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Quest Training

Machine Vision for Automated Quality Inspection Training Course

Explains how cameras, lighting and image processing software automate product inspection, detect defects and make manufacturing quality more consistent.

Q4DX · Quality 4.0 & Digital QualityAll Levels#2876ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Quality Management & Operational ExcellenceCategories:Quality 4.0 & Digital Quality

Course Overview

The Machine Vision for Automated Quality Inspection Training Course provides practical knowledge of machine vision technologies used to automate product inspection, detect defects, and improve quality consistency in manufacturing and industrial operations. It focuses on how cameras, lighting systems, image processing software, and automated inspection equipment can replace or complement manual inspection, increasing inspection speed, repeatability, and measurement accuracy. The course covers machine vision system components, image acquisition, lighting selection, camera and lens configuration, image processing techniques, and inspection criteria development. Participants learn how to detect surface defects, verify product dimensions, inspect component presence and orientation, read identification codes, and evaluate product conformity against defined specifications. The programme also introduces machine vision integration with production lines, programmable logic controllers (PLCs), industrial automation systems, and quality data platforms. Through practical exercises and industrial case studies, participants develop inspection concepts, select appropriate imaging configurations, define defect detection rules, and evaluate system performance. The course supports organizations in reducing inspection errors, minimizing scrap and rework, improving production throughput, and implementing reliable, data-driven automated quality inspection.

Learning Objectives

  • Explain the principles and industrial applications of machine vision in quality inspection.
  • Identify the main components of an automated vision inspection system.
  • Evaluate camera, lens, lighting, and image acquisition requirements.
  • Apply image processing techniques to identify defects and assess product conformity.
  • Develop inspection criteria for surface defects, dimensions, orientation, and component presence.
  • Understand the use of rule-based vision algorithms and introductory AI-based inspection methods.
  • Integrate machine vision systems with production equipment and industrial controls.
  • Evaluate inspection accuracy, repeatability, false acceptance, and false rejection rates.
  • Identify system limitations and factors affecting inspection reliability.
  • Develop an implementation and validation plan for automated quality inspection.

Who Should Attend

Quality assurance and quality control engineers. Manufacturing and production engineers. Industrial automation and control engineers. Machine vision and inspection system technicians. Process and industrial engineers. Metrology and measurement specialists. Quality managers and continuous improvement professionals. Maintenance and reliability engineers supporting inspection equipment. Manufacturing technology and Industry 4.0 specialists. Professionals involved in automated testing and production quality improvement.

Learning Outcomes

  • Explain machine vision system architecture and its role in automated inspection.
  • Select suitable cameras, lenses, lighting arrangements, and imaging configurations.
  • Define image acquisition requirements for specific inspection applications.
  • Apply basic image processing concepts to industrial inspection scenarios.
  • Develop inspection rules for detecting defects and verifying product characteristics.
  • Distinguish conventional rule-based vision from AI-based visual inspection.
  • Understand machine vision integration with PLCs and production line controls.
  • Evaluate inspection performance using accuracy, repeatability, and error rates.
  • Identify common causes of missed defects and false rejection.
  • Prepare a structured plan for implementing, testing, and maintaining a vision inspection solution.

Course Outline

Day 1

Machine Vision Fundamentals and System Components

  • Introduction to machine vision and automated quality inspection.
  • Applications in manufacturing, assembly, packaging, and product verification.
  • Machine vision system architecture and component selection.
  • Industrial cameras, image sensors, resolution, and frame rates.
  • Lens selection, field of view, working distance, and depth of field.
  • Lighting techniques, illumination angles, contrast, and reflection control.
  • Image acquisition requirements and inspection environment considerations.
  • Practical Exercise: Define a machine vision inspection concept for a selected product, specifying the camera, lens, lighting, and image acquisition requirements.
Day 2

Image Processing and Inspection Method Development

  • Image acquisition and image quality assessment.
  • Grayscale conversion, thresholding, filtering, and edge detection.
  • Image segmentation and region-of-interest selection.
  • Pattern matching, feature extraction, and geometric measurement.
  • Detecting scratches, cracks, contamination, missing components, and surface irregularities.
  • Presence, absence, orientation, and assembly verification.
  • Establishing inspection rules and acceptable quality limits.
  • Practical Exercise: Develop an inspection workflow using sample images to identify selected defects or verify product features through basic image processing techniques.
Day 3

Inspection Accuracy, Measurement, and System Validation

  • Defining inspection requirements and acceptance criteria.
  • Dimensional inspection and measurement calibration.
  • Evaluating repeatability, reproducibility, and measurement uncertainty.
  • Understanding false acceptance and false rejection.
  • Setting inspection thresholds and decision rules.
  • Creating reference samples and validating defect detection performance.
  • Managing changes in product appearance, lighting, and operating conditions.
  • Practical Exercise: Evaluate a sample inspection dataset, calculate basic detection and rejection performance measures, and recommend threshold adjustments to improve inspection reliability.
Day 4

Industrial Integration and Advanced Inspection Applications

  • Integrating vision systems with programmable logic controllers (PLCs).
  • Communication with production equipment and industrial networks.
  • Triggering image capture and synchronizing inspection with production cycles.
  • Automated product sorting, rejection mechanisms, and inspection traceability.
  • Reading barcodes, QR codes, and optical character recognition (OCR).
  • Introduction to AI-based vision and deep learning for complex defect detection.
  • Data exchange with manufacturing execution systems (MES) and quality management systems (QMS).
  • Practical Exercise: Design an automated inspection sequence that connects image acquisition, defect classification, PLC signaling, product rejection, and inspection result recording.
Day 5

Implementation, Performance Optimization, and Maintenance

  • Selecting pilot applications and defining implementation scope.
  • Evaluating technical feasibility, cycle time, and production integration requirements.
  • Establishing acceptance testing and commissioning procedures.
  • Monitoring inspection performance, false rejection, and missed defect rates.
  • Troubleshooting image quality, lighting, focus, and detection issues.
  • Maintaining cameras, lenses, lighting, and inspection configurations.
  • Reviewing inspection data to support continuous improvement.
  • Practical Exercise: Develop an implementation and validation plan for an automated vision inspection system, including performance targets, acceptance criteria, maintenance requirements, and ongoing monitoring measures.

Upcoming Dates

Selected date

Erbil · 14 – 18 Feb 2027 · Classroom

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Sharm El-SheikhScheduled
2 – 6 Nov 2026€4,300—
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RiyadhScheduled
2 – 6 Nov 2026€4,200—
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SingaporeScheduled
9 – 13 Nov 2026€6,700—
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AbujaScheduled
16 – 20 Nov 2026€5,100—
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AccraScheduled
23 – 27 Nov 2026€5,100€4,590until 2 Nov
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AmmanScheduled
29 Nov – 3 Dec 2026€3,100€2,790until 9 Nov
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AmsterdamScheduled
7 – 11 Dec 2026€5,400€4,860until 16 Nov
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BakuScheduled
14 – 18 Dec 2026€4,800€4,320until 23 Nov
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BarcelonaScheduled
21 – 25 Dec 2026€5,400€4,860until 30 Nov
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BangkokScheduled
21 – 25 Dec 2026€6,100€5,490until 30 Nov
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BeirutScheduled
27 – 31 Dec 2026€4,000€3,600until 7 Dec
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CairoScheduled
3 – 7 Jan 2027€3,300€2,970until 14 Dec
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Cape TownScheduled
11 – 15 Jan 2027€5,100€4,590until 21 Dec
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CasablancaScheduled
18 – 22 Jan 2027€4,300€3,870until 28 Dec
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DammamScheduled
24 – 28 Jan 2027€4,200€3,780until 4 Jan
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DohaScheduled
31 Jan – 4 Feb 2027€4,400€3,960until 11 Jan
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DubaiScheduled
7 – 11 Feb 2027€4,400€3,960until 18 Jan
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ErbilScheduled
14 – 18 Feb 2027€3,100€2,790until 25 Jan
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IstanbulScheduled
15 – 19 Feb 2027€4,700€4,230until 25 Jan
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JeddahScheduled
21 – 25 Feb 2027€4,200€3,780until 1 Feb
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KigaliScheduled
1 – 5 Mar 2027€5,100€4,590until 8 Feb
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Kuala LumpurScheduled
8 – 12 Mar 2027€4,400€3,960until 15 Feb
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LisbonScheduled
15 – 19 Mar 2027€5,400€4,860until 22 Feb
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MadridScheduled
22 – 26 Mar 2027€5,400€4,860until 1 Mar
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LondonScheduled
22 – 26 Mar 2027€5,600€5,040until 1 Mar
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MakkahScheduled
29 Mar – 2 Apr 2027€4,200€3,780until 8 Mar
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ManamaScheduled
5 – 9 Apr 2027€4,700€4,230until 15 Mar
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MuscatScheduled
12 – 16 Apr 2027€5,300€4,770until 22 Mar
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OnlineScheduled
18 – 22 Apr 2027€1,700€1,530until 29 Mar
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ParisScheduled
26 – 30 Apr 2027€5,700€5,130until 5 Apr
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Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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