Feature importance · Interpreting model outputs · Limitations of ML predictions · Scientific vs. statistical validation.

Final Hands-on R&D Case Study

Participants work through an end-to-end R&D problem:

Possible datasets: Materials property prediction · Metals process-quality prediction · Fibre strength prediction · Textile performance classification · Chemical process yield prediction · Laboratory anomaly detection.

Final group activity: Define the R&D problem · Prepare the dataset · Explore key relationships · Select an ML approach · Build a foundational model · Evaluate performance · Identify important parameters · Present findings and potential R&D applications.