Machine Vision Design

This program provides a comprehensive grounding in machine vision system design, covering the core components, purposes and practical applications of machine vision, and how to evaluate image quality against customer requirements and scene constraints. Participants will then explore machine vision camera specifications and selection, examining camera types, sensor attributes, and shutter characteristics, before moving into camera lenses and imaging systems, including lens selection criteria, image circles, focal lengths, and imaging performance factors such as f-number, depth of field, and common lens aberrations. The program concludes with machine vision lighting techniques, covering light-matter interaction and lighting selection criteria such as color, quality, angle, position, type, duration, and shape, equipping participants to design and specify complete, high-performing machine vision systems.

• Explain the machine vision model, its components, purposes and applications, and identify the criteria of a good image based on customer requirements and scene constraints.

• Select appropriate machine vision cameras based on sensor type, resolution, sensitivity, shutter type, frame rate, and camera interface.

• Evaluate and select camera lenses based on mount type, lens type, image circle, focal length, and imaging performance factors such as f-number, depth of field, and lens aberrations.

• Apply machine vision lighting selection criteria, including color, quality, angle, position, type, duration and shape, to optimize the performance of a machine vision system.

Duration

3-Full Day | 24 hours (include 1 hour lunch break each day)

Methodology

Face to Face | Lecture, Discussion and Case Study Review

Target Audience

Semiconductor, Electronics & Manufacturing | Suitable for individuals new to machine vision, including vision engineers, sales engineers, software engineers, technical support engineers, and manufacturing engineers.

Course Level

Beginner

Pre-requisites

No prior machine vision experience required; a basic technical or engineering background is an advantage.