[ { "Observation Type": "2D Measurement", "Sub-Parameters": "dimensions to be measured", "Example": "Diameter, thickness, etc.", "Relevance for Vision System Design": "Guides the sw algorithm to be used", "User Answer": "" }, { "Observation Type": "2D Measurement", "Sub-Parameters": "Dimenison Range", "Example": "100mm +/- .01mm", "Relevance for Vision System Design": "Guides the Field of View as we would know the dimension that is required.", "User Answer": "" }, { "Observation Type": "2D Measurement", "Sub-Parameters": "Tolerated error of measurement", "Example": "10 microns", "Relevance for Vision System Design": "Influences the selection of high-precision sensors and optics. Affects the algorithm's ability to distinguish between acceptable and unacceptable variances.", "User Answer": "" }, { "Observation Type": "Anomaly Detection", "Sub-Parameters": "Types of Anomaly defects to detect", "Example": "Scratches, dents, corrosion", "Relevance for Vision System Design": "Guides the development of specific algorithms for detecting each type of surface irregularity. Influences lighting and camera setup for optimal defect visualization.", "User Answer": "" }, { "Observation Type": "Anomaly Detection", "Sub-Parameters": "Minimum Defect Size", "Example": "Minimum detectable size: 0.5 mm", "Relevance for Vision System Design": "Determines the resolution and sensitivity of imaging equipment. Affects system's ability to detect and quantify defect severity.", "User Answer": "" }, { "Observation Type": "Print Defect", "Sub-Parameters": "Types of print defects to identify", "Example": "Smudging, misalignment", "Relevance for Vision System Design": "Impacts the development of algorithms for print quality control. Affects camera resolution and processing speed required to identify print errors effectively.", "User Answer": "" }, { "Observation Type": "Print Defect", "Sub-Parameters": "Minimum defect size", "Example": "Up to 2 mm", "Relevance for Vision System Design": "Sets the minimum threshold for print defect detection that needs to be detected. Anything smaller than this need not be detected (and thus flagged as a defect)", "User Answer": "" }, { "Observation Type": "Counting", "Sub-Parameters": "Types of objects/features to count", "Example": "Individual components, features", "Relevance for Vision System Design": "Dictates the design of counting algorithms and affects the system's processing speed. Influences camera setup for optimal object recognition and differentiation.", "User Answer": "" }, { "Observation Type": "Counting", "Sub-Parameters": "Min and Max object count", "Example": "Min = 0, Max = 100", "Relevance for Vision System Design": "Important for deciding training data to be collected for training the objects.", "User Answer": "" }, { "Observation Type": "3D Measurement", "Sub-Parameters": "Volume or spatial measurements needed", "Example": "Volume, surface area", "Relevance for Vision System Design": "Influences the selection of 3D imaging technologies (like stereoscopic cameras or laser scanners) and impacts algorithm complexity for spatial analysis.", "User Answer": "" }, { "Observation Type": "3D Measurement", "Sub-Parameters": "Accuracy and precision levels", "Example": "±0.1 mm", "Relevance for Vision System Design": "Guides the calibration process and selection of high-precision 3D measurement equipment. Impacts software algorithm development for accurate spatial analysis.", "User Answer": "" }, { "Observation Type": "Presence/Absence", "Sub-Parameters": "Details of objects/features to detect", "Example": "Missing components, color deviations", "Relevance for Vision System Design": "Critical for designing detection algorithms. Influences camera resolution and processing strategies to identify presence or absence of specific features or objects.", "User Answer": "" }, { "Observation Type": "Presence/Absence", "Sub-Parameters": "Acceptable variance levels", "Example": "Variance up to 5%", "Relevance for Vision System Design": "Sets the system's tolerance for detection errors, affecting the sensitivity and specificity of the algorithms. Impacts the choice of imaging technologies for accurate feature detection.", "User Answer": "" }, { "Observation Type": "OCR (Optical Character Recognition)", "Sub-Parameters": "Font types and sizes to be recognized", "Example": "Arial, size 12", "Relevance for Vision System Design": "Influences OCR algorithm development. Affects the choice of cameras capable of capturing various font sizes clearly.", "User Answer": "" }, { "Observation Type": "OCR (Optical Character Recognition)", "Sub-Parameters": "Reading speed and accuracy requirements", "Example": "99% accuracy at 2 characters per second", "Relevance for Vision System Design": "Dictates the balance between speed and accuracy for the OCR system. Impacts the selection of processing hardware for real-time character recognition.", "User Answer": "" }, { "Observation Type": "Code Reading (2D/1D)", "Sub-Parameters": "Types of codes to read (QR, Barcode)", "Example": "QR Codes, UPC Barcodes", "Relevance for Vision System Design": "Guides the development of algorithms for different types of code recognition. Influences camera selection for varying code sizes and distances.", "User Answer": "" }, { "Observation Type": "Code Reading (2D/1D)", "Sub-Parameters": "Reading distance and angle", "Example": "Up to 30 cm, 45° angle", "Relevance for Vision System Design": "Determines the system's ability to read codes from various angles and distances. Impacts camera positioning and field of view requirements.", "User Answer": "" }, { "Observation Type": "Mismatch Detection", "Sub-Parameters": "Specific features to compare for mismatches", "Example": "Component shapes, color mismatches", "Relevance for Vision System Design": "Essential for algorithm development to identify discrepancies in product features. Influences imaging and processing requirements to compare and detect mismatches accurately.", "User Answer": "" }, { "Observation Type": "Mismatch Detection", "Sub-Parameters": "Tolerance levels for mismatches", "Example": "Tolerances up to 5%", "Relevance for Vision System Design": "Dictates the system's sensitivity to mismatches, affecting algorithm design for defect detection and tolerance specification.", "User Answer": "" }, { "Observation Type": "Classification", "Sub-Parameters": "Categories of classes to be identified", "Example": "Different product types, defect categories", "Relevance for Vision System Design": "Crucial for developing classification algorithms. Influences sensor and processing capabilities to differentiate between various classes based on physical features.", "User Answer": "" }, { "Observation Type": "Classification", "Sub-Parameters": "Features defining each class", "Example": "Shape, size, color patterns", "Relevance for Vision System Design": "Guides the system's ability to recognize and categorize objects", "User Answer": "" }, { "Observation Type": "Assembly Verification", "Sub-Parameters": "Checklist of components or features to verify", "Example": "All screws, connectors in place", "Relevance for Vision System Design": "Influences the development of verification algorithms and imaging strategies to ensure complete assembly. Affects camera setup for capturing all assembly components.", "User Answer": "" }, { "Observation Type": "Assembly Verification", "Sub-Parameters": "Sequence of assembly to be followed", "Example": "Step-by-step assembly verification", "Relevance for Vision System Design": "Guides the programming of the system for sequential assembly verification. Affects the design of user interfaces and reporting features for assembly process tracking.", "User Answer": "" }, { "Observation Type": "Color Verification", "Sub-Parameters": "Color standards or samples to match", "Example": "Pantone 300C", "Relevance for Vision System Design": "Dictates the need for color-accurate imaging systems. Influences the development of algorithms for color matching and verification, impacting camera selection and lighting conditions for accurate color reproduction.", "User Answer": "" } ]