Glossary¶
Aggregation¶
The process of combining data from multiple sources to create a comprehensive dataset. Aggregation allows for a holistic view of manufacturing operations, facilitating analysis and decision-making.
Anomaly Detection¶
The identification of rare items, events, or observations that raise suspicions by differing significantly from the majority of the data. Anomaly detection helps identify potential defects or issues in manufacturing processes, enabling timely intervention.
Configuration Parameters¶
Settings and options used to configure a machine learning model, including hyper parameters and input features. Adjusting configuration parameters helps optimize model performance and accuracy.
Correlation Map¶
A visual representation of the relationships between different variables in a dataset. Correlation maps help identify dependencies and interactions that can inform model development and process improvements.
Cross-Validation¶
A technique used to assess the generalizability of a machine learning model by partitioning data into subsets, training the model on some subsets, and testing it on others. Cross-validation helps prevent overfitting and ensures robust model performance.
CSV File¶
A widely-used file format that stores tabular data in plain text, with each line representing a row and columns separated by commas. CSV files are easy to generate and import into various data analysis tools, making them ideal for data exchange in industrial settings.
Dashboard Designer¶
A tool for creating visual representations of data to facilitate monitoring and decision-making. Dashboards provide real-time insights into key performance indicators (KPIs), helping users track and improve manufacturing efficiency.
Data Aggregation¶
The process of combining data from various sources to produce a summary or analysis. Aggregated data provides a holistic view of manufacturing operations, facilitating comprehensive insights and decision-making.
Data Point¶
An individual unit of data, often representing a single observation or measurement in a dataset. Data points are the fundamental elements of data analysis, providing the basis for insights and decision-making.
Data Pre-Processing¶
The process of preparing raw data for analysis by cleaning, transforming, and organizing it. Data pre-processing is critical for ensuring data quality and consistency, leading to more reliable analysis and predictions.
Feature Generation¶
The creation of new input variables from raw data to enhance the predictive power of a model. Feature generation involves deriving meaningful metrics or transformations that capture relevant patterns in the data.
Feature Importance¶
A technique used in machine learning to rank input features based on their contribution to predicting a target variable. Identifying key features helps improve model performance and provides insights into critical factors affecting outcomes.
Filtering Features¶
Variables used to define specific phases or conditions in a manufacturing process. Filtering features help isolate relevant data segments for targeted analysis and prediction.
Histogram¶
A graphical representation of the distribution of numerical data, often used to show frequency distributions. Histograms provide insights into data variability and help identify patterns or anomalies.
Hyperparameters¶
Configuration settings used to tune the performance of machine learning models, often set before the learning process begins. Hyperparameter optimization is crucial for maximizing model accuracy and efficiency.
Identifier¶
A unique string or variable used to distinguish parts or data points within a dataset. Identifiers ensure accurate tracking and analysis of individual components or process stages.
Industrial Internet of Things (IIoT)¶
A network of interconnected devices used in industrial settings to collect, exchange, and analyze data. IIoT enables the integration of advanced analytics and machine learning to drive automation, enhance operational efficiency, and reduce costs.
Inference Engine¶
A component of artificial intelligence systems that applies logical rules to a knowledge base to deduce new information. In manufacturing, inference engines can automatically identify patterns and insights from complex datasets, aiding in process optimization.
Input Features¶
Variables used as predictors in a machine learning model to forecast outcomes. In manufacturing, input features might include machine settings, environmental conditions, or material properties.
Integrated Data Lake (IDL)¶
A centralized repository designed to store vast amounts of structured and unstructured data. It allows organizations to access, process, and analyze data from multiple sources, facilitating advanced analytics and decision-making.
IoT Timeseries Data¶
Data collected over time from IoT devices, capturing continuous streams of information such as temperature, pressure, or machine vibration. Timeseries data is crucial for monitoring trends and identifying anomalies in manufacturing processes.
Lead time¶
The total time from when an order is placed until the product is delivered. It includes all steps like processing, production, and shipping. Shorter lead times improve efficiency and customer satisfaction.
Machine Learning (ML)¶
A subset of artificial intelligence that involves training algorithms on large datasets so that they can learn patterns and make predictions or decisions without being explicitly programmed for specific tasks. In manufacturing, ML is used for predictive maintenance, quality control, and process optimization.
Mean Absolute Error (MAE)¶
A statistical measure used to quantify the average magnitude of errors in a set of predictions, without considering their direction. MAE provides an intuitive sense of how close predictions are to actual outcomes, with smaller values indicating more accurate models.
Model Accuracy Metrics¶
Statistical measures used to evaluate the performance of a machine learning model, such as R², RMSE, and MAE. These metrics provide insights into the model's predictive accuracy and guide refinements to enhance performance.
Model Evaluation¶
The process of assessing the performance of a machine learning model using metrics such as accuracy, precision, and recall. Model evaluation helps determine the model's effectiveness and guides improvements.
Model Training¶
The process of teaching a machine learning algorithm to recognize patterns in data by using a training dataset. Model training involves adjusting algorithm parameters to minimize errors and improve predictive accuracy.
Operator Board¶
A dashboard that allows operators to evaluate current production parameters and predicted quality results in real time. It provides actionable insights and alerts to ensure manufacturing processes remain within specified quality standards.
Outliers¶
Data points that differ significantly from other observations and may affect the results of data analysis. Identifying and addressing outliers is crucial for ensuring accurate predictions and preventing skewed results.
Parallel Coordinate Plot¶
A visualization tool used to analyze and compare multi-dimensional data by representing each variable as a vertical axis. Parallel coordinate plots help identify patterns, correlations, and anomalies across multiple variables.
Permutation Feature Importance¶
A method to determine the importance of each feature by permuting the feature values and measuring the impact on model performance. This technique helps identify the most influential variables in a predictive model.
Predictive Functionality¶
The feature of a system that uses historical and current data to forecast future trends and outcomes. This helps manufacturers anticipate potential issues, enabling proactive decision-making and strategic planning.
Predictive Modeling¶
The process of creating models that can predict outcomes based on input data. Predictive modeling is used in manufacturing to forecast product quality, equipment failures, and process efficiencies, enabling proactive decision-making.
Process Model¶
A detailed representation or simulation of a manufacturing process, used to understand, analyze, and improve operations. Process models help identify bottlenecks and inefficiencies, allowing for optimization of workflows and resource allocation.
Product Quality Parameters¶
Specific criteria or standards used to measure and evaluate the quality of products. These parameters can include dimensions, material properties, surface finish, and functional performance, which are critical to ensuring that products meet design specifications and customer expectations.
Real-Time Prediction¶
The capability to forecast future events or outcomes instantly, using live data. This allows manufacturers to make informed decisions on the fly and address issues as they arise, minimizing disruptions and optimizing processes.
Regression Problem¶
A type of statistical analysis used to predict continuous output values (e.g., product weight or temperature) based on one or more input variables. In manufacturing, regression models help in predicting product quality outcomes based on process parameters.
Regression Score¶
A metric used to evaluate the performance of a regression model, indicating how well the model predicts the target variable. Regression scores help assess model accuracy and guide improvements.
Root Cause Analysis¶
A systematic approach to identifying the fundamental causes of defects or problems within a manufacturing process. By understanding root causes, manufacturers can implement corrective actions to prevent recurrence, thereby improving product quality and efficiency.
Root Mean Square Error (RMSE)¶
A measure of the differences between predicted and observed values in a model, calculated as the square root of the average of the squared differences. RMSE provides a sense of model accuracy, with lower values indicating better predictions.
Root Squared Error (RSE)¶
A measure of the discrepancies between predicted and actual values in a regression model. It quantifies the accuracy of predictions, with lower values indicating better model performance.
R² Accuracy Score¶
The R² (R-squared) accuracy score, also known as the coefficient of determination, is a statistical measure that indicates how well a regression model fits the data. It explains the proportion of the variance in the dependent variable that is predictable from the independent variables.
Interpretation:
- R² = 1: The model perfectly predicts all the data points. This means that 100% of the variance in the dependent variable is explained by the model.
- R² = 0: The model does not explain any of the variance. The predictions are no better than taking the mean of the dependent variable.
- R² < 0: This happens when the model is worse than a simple mean prediction. It's a sign of a poorly fitting model.
Rules Engine¶
A software system that automatically executes predefined business rules in a production environment. Rules engines can enforce quality standards, trigger alerts, or initiate corrective actions in response to specific conditions in manufacturing processes.
Runtime Chart¶
A graphical representation of data that shows how a variable changes over time during a process. Runtime charts are used to monitor trends, identify anomalies, and optimize manufacturing operations.
Scatter Chart¶
A type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. Scatter charts help identify relationships and trends between variables, supporting data analysis and decision-making.
SHAP Values¶
Shapley Additive Explanations, a method to interpret machine learning model predictions by assigning a contribution value to each feature, explaining how they affect the output. This helps in understanding the influence of different variables on model predictions.
Target Variable¶
The output or dependent variable that a machine learning model aims to predict. In quality prediction, the target variable might be a product defect rate or dimensional accuracy.
Tolerance Check¶
A process to ensure that predicted values fall within specified acceptable limits. Tolerance checks help maintain quality standards and identify potential deviations in manufacturing processes.
Tolerance Limits¶
Defined acceptable ranges within which product quality parameters must fall. Ensuring that parameters remain within tolerance limits is essential for meeting quality standards and customer expectations.