디지털 검사 시장 - 세계 예측(2026-2032년)

The Digital Inspection Market is projected to grow by USD 35.10 billion at a CAGR of 8.03% by 2032.

Digital Inspection: Executive Summary and Industry Context

Digital inspection is rapidly becoming a core pillar of industrial quality assurance, asset integrity management, and regulatory compliance across manufacturing, energy, automotive, aerospace, electronics, infrastructure, healthcare, and process industries. The term refers to the use of connected inspection tools, machine vision, robotics, sensors, 3D metrology, non-destructive testing, computer vision, cloud platforms, and analytics to evaluate products, components, facilities, and field assets with greater consistency than manual-only methods. Its importance is rising as organizations manage tighter tolerances, complex supply chains, skilled labor shortages, and greater pressure to document traceability across production and maintenance workflows.

The shift from paper-based inspection to digital inspection systems is supported by widely adopted quality management, safety, calibration, and non-destructive testing standards, including frameworks used for ISO 9001-based quality systems, ISO/IEC 17025 testing and calibration competence, and sector-specific inspection requirements. Digital inspection improves repeatability by standardizing capture methods, reducing subjective interpretation, and creating auditable records that can be linked to product lifecycle management, enterprise resource planning, manufacturing execution, quality management, and maintenance systems. In regulated and high-reliability environments, these capabilities strengthen defect prevention, root-cause analysis, first-pass yield improvement, and risk-based maintenance decisions.

Search interest and procurement activity around digital inspection, automated visual inspection, AI inspection, machine vision inspection, digital quality control, 3D metrology, remote inspection, and non-destructive testing are being driven by the need to detect defects earlier, reduce rework, improve worker safety, and support remote collaboration. As industrial operations become more software-defined, digital inspection is evolving from a quality-control checkpoint into a continuous intelligence layer that connects design, production, field service, asset integrity, and compliance documentation.

Transformative Shifts Reshaping Digital Inspection Workflows

The digital inspection landscape is being transformed by automation, connected data ecosystems, and the convergence of imaging, sensing, robotics, and analytics. Traditional inspection models often relied on periodic sampling, manual documentation, and expert interpretation at isolated points in the production or asset lifecycle. Modern digital inspection workflows increasingly use inline cameras, smart sensors, robotic crawlers, drones, structured light scanning, computed tomography, ultrasound, eddy current testing, magnetic particle testing, dye penetrant testing, thermal imaging, laser scanning, and cloud-based dashboards to capture inspection data continuously or at higher frequency.

A major shift is the move from reactive defect detection to proactive quality intelligence. In manufacturing, inspection data is being integrated with process parameters, machine health indicators, batch records, and material traceability to identify patterns that contribute to nonconformance. In infrastructure and energy, digital inspection supports condition monitoring and risk-based prioritization by helping teams identify corrosion, cracks, misalignment, wear, leakage, coating degradation, and structural anomalies without relying solely on visual walkdowns. Remote inspection has also gained traction where safety risks, travel constraints, confined spaces, offshore assets, or geographic dispersion make on-site expert presence inefficient.

Another transformative change is the growing role of interoperability. Inspection results increasingly need to flow across quality management systems, maintenance platforms, digital twins, compliance repositories, and industrial data historians. This requirement is encouraging adoption of standardized data formats, secure connectivity, workflow automation, and application programming interfaces. At the same time, cybersecurity and data governance have become central considerations because inspection systems may connect to operational technology networks and generate sensitive engineering, design, production, or asset-condition data.

Workforce dynamics are also reshaping the sector. Skilled inspectors remain essential, particularly for interpreting complex findings and validating critical decisions, but digital tools are augmenting their productivity. Assisted defect recognition, guided workflows, mobile reporting, electronic checklists, remote expert review, and automated report generation reduce repetitive tasks and improve documentation consistency. The result is a more resilient inspection environment in which human expertise is supported by reliable digital evidence, standardized procedures, and scalable analytics.

Cumulative Impact of Artificial Intelligence on Digital Inspection

Artificial intelligence is creating a cumulative impact on digital inspection by improving how visual, acoustic, thermal, dimensional, radiographic, and sensor-based data is interpreted. AI-enabled inspection systems can be trained to identify scratches, dents, surface contamination, weld defects, porosity, missing components, assembly errors, coating inconsistencies, foreign object debris, dimensional deviations, and structural anomalies in large volumes of inspection imagery and signal data. In high-speed production lines, computer vision helps reduce dependence on manual visual checks while supporting consistent detection criteria and faster escalation of quality issues.

The strongest value of AI in digital inspection comes from combining detection, classification, measurement, and workflow decision support. Deep learning models can highlight likely defects, rank severity, and route findings for human validation. When integrated with historical inspection records, process parameters, maintenance data, and environmental conditions, AI can support trend analysis and early warning indicators for recurring quality issues. In asset-intensive industries, AI-assisted analysis of drone imagery, ultrasonic readings, vibration data, acoustic emissions, and thermal patterns can help maintenance teams prioritize interventions based on risk and condition evidence.

However, AI adoption in digital inspection requires disciplined governance. Model performance depends on representative training data, high-quality labeling, controlled imaging conditions, validation against accepted inspection standards, and continuous monitoring for drift. False positives can create unnecessary rework, while false negatives can introduce safety, warranty, and reliability risks. For this reason, leading digital inspection strategies emphasize human-in-the-loop review, explainable outputs where practical, controlled deployment, documented validation, cybersecurity review, and alignment with quality management requirements.

AI also expands the role of digital twins and predictive quality. Inspection outcomes can be mapped to product, process, or asset models to create a feedback loop between design intent and real-world performance. Over time, this enables organizations to reduce recurring defects, optimize inspection plans, refine process controls, and improve design-for-manufacturing and maintenance planning. The cumulative effect is not merely faster inspection; it is a shift toward evidence-based quality intelligence embedded throughout industrial operations.

Key Regional Insights Across Asia-Pacific, North America, Latin America, Europe, Middle East, and Africa

Asia-Pacific is a major center of digital inspection adoption because the region combines large-scale electronics manufacturing, automotive production, semiconductor activity, shipbuilding, renewable energy development, mining, and infrastructure expansion. China, Japan, South Korea, India, and Australia are advancing machine vision, robotics, smart factories, and automated non-destructive testing as manufacturers pursue higher quality consistency and traceability. The region's strong manufacturing base supports deployment of inline optical inspection, 3D measurement, X-ray inspection, and AI-assisted defect detection, while infrastructure, utilities, and mining activities create demand for drone-based inspection, remote monitoring, and condition assessment.

North America is characterized by high adoption of automation, quality management systems, aerospace and defense requirements, energy infrastructure inspection, and advanced manufacturing initiatives. The United States and Canada have mature ecosystems for machine vision, robotics, industrial software, metrology, and non-destructive testing, supported by strong safety, certification, and regulatory expectations in sectors such as aviation, medical devices, oil and gas, utilities, nuclear energy, and transportation. Digital inspection is increasingly used to reduce downtime, improve compliance documentation, standardize inspection evidence, and enable remote expert collaboration across distributed facilities.

Latin America is adopting digital inspection in industries tied to mining, energy, automotive assembly, food processing, construction, ports, and transportation infrastructure. Brazil and Mexico are important anchors due to their industrial bases and integration with global supply chains. Digital inspection use cases in the region often focus on operational reliability, asset integrity, maintenance optimization, and export-quality compliance. Adoption of connected industrial systems is gradual and influenced by workforce training, investment cycles, connectivity availability, and the need to modernize aging infrastructure.

Europe shows strong momentum in digital inspection due to stringent product quality expectations, worker safety rules, environmental compliance, advanced automotive manufacturing, aerospace activity, pharmaceuticals, energy systems, and industrial automation. Germany, France, Italy, Spain, and the United Kingdom use digital inspection to support precision manufacturing, traceability, regulated production environments, and lifecycle asset documentation. The region's emphasis on data protection, sustainability, machinery safety, and standardized certification is shaping inspection platforms that prioritize secure data handling, auditable records, interoperability, and conformity evidence.

The Middle East is increasingly applying digital inspection to oil and gas, petrochemicals, power generation, aviation, construction, ports, utilities, and smart city infrastructure. Countries in the Gulf are using drones, robotics, remote sensing, thermal imaging, and ultrasonic inspection to reduce worker exposure in hazardous environments and improve asset monitoring across geographically dispersed facilities. Africa presents a diverse landscape in which mining, energy, utilities, transportation corridors, ports, and infrastructure maintenance are key drivers. Digital inspection adoption is supported by the need to improve safety, extend asset life, reduce unplanned downtime, and manage remote operations, though connectivity, skills development, and capital allocation remain important implementation factors.

Key Group Insights Covering ASEAN, GCC, European Union, BRICS, G7, and NATO

ASEAN countries are strengthening their role in digital inspection through electronics manufacturing, automotive assembly, food processing, energy projects, logistics facilities, and expanding industrial parks. As regional supply chains become more sophisticated, manufacturers are adopting automated visual inspection, dimensional metrology, barcode and serialization verification, and digital quality records to meet export requirements and improve process consistency. The region's diversity means adoption varies by industrial maturity, but investment in smart manufacturing, technical education, and workforce upskilling is supporting broader use of inspection automation.

The GCC is closely associated with asset integrity, safety, and remote inspection in oil and gas, petrochemicals, power, desalination, water infrastructure, aviation, ports, and large construction programs. Harsh operating environments, large-scale industrial assets, and strict safety requirements make drones, robotic inspection, thermal imaging, ultrasonic testing, corrosion monitoring, and cloud-based reporting especially relevant. Digital inspection in the GCC is also tied to national diversification programs that encourage advanced technology deployment in industrial, energy, logistics, and infrastructure sectors.

The European Union provides a highly structured environment for digital inspection because of its emphasis on product conformity, machinery safety, environmental regulation, data governance, and cross-border industrial standards. EU manufacturers and infrastructure operators are adopting digital quality control and automated inspection to support traceability, sustainability documentation, maintenance planning, and compliance with strict regulatory frameworks. The EU's focus on digital transformation and industrial resilience encourages secure, interoperable inspection platforms that integrate with broader manufacturing, quality, and lifecycle management systems.

BRICS economies reflect a broad mix of manufacturing scale, infrastructure needs, energy assets, mining activity, and technology development. China and India drive significant demand through industrial production, infrastructure expansion, and smart factory initiatives, while Brazil, Russia, and South Africa have important use cases in mining, energy, heavy industry, aviation, and transportation infrastructure. Across BRICS, digital inspection is being used to improve reliability, reduce dependence on manual documentation, support quality requirements in domestic and export-oriented industries, and strengthen asset monitoring in remote or harsh operating environments.

G7 countries generally show advanced adoption of digital inspection because of mature industrial automation, high labor costs, strict safety expectations, and strong participation in aerospace, automotive, healthcare, electronics, energy, and precision manufacturing. These economies are more likely to deploy AI-enabled inspection, robotic metrology, digital twins, automated non-destructive testing, and integrated quality platforms across complex production and asset-management workflows. NATO countries add a defense and security dimension, where inspection reliability, supply chain assurance, equipment readiness, controlled documentation, and data integrity are critical. In these environments, secure digital inspection workflows support maintenance, manufacturing quality, lifecycle assurance, and readiness for complex systems.

Key Country Insights for Digital Inspection Adoption and Use Cases

The United States is a leading adopter of digital inspection across aerospace, defense, automotive, medical devices, semiconductors, energy, utilities, rail, and advanced manufacturing, with emphasis on automation, traceability, safety, and regulatory documentation. Canada shows strong use cases in energy, mining, transportation, utilities, nuclear facilities, and aerospace, where remote inspection, drones, and non-destructive testing support safety and asset integrity across large geographies. Mexico's role in automotive, aerospace, electronics, appliances, and nearshore manufacturing supports demand for machine vision inspection, digital quality control, dimensional verification, and export-compliant inspection records.

Brazil applies digital inspection across oil and gas, mining, aviation, automotive, agribusiness processing, utilities, and infrastructure, with asset reliability and maintenance optimization as central priorities. The United Kingdom uses digital inspection in aerospace, rail, energy, construction, nuclear decommissioning, maritime assets, and advanced manufacturing, supported by strong safety and engineering standards. Germany is deeply associated with precision manufacturing, automotive production, industrial automation, robotics, machinery, and metrology, making digital inspection a key component of smart factory and quality assurance strategies. France applies digital inspection in aerospace, nuclear energy, transportation, pharmaceuticals, defense-related manufacturing, and industrial production, where compliance, reliability, and documentation are essential.

Russia's digital inspection demand is linked to energy, mining, heavy industry, transportation infrastructure, aerospace, and defense-related manufacturing, with emphasis on non-destructive testing and asset monitoring in challenging environments. Italy uses digital inspection across machinery, automotive components, aerospace, pharmaceuticals, food processing, packaging, and industrial equipment, often focusing on product quality, process verification, and conformity documentation. Spain's adoption is supported by automotive production, renewable energy, rail, aerospace, shipbuilding, and infrastructure maintenance, where inspection digitization improves reliability, worker safety, and records management.

China is advancing digital inspection through its large manufacturing ecosystem, electronics production, automotive and electric vehicle supply chains, semiconductors, batteries, infrastructure, and robotics adoption. India is expanding use of digital inspection across automotive, pharmaceuticals, rail, energy, electronics, steel, aerospace, and infrastructure as manufacturers modernize quality systems and improve compliance documentation. Japan has strong digital inspection capabilities in precision manufacturing, automotive, electronics, robotics, semiconductors, materials, and high-reliability industrial systems, emphasizing accuracy, repeatability, and defect prevention. Australia applies digital inspection in mining, energy, utilities, infrastructure, ports, and transportation, where remote operations and worker safety are prominent drivers. South Korea uses digital inspection extensively in electronics, semiconductors, automotive, shipbuilding, batteries, displays, and advanced manufacturing, with machine vision and automated inspection supporting high-volume quality control.

Actionable Recommendations for Digital Inspection Leaders

Industry leaders should treat digital inspection as a strategic quality and asset intelligence capability rather than a standalone technology purchase. The first priority is to map inspection pain points across production, maintenance, safety, warranty, and compliance workflows, then identify where digitization can reduce variability, improve traceability, or prevent costly failures. High-value use cases typically include repetitive visual inspection, hazardous or hard-to-access asset inspection, dimensional verification, weld and surface defect detection, corrosion monitoring, assembly validation, and regulated documentation.

Organizations should build implementation roadmaps that combine hardware, software, data governance, cybersecurity, and workforce enablement. Selecting inspection technologies requires careful alignment with defect types, environmental conditions, required accuracy, throughput, regulatory expectations, calibration requirements, and integration needs. Leaders should validate AI-enabled inspection models using representative datasets and controlled acceptance criteria before scaling them across facilities. Human-in-the-loop review remains essential for critical inspections, especially where safety, warranty, legal, or regulatory outcomes are involved.

Data integration should be a core requirement. Inspection records are most valuable when linked to manufacturing execution systems, quality management platforms, maintenance systems, product lifecycle data, asset registries, and digital twins. This connectivity enables root-cause analysis, closed-loop corrective action, predictive maintenance, inspection-plan optimization, and continuous improvement. Leaders should also define data ownership, retention policies, access controls, audit trails, model governance, and cybersecurity responsibilities early in the deployment process.

To accelerate adoption, companies should invest in inspector training, change management, and cross-functional collaboration between quality, operations, engineering, maintenance, information technology, operational technology, and cybersecurity teams. Pilot projects should be measured through defect detection consistency, reporting time reduction, rework reduction, safety improvement, audit readiness, and documentation quality rather than technology novelty. Over time, a scalable digital inspection program can strengthen operational resilience, reduce risk, improve customer confidence, and support evidence-based decision-making.

Research Methodology for Evidence-Based Digital Inspection Analysis

A robust research methodology for digital inspection combines primary industry validation with secondary analysis of verified technical, regulatory, and operational sources. Primary inputs typically include structured discussions with quality managers, inspection engineers, plant leaders, maintenance specialists, non-destructive testing professionals, automation experts, metrology specialists, system integrators, safety professionals, and procurement stakeholders across manufacturing and asset-intensive industries. These conversations help identify adoption drivers, implementation barriers, workflow priorities, technology selection criteria, and governance requirements.

Secondary research should draw from standards bodies, government industrial and safety agencies, patent filings, technical publications, certification frameworks, regulatory guidance, trade associations, academic research, and public documentation on automation, machine vision, metrology, robotics, artificial intelligence, cybersecurity, and non-destructive testing. Cross-verification is essential to ensure that insights reflect current industrial practice rather than isolated vendor claims. Particular attention should be paid to use-case evidence, inspection standards, calibration requirements, interoperability needs, AI validation practices, cybersecurity considerations, and regional regulatory conditions.

The analysis process should segment findings by technology, inspection method, deployment environment, end-use industry, geography, and workflow maturity. Qualitative triangulation helps compare perspectives from equipment users, technology providers, regulators, standards specialists, and domain experts. Because digital inspection spans both operational technology and information technology, methodology should also evaluate integration readiness, data architecture, human factors, validation procedures, and governance requirements.

All findings should be reviewed for factual consistency, practical relevance, and alignment with real-world deployment evidence. Since market estimation, sizing, share analysis, and forecasting are excluded, the methodology focuses on evidence-backed trends, adoption patterns, application areas, regional dynamics, standards alignment, and strategic implications. This approach supports an executive summary that is practical, verifiable, and useful for decision-makers evaluating digital inspection initiatives.

Conclusion: Digital Inspection as a Foundation for Quality and Asset Intelligence

Digital inspection is redefining how organizations verify quality, maintain assets, document compliance, and manage operational risk. By combining machine vision, robotics, sensors, non-destructive testing, cloud platforms, AI analytics, and connected workflows, inspection is moving from a manual checkpoint toward a continuous intelligence function. This evolution is especially important in industries where product reliability, worker safety, regulatory compliance, audit readiness, and uptime are critical.

Regional and country-level adoption patterns show that digital inspection is not confined to one sector or geography. Advanced manufacturing economies are using it to improve precision, repeatability, automation, and quality documentation, while resource-rich and infrastructure-focused regions are applying it to remote monitoring, asset integrity, and safety. Economic and strategic groups such as ASEAN, the GCC, the European Union, BRICS, G7, and NATO demonstrate how industrial structure, regulation, security priorities, infrastructure needs, and workforce capabilities influence inspection strategies.

Artificial intelligence is intensifying the value of digital inspection by enabling faster defect recognition, severity classification, anomaly detection, predictive quality insights, and more consistent prioritization of inspection findings. Yet successful adoption depends on validated models, high-quality data, human expertise, cybersecurity, calibration discipline, and integration with enterprise and operational systems. Organizations that approach digital inspection as an integrated quality and asset intelligence program will be better positioned to reduce risk, improve documentation, enhance operational resilience, and support evidence-based industrial decisions.

The future of digital inspection will be shaped by secure connectivity, interoperable data, advanced sensors, autonomous inspection platforms, digital twins, and human-centered AI. Industry leaders that invest in scalable governance, workforce capability, and evidence-based deployment will gain stronger control over quality, safety, compliance, and operational performance.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Definition
• 1.3. Market Segmentation & Coverage
• 1.4. Years Considered for the Study
• 1.5. Currency Considered for the Study
• 1.6. Language Considered for the Study
• 1.7. Key Stakeholders

2. Research Methodology

• 2.1. Introduction
• 2.2. Research Design
  • 2.2.1. Primary Research
  • 2.2.2. Secondary Research
• 2.3. Research Framework
  • 2.3.1. Qualitative Analysis
  • 2.3.2. Quantitative Analysis
• 2.4. Market Size Estimation
  • 2.4.1. Top-Down Approach
  • 2.4.2. Bottom-Up Approach
• 2.5. Data Triangulation
• 2.6. Research Outcomes
• 2.7. Research Assumptions
• 2.8. Research Limitations

3. Executive Summary

• 3.1. Introduction
• 3.2. CXO Perspective
• 3.3. Market Size & Growth Trends
• 3.4. New Revenue Opportunities
• 3.5. Next-Generation Business Models
• 3.6. Industry Roadmap

4. Market Overview

• 4.1. Introduction
• 4.2. Industry Ecosystem & Value Chain Analysis
  • 4.2.1. Supply-Side Analysis
  • 4.2.2. Demand-Side Analysis
  • 4.2.3. Stakeholder Analysis
• 4.3. Market Dynamics
  • 4.3.1. Key Drivers
  • 4.3.2. Key Restraints
  • 4.3.3. Key Opportunities
  • 4.3.4. Key Challenges
• 4.4. Porter's Five Forces Analysis
• 4.5. PESTLE Analysis
• 4.6. Market Outlook
  • 4.6.1. Near-Term Market Outlook (0-2 Years)
  • 4.6.2. Medium-Term Market Outlook (3-5 Years)
  • 4.6.3. Long-Term Market Outlook (5-10 Years)
• 4.7. Go-to-Market Strategy

5. Market Insights

• 5.1. Consumer Insights & End-User Perspective
• 5.2. Consumer Experience Benchmarking
• 5.3. Opportunity Mapping
• 5.4. Distribution Channel Analysis
• 5.5. Pricing Trend Analysis
• 5.6. Regulatory Compliance & Standards Framework
• 5.7. ESG & Sustainability Analysis
• 5.8. Disruption & Risk Scenarios
• 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of Artificial Intelligence 2026

7. Digital Inspection Market, by Technology Type

• 7.1. Introduction
• 7.2. Machine Vision Systems
  • 7.2.1. 2D Machine Vision
  • 7.2.2. 3D Machine Vision
  • 7.2.3. Stereo Vision Systems
• 7.3. Deep Learning-Based Inspection
• 7.4. Optical Inspection Systems
• 7.5. X-ray & CT Digital Inspection
• 7.6. Ultrasonic Digital Inspection

8. Digital Inspection Market, by Inspection Type

• 8.1. Introduction
• 8.2. Surface Inspection
  • 8.2.1. Scratch Detection
  • 8.2.2. Contamination Detection
• 8.3. Dimensional Inspection
  • 8.3.1. Measurement Verification
  • 8.3.2. Tolerance Analysis
• 8.4. Structural Inspection
• 8.5. Functional Inspection
• 8.6. Cosmetic Inspection

9. Digital Inspection Market, by Deployment Type

• 9.1. Introduction
• 9.2. Inline Inspection Systems
• 9.3. Offline Inspection Systems
• 9.4. At-Line Inspection Systems
• 9.5. Portable Inspection Systems

10. Digital Inspection Market, by Component

• 10.1. Introduction
• 10.2. Hardware
  • 10.2.1. Cameras & Sensors
  • 10.2.2. Lighting Systems
  • 10.2.3. Robots & Automation Arms
• 10.3. Software
  • 10.3.1. Image Processing Software
  • 10.3.2. AI Defect Detection Platforms
• 10.4. Services
  • 10.4.1. System Integration Services
  • 10.4.2. Calibration & Maintenance Services

11. Digital Inspection Market, by End-use Industry

• 11.1. Introduction
• 11.2. Manufacturing
• 11.3. Automotive
• 11.4. Electronics & Semiconductor
• 11.5. Aerospace & Defense
• 11.6. Energy & Utilities
• 11.7. Food & Beverage
• 11.8. Pharmaceuticals & Healthcare
• 11.9. Construction & Infrastructure

12. Digital Inspection Market, by Region

• 12.1. Asia-Pacific
• 12.2. North America
• 12.3. Latin America
• 12.4. Europe
• 12.5. Middle East
• 12.6. Africa

13. Digital Inspection Market, by Group

• 13.1. ASEAN
• 13.2. GCC
• 13.3. European Union
• 13.4. BRICS
• 13.5. G7
• 13.6. NATO

14. Digital Inspection Market, by Country

• 14.1. United States
• 14.2. Canada
• 14.3. Mexico
• 14.4. Brazil
• 14.5. United Kingdom
• 14.6. Germany
• 14.7. France
• 14.8. Russia
• 14.9. Italy
• 14.10. Spain
• 14.11. China
• 14.12. India
• 14.13. Japan
• 14.14. Australia
• 14.15. South Korea

15. Competitive Landscape

• 15.1. Market Share Analysis, 2025
• 15.2. FPNV Positioning Matrix, 2025
• 15.3. Market Concentration Analysis, 2025
  • 15.3.1. Concentration Ratio (CR)
  • 15.3.2. Herfindahl Hirschman Index (HHI)
• 15.4. Recent Developments & Impact Analysis, 2025
• 15.5. Product Portfolio Analysis, 2025
• 15.6. Benchmarking Analysis, 2025

16. Company Profiles

• 16.1. ABB Ltd.
• 16.2. ASML Holding N.V.
• 16.3. Basler AG
• 16.4. Basler Vision Technologies
• 16.5. Bosch Rexroth AG
• 16.6. Bruker Corporation
• 16.7. Camtek Ltd.
• 16.8. Carl Zeiss SMT GmbH
• 16.9. Cognex Corporation
• 16.10. FANUC Corporation
• 16.11. FLIR Systems
• 16.12. Hexagon AB
• 16.13. Hitachi High-Tech Corporation
• 16.14. Keyence Corporation
• 16.15. KLA Corporation
• 16.16. National Instruments Corporation
• 16.17. Nikon Corporation
• 16.18. Omron Corporation
• 16.19. Onto Innovation Inc.
• 16.20. Rockwell Automation, Inc.
• 16.21. Schneider Electric SE
• 16.22. SICK AG
• 16.23. Siemens AG
• 16.24. Teledyne Technologies Incorporated
• 16.25. Thermo Fisher Scientific Inc.
• 16.26. Yokogawa Electric Corporation
• 16.27. Zebra Technologies Corporation
• 16.28. ZEISS Group