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市場調查報告書
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2106414

人工智慧驅動的品管系統市場預測至2034年：按組件、檢測類型、技術、應用、最終用戶和地區分類的全球分析

AI-Based Quality Control Systems Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software and Services), Inspection Type, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球基於人工智慧的品管系統市場預計將在 2026 年達到 28 億美元，並在預測期內以 17.0% 的複合年成長率成長，到 2034 年達到 72 億美元。

基於人工智慧的品管系統是一種智慧化的檢測和監控解決方案，它利用機器學習、電腦視覺和深度學習等人工智慧技術來評估製造過程中的產品品質。這些系統分析視覺數據、尺寸數據以及感測器產生的數據，以識別缺陷、不一致之處以及與預定義標準的偏差。這實現了自動化、準確且一致的品質評估，並支援整個生產環境中的即時決策、流程最佳化、可追溯性和持續改進。

對「零缺陷」製造的需求

隨著全球製造商在嚴格的監管要求、品牌聲譽保護以及產品召回和保固索賠成本飆升（尤其是在安全至關重要的領域）的驅動下，追求零缺陷生產的目標，對基於人工智慧的品管系統的需求正在穩步成長。在汽車產業，關鍵安全部件（例如煞車系統、安全氣囊模組和結構部件）需要亞毫米級缺陷檢測，因為這些部件的故障可能造成致命後果。電子和半導體製造商在晶圓製造、晶片封裝和印刷基板組裝等領域也需要奈米級檢測能力，這超越了人類視覺和傳統機器視覺的極限。

模型訓練的複雜性

基於人工智慧的品管系統市場面臨著許多阻礙，例如開發、訓練和檢驗能夠可靠檢測各種產品變體和製造條件下所有潛在缺陷的機器學習模型需要耗費大量時間和專業知識，而這些模型本身就十分複雜。每種產品類型和生產環境都需要大量的標註訓練資料集，而製造業往往缺乏此類資料集。這意味著在人工智慧模型達到可接受的效能水準之前，必須進行成本高昂的資料收集和專家標註服務。製造過程的動態特性，包括材料差異、光照變化和設備磨損等，都會隨著時間的推移降低模型的準確性。因此，持續的重新訓練和檢驗必不可少，但許多企業缺乏有效管理這些工作的技術能力。

透過代際人工智慧增強功能

將生成式人工智慧整合到品管系統中，正在創造變革性的機遇，例如產生合成缺陷、自動化模型最佳化和智慧檢測規劃。這顯著降低了採用基於人工智慧的品管系統的門檻，並提高了偵測能力。生成對抗網路（GAN）可以生成逼真的合成缺陷影像，彌補真實世界訓練資料集的不足。這使得人工智慧模型無需產生大量缺陷樣本進行訓練，即可學習罕見的缺陷模式。

易受敵對攻擊

基於人工智慧的品管系統市場正面臨一種新的威脅：對抗性攻擊利用深度學習模型中的漏洞，將缺陷產品錯誤​​地分類為合格產品，或將合格產品錯誤地分類為缺陷產品。這使得惡意攻擊者能夠損害產品品質或擾亂生產營運。精通人工智慧模型架構的攻擊者可以對產品表面、光照條件或攝影機輸入進行細微的改動，從而導致神經網路以可預測的方式發生故障，而這些故障在人類觀察者看來卻一切正常。

新型冠狀病毒（COVID-19）的影響：

新冠疫情初期，由於製造工廠暫時關閉、需求不確定性導致資本預算凍結以及供應鏈受限造成感測器和計算組件交付延遲，人工智慧品管系統的普及應用受到阻礙。然而，這場危機迅速凸顯了自動化檢測作為一種韌性策略的重要性，它能夠在勞動力短缺限制人工檢測團隊運作、旅行限制和保持社交距離等因素的影響下，維持品質標準的一致性。疫情後，重組供應鏈和製造業回流的努力刺激了對國內製造業產能的投資。這需要一套完善的品質保證體系，以滿足國內監管標準和客戶期望。

在預測期內，硬體領域預計將佔據最大的市場佔有率。

預計在預測期內，硬體領域將佔據最大的市場佔有率，因為高解析度工業相機、專用照明陣列、配備GPU的邊緣運算設備和整合感測器的硬體為基於人工智慧的品管部署提供了必要的實體基礎設施。硬體組件是人工智慧檢測系統部署中最大的資本投資項目，其中先進的CMOS和CCD成像感測器能夠以超越消費產品的解析度和影格速率捕捉詳細資訊。

預計在預測期內，缺陷檢測細分市場將呈現最高的複合年成長率。

在預測期內，缺陷檢測領域預計將呈現最高的成長率，這主要得益於汽車、電子、製藥和消費品製造業日益成長的品質要求。在這些行業中，未被檢測到的缺陷會直接影響產品安全、合規性和品牌聲譽。人工智慧驅動的缺陷偵測系統利用基於龐大缺陷庫訓練的深度學習模型，辨識出在高速生產過程中常被人工檢驗員忽略的刮痕、裂縫、污染、尺寸偏差和組裝錯誤。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其先進的製造業基礎設施、人工智慧技術的早期應用，以及領先的人工智慧品管技術供應商在美國和加拿大的強大影響力。美國擁有眾多領先的工業自動化和機器視覺公司，例如康耐視（Cognex）、泰萊科技（Teledyne）和Keyence），這些公司正在推動創新，並為基於人工智慧的檢測平台製定市場標準。北美的汽車、航太和製藥製造商堅持嚴格的品質標準，需要先進的自動化檢測系統來確保符合監管要求並避免法律責任。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、日本、韓國和台灣地區大規模的製造業規模、政府主導的產業現代化計劃以及不斷擴大的電子和汽車生產。中國的「中國製造2025」舉措和半導體自給自足政策正推動對國內製造工廠先進品管系統進行前所未有的投資。日本和韓國擁有世界領先的電子和汽車製造業，人工智慧驅動的檢測對於確保其在全球市場的品質競爭力至關重要。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶要求，我們可以提供主要國家的市場估算和預測，以及複合年成長率（註：需經可行性確認）。
  • 競爭性標竿分析
  • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章：執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧驅動的品管系統市場：按組件分類

  • 硬體
  • 軟體
  • 服務

第6章 全球人工智慧驅動的品管系統市場：按檢測類型分類

  • 表面檢查
  • 尺寸檢驗
  • 缺陷檢測
  • 產品分類
  • 組裝檢驗
  • 標籤檢查
  • 包裝檢驗

第7章 全球人工智慧驅動的品管系統市場：按技術分類

  • 電腦視覺
  • 深度學習
  • 機器學習
  • 神經網路
  • 邊緣人工智慧
  • 雲端人工智慧

第8章：全球人工智慧驅動的品管系統市場：按應用領域分類

  • 生產製造中的品質保證
  • 線上測試
  • 最終檢驗
  • 流程監控
  • 預測品質分析
  • 合規性檢查
  • 產品可追溯性

第9章 全球人工智慧驅動的品管系統市場：按最終用戶分類

  • 製造商
  • OEMs
  • 契約製造
  • 系統整合商
  • 品質檢驗服務供應商

第10章：全球人工智慧驅動的品管系統市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • Cognex Corporation
  • Keyence Corporation
  • Omron Corporation
  • Teledyne Technologies Incorporated
  • Basler AG
  • MVTec Software GmbH
  • ISRA VISION AG
  • SICK AG
  • ABB Ltd.
  • Siemens AG
  • Rockwell Automation, Inc.
  • Hikrobot Co., Ltd.
  • Baumer Holding AG
  • Advantech Co., Ltd.
  • Zebra Technologies Corporation
  • Balluff GmbH
  • NVIDIA Corporation
Product Code: SMRC38544

According to Stratistics MRC, the Global AI-Based Quality Control Systems Market is accounted for $2.8 billion in 2026 and is expected to reach $7.2 billion by 2034 growing at a CAGR of 17.0% during the forecast period. AI-Based Quality Control Systems are intelligent inspection and monitoring solutions that use artificial intelligence technologies, including machine learning, computer vision, and deep learning, to evaluate the quality of products during manufacturing processes. These systems analyze visual, dimensional, and sensor-generated data to identify defects, inconsistencies, and deviations from predefined standards. They enable automated, accurate, and consistent quality assessment while supporting real-time decision-making, process optimization, traceability, and continuous improvement across production environments.

Market Dynamics:

Driver:

Zero-defect manufacturing demand

AI-based quality control systems are experiencing robust demand growth as global manufacturing industries pursue zero-defect production targets driven by stringent regulatory requirements, brand reputation protection, and escalating costs associated with product recalls and warranty claims in safety-critical sectors. The automotive industry requires sub-millimeter defect detection for critical safety components including brake systems, airbag modules, and structural elements where failures can result in catastrophic consequences. Electronics and semiconductor manufacturers demand nanometer-scale inspection capabilities for wafer fabrication, chip packaging, and printed circuit board assembly that exceed human visual acuity and traditional machine vision limitations.

Restraint:

Model training complexity

The AI-based quality control systems market faces significant adoption barriers from the complexity, time, and expertise required to develop, train, and validate machine learning models capable of reliably detecting the full spectrum of potential defects across diverse product variations and manufacturing conditions. Each product type and production environment requires extensive labeled training datasets that manufacturing organizations frequently lack, necessitating costly data collection campaigns and expert annotation services before AI models can achieve acceptable performance levels. The dynamic nature of manufacturing processes, including material variations, lighting changes, and equipment wear, can degrade model accuracy over time, requiring continuous retraining and validation that many organizations lack the technical capacity to manage effectively.

Opportunity:

Generative AI augmentation

The integration of generative artificial intelligence with quality control systems is creating transformative opportunities for synthetic defect generation, automated model optimization, and intelligent inspection planning that substantially reduce the barriers to AI-based quality system deployment and improve detection performance. Generative adversarial networks can create realistic synthetic defect images that augment limited real-world training datasets, enabling AI models to learn rare defect patterns without requiring extensive production of defective samples for training purposes.

Threat:

Adversarial attack vulnerability

The AI-based quality control systems market faces emerging threats from adversarial attacks that exploit vulnerabilities in deep learning models to cause misclassification of defective products as acceptable or acceptable products as defective, potentially enabling malicious actors to compromise manufacturing quality or disrupt production operations. Sophisticated adversaries with knowledge of AI model architectures can craft subtle perturbations to product surfaces, lighting conditions, or camera inputs that cause neural networks to fail in predictable ways while appearing normal to human observers.

Covid-19 Impact:

The COVID-19 pandemic initially disrupted AI-based quality control system deployments as manufacturing facilities faced temporary closures, capital budgets were frozen amid demand uncertainty, and supply chain constraints delayed sensor and computing component deliveries. However, the crisis accelerated recognition of automated inspection as a resilience strategy that maintains consistent quality standards despite workforce disruptions, travel restrictions, and social distancing requirements that limit manual inspection team availability. Post-pandemic, supply chain restructuring and reshoring initiatives are driving investment in domestic manufacturing capabilities that require advanced quality assurance infrastructure to meet domestic regulatory standards and customer expectations.

The hardware segment is expected to be the largest during the forecast period

The hardware segment is expected to account for the largest market share during the forecast period, due to the essential physical infrastructure that high-resolution industrial cameras, specialized lighting arrays, GPU-enabled edge computing devices, and sensor integration hardware provide as the foundation of AI-based quality control deployments. Hardware components represent the largest capital expenditure category for AI inspection system implementations, with advanced CMOS and CCD imaging sensors capable of capturing defect-relevant detail at resolutions and frame rates that exceed consumer-grade alternatives.

The defect detection segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the defect detection segment is predicted to witness the highest growth rate, driven by escalating quality requirements across automotive, electronics, pharmaceuticals, and consumer goods manufacturing, where undetected defects directly impact product safety, regulatory compliance, and brand reputation. AI-powered defect detection systems leverage deep learning models trained on extensive defect libraries to identify scratches, cracks, contamination, dimensional deviations, and assembly errors that human inspectors frequently miss during high-speed production operations.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to advanced manufacturing infrastructure, early adoption of artificial intelligence technologies, and substantial presence of leading AI quality control technology providers across the United States and Canada. The United States hosts major industrial automation and machine vision companies, including Cognex, Teledyne, and Keyence, that drive innovation and establish market standards for AI-based inspection platforms. North American automotive, aerospace, and pharmaceutical manufacturers maintain stringent quality standards that necessitate sophisticated automated inspection systems for regulatory compliance and liability protection.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to massive manufacturing scale, government-led industrial modernization programs, and expanding electronics and automotive production across China, Japan, South Korea, and Taiwan. China's Made in China 2025 initiative and semiconductor self-sufficiency policies are driving unprecedented investment in advanced quality control systems for domestic manufacturing facilities. Japan and South Korea maintain world-leading electronics and automotive manufacturing sectors that require AI inspection for competitive quality positioning in global markets.

Key players in the market

Some of the key players in AI-Based Quality Control Systems Market include Cognex Corporation, Keyence Corporation, Omron Corporation, Teledyne Technologies Incorporated, Basler AG, MVTec Software GmbH, ISRA VISION AG, SICK AG, ABB Ltd., Siemens AG, Rockwell Automation, Inc., Hikrobot Co., Ltd., Baumer Holding AG, Advantech Co., Ltd., Zebra Technologies Corporation, Balluff GmbH, and NVIDIA Corporation.

Key Developments:

In June 2026, Cognex Corporation launched a next-generation AI defect detection platform with synthetic data augmentation capabilities, enabling manufacturers to train high-accuracy inspection models with minimal real-world defective sample requirements.

In May 2026, Keyence Corporation introduced an AI-powered inline quality control system with real-time adaptive learning, allowing manufacturers to deploy defect detection without extensive pre-training datasets or specialized machine learning expertise.

In April 2026, NVIDIA Corporation expanded its Isaac robotics platform with generative AI modules for quality control applications, enabling autonomous generation of inspection scenarios and defect simulations for model validation and performance optimization.

Components Covered:

  • Hardware
  • Software
  • Services

Inspection Types Covered:

  • Surface Inspection
  • Dimensional Inspection
  • Defect Detection
  • Product Classification
  • Assembly Verification
  • Label Inspection
  • Packaging Inspection

Technologies Covered:

  • Computer Vision
  • Deep Learning
  • Machine Learning
  • Neural Networks
  • Edge AI
  • Cloud AI

Applications Covered:

  • Manufacturing Quality Assurance
  • In-Line Inspection
  • End-of-Line Inspection
  • Process Monitoring
  • Predictive Quality Analytics
  • Compliance Inspection
  • Product Traceability

End Users Covered:

  • Manufacturers
  • OEMs
  • Contract Manufacturers
  • System Integrators
  • Quality Inspection Service Providers

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI-Based Quality Control Systems Market, By Component

  • 5.1 Hardware
  • 5.2 Software
  • 5.3 Services

6 Global AI-Based Quality Control Systems Market, By Inspection Type

  • 6.1 Surface Inspection
  • 6.2 Dimensional Inspection
  • 6.3 Defect Detection
  • 6.4 Product Classification
  • 6.5 Assembly Verification
  • 6.6 Label Inspection
  • 6.7 Packaging Inspection

7 Global AI-Based Quality Control Systems Market, By Technology

  • 7.1 Computer Vision
  • 7.2 Deep Learning
  • 7.3 Machine Learning
  • 7.4 Neural Networks
  • 7.5 Edge AI
  • 7.6 Cloud AI

8 Global AI-Based Quality Control Systems Market, By Application

  • 8.1 Manufacturing Quality Assurance
  • 8.2 In-Line Inspection
  • 8.3 End-of-Line Inspection
  • 8.4 Process Monitoring
  • 8.5 Predictive Quality Analytics
  • 8.6 Compliance Inspection
  • 8.7 Product Traceability

9 Global AI-Based Quality Control Systems Market, By End User

  • 9.1 Manufacturers
  • 9.2 OEMs
  • 9.3 Contract Manufacturers
  • 9.4 System Integrators
  • 9.5 Quality Inspection Service Providers

10 Global AI-Based Quality Control Systems Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 Cognex Corporation
  • 13.2 Keyence Corporation
  • 13.3 Omron Corporation
  • 13.4 Teledyne Technologies Incorporated
  • 13.5 Basler AG
  • 13.6 MVTec Software GmbH
  • 13.7 ISRA VISION AG
  • 13.8 SICK AG
  • 13.9 ABB Ltd.
  • 13.10 Siemens AG
  • 13.11 Rockwell Automation, Inc.
  • 13.12 Hikrobot Co., Ltd.
  • 13.13 Baumer Holding AG
  • 13.14 Advantech Co., Ltd.
  • 13.15 Zebra Technologies Corporation
  • 13.16 Balluff GmbH
  • 13.17 NVIDIA Corporation

List of Tables

  • Table 1 Global AI-Based Quality Control Systems Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Based Quality Control Systems Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI-Based Quality Control Systems Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global AI-Based Quality Control Systems Market Outlook, By Software (2023-2034) ($MN)
  • Table 5 Global AI-Based Quality Control Systems Market Outlook, By Services (2023-2034) ($MN)
  • Table 6 Global AI-Based Quality Control Systems Market Outlook, By Inspection Type (2023-2034) ($MN)
  • Table 7 Global AI-Based Quality Control Systems Market Outlook, By Surface Inspection (2023-2034) ($MN)
  • Table 8 Global AI-Based Quality Control Systems Market Outlook, By Dimensional Inspection (2023-2034) ($MN)
  • Table 9 Global AI-Based Quality Control Systems Market Outlook, By Defect Detection (2023-2034) ($MN)
  • Table 10 Global AI-Based Quality Control Systems Market Outlook, By Product Classification (2023-2034) ($MN)
  • Table 11 Global AI-Based Quality Control Systems Market Outlook, By Assembly Verification (2023-2034) ($MN)
  • Table 12 Global AI-Based Quality Control Systems Market Outlook, By Label Inspection (2023-2034) ($MN)
  • Table 13 Global AI-Based Quality Control Systems Market Outlook, By Packaging Inspection (2023-2034) ($MN)
  • Table 14 Global AI-Based Quality Control Systems Market Outlook, By Technology (2023-2034) ($MN)
  • Table 15 Global AI-Based Quality Control Systems Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 16 Global AI-Based Quality Control Systems Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 17 Global AI-Based Quality Control Systems Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 18 Global AI-Based Quality Control Systems Market Outlook, By Neural Networks (2023-2034) ($MN)
  • Table 19 Global AI-Based Quality Control Systems Market Outlook, By Edge AI (2023-2034) ($MN)
  • Table 20 Global AI-Based Quality Control Systems Market Outlook, By Cloud AI (2023-2034) ($MN)
  • Table 21 Global AI-Based Quality Control Systems Market Outlook, By Application (2023-2034) ($MN)
  • Table 22 Global AI-Based Quality Control Systems Market Outlook, By Manufacturing Quality Assurance (2023-2034) ($MN)
  • Table 23 Global AI-Based Quality Control Systems Market Outlook, By In-Line Inspection (2023-2034) ($MN)
  • Table 24 Global AI-Based Quality Control Systems Market Outlook, By End-of-Line Inspection (2023-2034) ($MN)
  • Table 25 Global AI-Based Quality Control Systems Market Outlook, By Process Monitoring (2023-2034) ($MN)
  • Table 26 Global AI-Based Quality Control Systems Market Outlook, By Predictive Quality Analytics (2023-2034) ($MN)
  • Table 27 Global AI-Based Quality Control Systems Market Outlook, By Compliance Inspection (2023-2034) ($MN)
  • Table 28 Global AI-Based Quality Control Systems Market Outlook, By Product Traceability (2023-2034) ($MN)
  • Table 29 Global AI-Based Quality Control Systems Market Outlook, By End User (2023-2034) ($MN)
  • Table 30 Global AI-Based Quality Control Systems Market Outlook, By Manufacturers (2023-2034) ($MN)
  • Table 31 Global AI-Based Quality Control Systems Market Outlook, By OEMs (2023-2034) ($MN)
  • Table 32 Global AI-Based Quality Control Systems Market Outlook, By Contract Manufacturers (2023-2034) ($MN)
  • Table 33 Global AI-Based Quality Control Systems Market Outlook, By System Integrators (2023-2034) ($MN)
  • Table 34 Global AI-Based Quality Control Systems Market Outlook, By Quality Inspection Service Providers (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.