In today's fast-paced manufacturing world, ensuring consistent, high-quality production is more critical than ever. Automated Quality Inspection (AQI), powered by machine vision and AI inspection systems, allows manufacturers to detect defects, verify assembly, and optimize processes in real time—without slowing down production.
This comprehensive glossary is tailored for production engineers, quality teams, and systems integrators working with industrial vision systems. It covers essential terms, technologies, and concepts behind automated visual inspection, helping teams make smarter decisions, implement effective inspection systems, and maintain top-tier quality across production lines. Whether you're exploring machine vision hardware, AI-powered defect detection, or industrial inspection metrics, this guide provides a clear, practical reference for over 100 key terms in the field.
Table of Contents
- Core Concepts and Terminology
- Types of Image Capture Systems
- Optical Components
- Timing & Control
- Image Processing Techniques
- AI and Machine Learning
- System Integration
- Common Quality Inspection Applications
- Measuring System Performance
What is Machine Vision? Core Concepts and Terminology
These fundamental concepts determine how effectively an inspection system will capture, process, and analyze visual data from production lines. Teams often underestimate how these foundational elements impact overall system performance—getting them right from the start prevents costly redesigns later.
What Are the Basic Terms in Machine Vision?
- Machine Vision (MV): The application of image processing and image analysis to automatically control specific actions, primarily within industrial contexts. It encompasses the technology and methods used for imaging-based automatic inspection, process control, and robot guidance. A machine vision system is capable of acting on visual stimuli by digitizing, processing, and analyzing images.
- Computer Vision (CV): An interdisciplinary field of science and technology that explores how computers can derive understanding from images and videos. It aims to automate activities typically performed by the human visual system. While closely related to machine vision, computer vision is often regarded as a more fundamental form of computer science, whereas machine vision focuses on practical industrial applications.
- Image Processing: The conversion of an image into another image to highlight or identify certain properties within it. Its primary focus is on manipulating digital images to enhance quality, restore corrupted data, or compress/decompress image files.
- Image Analysis: The process of extracting meaningful features or attributes from an image based on its inherent properties. It goes beyond simple manipulation to derive actionable information.
- Automated Quality Inspection (AQI): Uses automated systems, often incorporating image processing and sensors, to detect and classify defects in products or processes without manual intervention.
What Are Basic Optical and Imaging Principles?
These optical fundamentals directly impact a system's ability to detect defects and maintain consistent quality. Many implementation challenges stem from inadequate attention to these basics—proper lighting and camera positioning often make the difference between a successful deployment and one that struggles with false positives.
- Pixel: The smallest individual value within a digitized image, describing the image's color and intensity at a specific point.
- Resolution: Defines the number of pixels in an image, directly determining its clarity and the vision system's ability to distinguish fine features. It is typically expressed in horizontal and vertical pixels or as the total number of megapixels.
- Field of View (FOV): The specific portion of the real-world scene that the machine vision system can perceive at any given moment. The FOV is critically influenced by the system's lens choice and the working distance between the object being inspected and the camera.
- Depth of Field (DOF): The range of distances within a scene where objects appear acceptably sharp and in focus to the camera or imaging device.
- Frame Rate (FPS): The number of individual images or frames that a device can capture or process per second, indicating the system's speed.
- Lens: An optical device, typically made of shaped glass, designed to converge or diverge light rays to form an image.
- Aperture: The adjustable opening within a photographic lens that controls the amount of light reaching the film or image sensor.
- Shutter: A mechanical or electronic device that controls the duration for which light is allowed to pass through the lens and expose the image sensor.
- Image Sensor (CCD/CMOS): An electronic component that incorporates multiple light-sensitive elements to capture images electronically. It converts an optical image into a digital electronic signal. The most commonly used image sensors are Charged Coupled Device (CCD) and Complementary Metal-Oxide Semiconductor (CMOS) chips.
What Characteristics of an Image Matter in Machine Vision?
- Grayscale: A digital image format where each pixel is represented by a single sample value. These images are typically composed of varying shades of gray, ranging from black (weakest intensity) to white (strongest intensity), with no color information.
- RGB: An acronym for Red, Green, and Blue. This is an additive color model where varying combinations of these three primary colors represent a pixel's overall color.
- HLS (Hue, Lightness, Saturation): An acronym defining a color space based on the hue (the pure color), lightness (brightness), and saturation (purity or intensity) of a pixel.
- HSV (Hue, Saturation, Value): Similar to HLS, this model defines a color space using Hue, Saturation, and Value (which corresponds to brightness).
- Histogram: A graphical representation that displays the distribution of pixel intensity values within an image. For grayscale images, it shows the number of pixels at each intensity level. For color images, it can represent the distribution of colors across different ranges.
- Contrast: In visual perception, contrast refers to the discernible difference in visual properties that allows an object to be distinguished from other objects or its background within an image.
- Dynamic Range: The ratio between the brightest and weakest light intensities that a camera or imaging device can perceive and accurately capture.
- Noise: Random variations in pixel intensity that reduce image quality and can interfere with accurate inspection, often appearing as a high number of pixels with widely varying intensities, similar to an untuned television picture.
What Types of Image Capture Systems Are Available?
Selecting the right image capture technology is fundamental to inspection system success. Different imaging modalities serve distinct inspection needs, from traditional visible light cameras to specialized techniques that reveal hidden defects or enable precise measurements.
- Cameras: The primary device for capturing images, either as single shots or in a continuous sequence. Types include 2D area scan cameras, 1D line scan cameras, and 3D cameras for height or shape measurements.
- 1D Vision System / Line Scan System: Utilizes a camera that captures a single line of pixels. A two-dimensional image is then constructed as the object moves continuously past this line, making it suitable for web inspection or extrusion processes.
- 2D Vision System / Area Scan Camera: The most common type, producing a standard image similar to those from a cell phone or webcam. These are widely used for general inspection tasks in industrial automation.
- 3D Vision System: Capable of perceiving three dimensions, including depth. These systems typically employ techniques such as stereo vision (multiple cameras) or structured light projection to create a 3D model of the scene.
- Smart Cameras: An integrated machine vision system that combines image capture circuitry with an onboard processor. It can extract information from images without requiring an external processing unit, and includes interface devices to communicate results.
- Gigabit Ethernet (GigE) Camera: Adhering to the GigE Vision standard, these cameras offer fast data throughput rates (up to 120 MB/s) and support long cable lengths (up to 100 meters). A significant advantage is that they often do not require a separate frame grabber, simplifying system architecture and reducing costs.
- X-ray Imaging: Non-destructive inspection technique that penetrates materials to reveal internal structures, defects, and foreign objects invisible to optical cameras, commonly used for electronics inspection and food safety applications.
- Thermal Imaging: Captures infrared radiation to create images based on temperature differences, enabling detection of hot spots, electrical faults, and process variations through heat signature analysis.
- Hyperspectral Imaging: Advanced technique that captures images across multiple wavelength bands to create detailed spectral signatures, enabling material identification and detection of subtle defects based on chemical composition.
- Laser-based Inspection: Utilizes laser light sources for precision measurements and surface analysis, including laser triangulation for dimensional measurement and laser line scanning for 3D profiling.
- Microscope Vision: High-magnification imaging systems that enable inspection of micro-scale features, critical for semiconductor manufacturing, micro-assembly verification, and detailed surface analysis.
What Optical Components Do Vision Systems Need?
The optical path from object to sensor determines image quality and measurement accuracy. Understanding these components and their trade-offs helps teams design systems that capture the information needed for reliable inspection decisions.
What Are the Different Types of Industrial Lenses and Optics?
- Lenses: An optical device, typically made of shaped glass, designed to converge or diverge light rays to form an image.
- C-Mount / CS-Mount: Standardized mounting threads for optical lenses on CCD cameras, differing in their back focal distance (17.5 mm for C-Mount vs. 12.5 mm for CS-Mount). A C-Mount lens can be adapted for a CS-Mount camera with a 5 mm extension.
- Telecentric Lens: A specialized compound lens designed to maintain constant magnification regardless of the object's distance from the lens. This property is invaluable in machine vision for precise dimensional and geometric measurements, as it eliminates perspective error.
- Normal Lens / Entrocentric Lens: Lenses that produce images with a perspective generally perceived as "natural" to the human eye.
- Wide Angle Lens: Features a shorter focal length than a normal lens, resulting in a broader field of view.
- Telephoto Lens: Characterized by a significantly longer focal length than a normal lens, providing a narrower field of view and greater magnification.
- Zoom Lens: A mechanical assembly of lenses whose focal length can be varied, allowing for adjustable magnification without changing the lens.
What Types of Industrial Lighting Are Used in Machine Vision?
- Lighting: Refers to both artificial light sources and natural illumination. Proper lighting is paramount in machine vision, as it is essential for eliminating shadows and glare, thereby enhancing image clarity and contrast for accurate defect detection.
- Ring Lighting: Circular lighting arrangement that provides uniform illumination around the camera's optical axis, ideal for eliminating shadows and providing even surface illumination.
- Coaxial Lighting: Illumination that travels along the same path as the camera's optical axis, achieved through beam splitters, providing shadow-free lighting that's particularly effective for reflective surfaces.
- Structured Lighting: Projection of specific patterns (grids, lines, or dots) onto objects to reveal surface topology and enable 3D measurements through pattern analysis.
- Polarized Lighting: Uses polarizing filters to control light reflections, reducing glare from shiny surfaces and enhancing contrast for better defect detection.
- UV/IR Lighting: Ultraviolet or infrared illumination that can reveal features invisible to standard visible light, useful for fluorescence applications or materials that absorb/reflect specific wavelengths.
What Image Sensors and Processing Hardware Do Vision Systems Use?
- CCD (Charge-Coupled Device): A type of image sensor that converts light into electrical charges, traditionally offering superior image quality with lower noise and higher sensitivity, though typically more expensive and power-hungry than CMOS sensors.
- CMOS (Complementary Metal-Oxide Semiconductor): A type of image sensor that uses standard semiconductor manufacturing processes, offering advantages in power consumption, cost, and integration capabilities, with modern versions achieving image quality comparable to CCD sensors.
- Global Shutter: An image sensor design where all pixels begin and end their exposure simultaneously, eliminating motion blur and distortion when capturing fast-moving objects, though typically more complex and expensive than rolling shutter designs.
- Rolling Shutter: An image sensor design where different rows of pixels are exposed sequentially rather than simultaneously, offering simpler manufacturing and lower cost but potentially causing distortion with rapidly moving subjects.
- Frame Grabbers: An electronic device responsible for converting analog video signals into digital still frames or capturing digital video streams, making them available for computer processing.
- Interface: The method through which two systems communicate. For machine vision cameras, this refers to the type of connection between the camera and the PC, such as Gigabit Ethernet, USB2, or USB3.
- I/O (Input/Output): Refers to the signals received (input) and sent (output) by a vision camera for various communication, triggering, or status reading purposes.
How Do Vision Systems Synchronize with Production Lines?
Proper timing and synchronization are critical for reliable vision system performance in manufacturing environments. These concepts ensure cameras capture images at the right moment and coordinate with production equipment to maintain consistent inspection quality at high speeds.
- Triggering: The process of coordinating when a camera captures an image, typically synchronized with production line events such as part presence, conveyor position, or external signals to ensure consistent image capture timing.
- Hardware Triggering: External signal-based camera activation using physical connections (digital I/O) to ensure precise timing synchronization with production equipment, providing more accurate timing than software-based triggering.
- Software Triggering: Camera activation controlled through software commands, offering flexibility in timing control but with potential latency variations that may affect high-speed applications.
- Encoder Synchronization: Using rotary or linear encoders to trigger image capture based on actual mechanical position or movement of production line components, ensuring consistent spatial relationships between parts and captured images.
- Strobing: Coordinated activation of lighting and camera exposure to freeze motion in high-speed applications, using brief, intense light pulses synchronized with image capture to eliminate motion blur.
- Free-Running Mode: Camera operation where images are captured continuously at a fixed frame rate without external triggering, suitable for monitoring applications where precise timing synchronization is not critical.
- External Synchronization: Coordination of multiple cameras or vision systems using shared timing signals to ensure simultaneous image capture across different inspection stations or viewing angles.
What Image Processing Techniques Are Used in Machine Vision?
These processing methods transform raw images into actionable insights. The choice of technique depends on specific inspection requirements—teams often find that dimensional accuracy demands different approaches than surface defect detection or assembly verification.
- Image Acquisition: The initial step in the vision system workflow, referring to the method by which an image is captured from the real world and converted into a format suitable for digital image analysis.
- Image Filter: A mathematical operation applied to every pixel value in a digital image to transform it in a desired way, often for noise reduction, sharpening, or feature enhancement.
- Filtering: Signal processing techniques applied to images, including median filtering (removes noise while preserving edges), Gaussian filtering (smoothing and noise reduction), and other kernel-based operations for image enhancement.
- Image Enhancement: Techniques used to improve image quality by adjusting contrast, brightness, sharpness, or other visual properties to make features more detectable for analysis.
- Region of Interest (ROI): A specific area or subset of an image selected for focused analysis, allowing systems to concentrate processing power on the most relevant portions while ignoring irrelevant background areas.
- Edge Detection: A fundamental technique that identifies and marks points within a digital image where the luminous intensity changes sharply. These points typically correspond to the boundaries or high-frequency areas of objects.
- Segmentation: The process of partitioning a digital image into multiple segments or regions. This simplifies the image's representation, making it more meaningful and easier to analyze by isolating objects or areas of interest.
- Thresholding: A process that involves setting a specific gray value (or color range) to separate different portions of an image. Pixels above or below this threshold are often transformed to a binary (black and white) representation, simplifying the image for further analysis.
- Template Matching: A method in digital image processing used to locate areas within an image that closely match a predefined "template" image. This is commonly applied in object recognition and localization tasks.
- Blob Detection: A technique that inspects an image for discrete "blobs" or connected regions of pixels that share similar properties (e.g., a dark hole in a lighter object). These blobs often serve as landmarks for machining, robotic manipulation, or identifying manufacturing defects.
- Morphological Operations: Mathematical operations based on shape analysis, including erosion (shrinking objects) and dilation (expanding objects), used to clean up binary images and enhance object boundaries.
- Flat-field Correction / Shading Correction: Techniques employed to improve the quality of digital images by compensating for variations in pixel-to-pixel sensitivity of the detector or optical distortions such as vignetting (darkening of image corners) or dust particles on the sensor.
- Gamma Correction: A non-linear operation applied to digital images to adjust their light intensity, illumination, or overall brightness. It can also influence the RGB color balance.
- Color Analysis: Techniques used to identify parts, products, or items based on their color. This can involve assessing quality from color variations or isolating specific features using color properties.
- Optical Character Recognition (OCR): The technology that translates images of typewritten or printed text into machine-editable text, converting visual characters into a standard encoding scheme.
- Optical Character Verification (OCV): A process closely related to OCR, focusing on verifying the accuracy and quality of recognized characters, often against a known standard.
How Are Artificial Intelligence and Deep Learning Used in Industrial Vision?
AI represents the next evolution in quality inspection, moving beyond rule-based programming to systems that learn and adapt. Organizations looking to implement these more flexible, accurate, and scalable inspection solutions need to understand both the opportunities and the complexities involved.
What Are the Core AI and Machine Learning Concepts?
- Algorithm: A precise set of instructions or rules that a computer follows to complete a specific task. Algorithms are particularly powerful when combined with big data and machine learning techniques, enabling complex data analysis and predictive modeling.
- Artificial Intelligence (AI): The simulation of human intelligence processes by machines, particularly computer systems. This encompasses a broad range of capabilities, including learning from experience, reasoning, problem-solving, decision-making, creativity, and autonomous action.
- Machine Learning (ML): A subset of AI where algorithms are trained to learn patterns and make predictions from data without being explicitly programmed for every scenario. This learning often occurs through trial-and-error processes or by identifying hidden patterns in data.
- Deep Learning (DL): A specialized technique within machine learning that utilizes artificial neural networks with multiple layers (referred to as "deep architectures") to learn complex representations from vast amounts of data and make predictions. Deep learning has demonstrated remarkable success in tasks such as image recognition and natural language processing.
- Neural Networks & CNNs (Convolutional Neural Networks): Computational models inspired by the structure and function of the human brain. They consist of interconnected "neurons" organized into layers that process and transform data. CNNs are a specialized type of deep learning model explicitly designed for image recognition and processing tasks, utilizing convolutional layers to automatically detect and learn hierarchical patterns and features within images.
- Generative AI: A class of AI technology focused on creating novel content, such as text, video, code, or images, by learning patterns and structures from existing training data.
- Multimodal Model: An AI model capable of processing and generating information across multiple types of input and output modalities, such as text, images, audio, and video. An example is a model that can describe an image and generate captions or code from a diagram.
- Big Data: Refers to extremely large and complex datasets that can be analyzed computationally to reveal patterns, trends, and associations. Big data forms the essential foundation for AI, as learning algorithms require immense quantities of information to emulate human decision-making and generate accurate forecasts.
What Data Preparation Is Required for AI Vision Systems?
- Dataset: A structured collection of data, often labeled, that is used for training, validating, and evaluating AI models. Datasets provide the empirical basis for machine learning algorithms to learn and generalize.
- Data Curation: The comprehensive process of preparing data for AI training, which includes tasks such as collection, organization, integration, and maintenance to ensure its quality and suitability for model development.
- Data Cleaning/Scrubbing: The process of identifying and removing flawed, redundant, inaccurate, or outdated data from a dataset. This step is essential to enhance the effectiveness and accuracy of learning algorithms, as AI models rely on clean, reliable, and consistent data sources.
- Data Labeling / Annotation: The critical process of assigning meaningful labels or tags to raw input data, such as images or videos, in preparation for AI model training. This involves outlining objects with bounding boxes for object detection or creating pixel-level masks for image segmentation, depending on the specific task the AI model is intended to perform.
- Data Augmentation: A technique used to artificially increase the size and diversity of a dataset by applying slight modifications to existing images. Examples include rotating, flipping, scaling, cropping, or shifting images to create numerous augmented versions from a single original.
- Training Data: The digital information, comprising examples and their corresponding labels, that is fed into an AI algorithm to enable it to learn patterns and relationships. This data is crucial for the algorithm to differentiate between acceptable and unacceptable outcomes.
How Are AI Vision Techniques Used in Quality Control?
- Object Detection & Classification: The capability of identifying and classifying specific objects within the camera's field of view, crucial for sorting, assembly verification, and inventory management. This involves training AI models to identify and categorize specific objects, patterns, or features present within images.
- Anomaly Detection: The process of identifying unusual patterns or outliers in data that deviate significantly from expected behavior. This technique is frequently employed in quality control and cybersecurity to flag irregularities.
- Multimodal Models: An AI model capable of processing and generating information across multiple types of input and output modalities, such as text, images, audio, and video. An example is a model that can describe an image and generate captions or code from a diagram.
- Edge Learning: An AI technology where the processing and learning occur directly on the device ("at the edge") rather than in a centralized cloud environment. This approach typically requires smaller image sets and shorter training periods compared to traditional deep learning solutions, offering benefits in terms of latency and privacy.
How Do Vision Systems Integrate with Manufacturing Systems?
Modern vision systems must seamlessly integrate with existing manufacturing infrastructure through standardized communication protocols and interfaces. These connections enable automated decision-making, real-time process control, and enterprise-level quality management. The following terms cover the critical interfaces and processes that connect automated inspection systems to broader production control systems.
- PLC (Programmable Logic Controller) Integration: The connection between vision systems and industrial controllers that manage manufacturing processes, enabling automated decision-making and process control based on inspection results.
- HMI (Human Machine Interface): User interface systems that allow operators to interact with and monitor automated inspection systems, typically featuring touchscreens, status displays, and control panels for system operation and troubleshooting.
- SCADA Integration: Connection with Supervisory Control and Data Acquisition systems that collect, monitor, and analyze inspection data across multiple production lines or facilities for enterprise-level quality management.
- Reject Mechanisms/Sorting Systems: Automated physical systems that remove defective parts from the production line based on vision system decisions, including pneumatic ejectors, robotic sorters, and diverter gates.
- Ethernet/IP: Industrial Ethernet protocol that enables vision systems to communicate with PLCs and other factory automation devices over standard Ethernet networks, providing real-time data exchange and control integration.
- Profinet: Industrial Ethernet standard widely used in European manufacturing, allowing vision systems to integrate seamlessly with Siemens PLCs and other Profinet-compatible devices for coordinated automation control.
- Modbus: Serial communication protocol commonly used in industrial applications, enabling vision systems to exchange data with PLCs, HMIs, and other control devices through standardized register-based communication.
What Are Common Quality Inspection Applications?
These real-world applications demonstrate how machine vision and AI translate into tangible manufacturing benefits. Each application type presents distinct requirements for lighting, resolution, and processing speed—factors that should shape system architecture decisions from the beginning.
- Automated Optical Inspection (AOI): A widely adopted automated visual inspection method, particularly prevalent in the manufacturing of printed circuit boards (PCBs), liquid crystal displays (LCDs), and transistors. In AOI, a camera autonomously scans the device under test to identify both catastrophic failures (e.g., missing components) and subtle quality defects (e.g., fillet size, shape, or component skew). It is a non-contact testing method.
- Defect Detection: The primary function of many AQI systems, involving the identification of flaws, abnormalities, or contaminants in products that deviate from specified quality standards.
- Surface Inspection: Focused on examining the surface quality of products to detect imperfections such as scratches, dents, or discolorations.
- Packaging Verification: Ensures that product packaging is properly sealed, accurately labeled, and free from damage, which is critical for product safety and consumer information.
- Filling & Cap Inspection: Confirms that containers hold the correct volume or level of liquid, preventing both underfilling and overfilling. Cap inspection ensures the presence, correct positioning, and proper closure of caps on bottles or containers.
- Gauging/Metrology: Involves the precise measurement of object dimensions, such as length, width, height, or diameter, often to ensure components meet exact specifications.
- OCR & Barcode Reading: The automated process of reading machine-readable representations of information, such as one-dimensional barcodes or two-dimensional data matrix codes, for tracking and identification purposes.
How Do You Measure Machine Vision and AI System Performance?
Evaluating system performance requires careful attention to which metrics matter most for specific applications. The choice of metrics to prioritize depends heavily on quality requirements and the relative costs of different types of errors in production processes—getting this alignment wrong can lead to systems that appear to perform well on paper but fail in practice.
What Are the Key Classification Metrics for Vision Systems?
The choice between these metrics involves important trade-offs: high precision minimizes false alarms but may miss some defects, while high recall catches more defects but may flag acceptable parts as defective. Teams need to understand which errors are more costly in their specific context.
- Accuracy: The simplest metric, defined as the proportion of all correct predictions (both True Positives and True Negatives) out of the total number of predictions made. While straightforward, accuracy can be misleading, particularly in datasets where one class is significantly more prevalent than others (imbalanced datasets).
- Precision: The ratio of true positives to the total number of positive predictions made by the model. Precision focuses on minimizing false positives, indicating how many of the identified positive cases were actually correct. This becomes critical when false alarms are costly or disruptive to production flow.
- Recall: The ratio of true positives to all actual positives in the ground truth. Recall focuses on minimizing false negatives, indicating how many of the actual positive cases the model successfully identified. Critical when missing defects could impact safety or customer satisfaction.
- Confusion Matrix: A fundamental tabular visualization that maps the model's predictions against the actual ground-truth labels. It forms the basis for calculating many other classification metrics.
- True Positive (TP): Instances where the model correctly predicted the positive class.
- True Negative (TN): Instances where the model correctly predicted the negative class.
- False Positive (FP): Instances where the model incorrectly predicted the positive class when the actual class was negative (Type-I error).
- False Negative (FN): Instances where the model incorrectly predicted the negative class when the actual class was positive (Type-II error).
- F1-Score: The harmonic mean of precision and recall. This metric provides a balanced measure, particularly useful for class-imbalanced datasets where accuracy alone can be misleading.
- AUROC: A metric that quantifies a model's ability to distinguish between positive and negative classes across various classification thresholds. It is derived by plotting the True Positive Rate against the False Positive Rate and measures how well a model separates positive and negative classes, often used for anomaly detection.
What Are the Key Regression Metrics for Vision Systems?
These metrics apply when inspection systems measure continuous values like dimensions, fill levels, or color intensity rather than simply classifying pass/fail.
- Mean Absolute Error (MAE): The average of the absolute differences between the ground truth (actual) values and the predicted values. MAE is robust to outliers and provides a direct measure of the average prediction error.
- Mean Squared Error (MSE): The average of the squared differences between the target values and the predicted values. MSE penalizes larger errors more heavily due to the squaring operation and is differentiable, making it suitable for optimization algorithms.
- Root Mean Squared Error (RMSE): The square root of the MSE. RMSE addresses some of the scale interpretation challenges of MSE, providing an error measure in the same units as the target variable, making it easier to interpret.
- R² (Coefficient of Determination): A statistical measure that indicates the proportion of the variance in the dependent variable that is predictable from the independent variables. In regression, it indicates how well the model's predictions explain the variability of the actual outcomes. Values closer to 1.0 indicate better model performance.
Applying This Knowledge
Machine vision and AI inspection technologies continue reshaping manufacturing quality control, moving from simple pass/fail decisions to sophisticated defect analysis and process optimization. The terminology in this glossary provides the foundation for navigating vendor conversations, writing technical specifications, and making informed technology investments.
Whether evaluating your first automated inspection system or expanding existing capabilities, this reference supports technical discussions, project planning, and team development. Understanding these concepts helps distinguish between marketing claims and technical capabilities, leading to more successful deployments and better return on investment.
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