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市場調查報告書
商品編碼
1698480

人工智慧品質檢測市場：2025-2030 年預測

AI Quality Inspection Market - Forecasts from 2025 to 2030
出版日期: | 出版商: Knowledge Sourcing Intelligence | 英文 140 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

預計2025年AI質檢市場價值將達到231,586,000美元，到2030年將達到490,485,000美元，複合年成長率為16.19%。

AI 品質檢測使用軟體主導的人工智慧和視覺技術來幫助檢測和處理半導體、藥品、紡織品和汽車製造等產品中的不一致性。因此，由於其準確性和節省時間的能力，執行品質檢查的人工智慧應用不僅在半導體行業變得普遍，而且在醫療保健、服飾製造、汽車製造等領域也變得普遍。

市場趨勢

  • 製造業擴大使用基於人工智慧的品管軟體：採用率的激增是由於產品品質不合格導致製造商的營運成本上升。例如，豐田因製造缺陷損失了13億美元。有缺陷的部件經常被忽視並最終進入最終產品，從而增加營運成本並導致產品無法銷售。
  • AI Vision：AI Vision 提供與基於規則的機器視覺系統類似的功能，增強品質檢查，同時允許在人工監督下隨著時間的推移進行迭代改進，從而提高其有效性。
  • 北美：北美引領全球人工智慧產業，正在積極投資擴大人工智慧軟體的範圍和功能，包括品管和檢查中的應用。該地區領先的軟體公司正在競相推進人工智慧產品，以增強其產品和服務組合。

報告中介紹的主要企業包括英特爾公司、Kitov Systems、三豐美國公司、Landing AI、NEC 公司、羅伯特博世有限公司、Wenglor Deevio GmbH、Craftworks GmbH、Pleora Technologies Inc、IBM 公司、Qualitas Technologies、Lincode 和 Crayon AS。

本報告的主要優點

  • 深刻分析：獲得涵蓋主要地區和新興地區的深入市場洞察，重點關注客戶群、政府政策和社會經濟因素、消費者偏好、垂直行業和其他子區隔。
  • 競爭格局：了解全球主要企業採取的策略策略，並了解正確策略的市場滲透潛力。
  • 市場趨勢和促進因素：探索動態因素和關鍵市場趨勢以及它們將如何影響市場的未來發展。
  • 可行的建議：利用洞察力進行策略決策，在動態環境中開闢新的業務流和收益。
  • 受眾廣泛：對於新興企業、研究機構、顧問公司、中小企業和大型企業都有益且具有成本效益。

它有什麼用途？

產業和市場考量、商業機會評估、產品需求預測、打入市場策略、地理擴張、資本支出決策、法律規範與影響、新產品開發、競爭影響

研究範圍

  • 2022 年至 2024 年的歷史數據和 2025 年至 2030 年的預測數據
  • 成長機會、挑戰、供應鏈前景、法律規範與趨勢分析
  • 競爭定位、策略和市場佔有率分析
  • 各細分市場和地區（包括國家）的收益成長和預測分析
  • 公司概況（策略、產品、財務資訊、主要趨勢等）

目錄

第1章執行摘要

第2章市場概述

  • 市場概覽
  • 市場定義
  • 研究範圍
  • 市場區隔

第3章 商業景氣

  • 市場促進因素
  • 市場限制
  • 市場機會
  • 波特五力分析
  • 產業價值鏈分析
  • 政策法規
  • 策略建議

第4章 技術展望

第5章 AI 質檢市場類型

  • 介紹
  • 預先訓練
  • 深度學習

第6章 AI 品質檢測市場（按部署）

  • 介紹
  • 本地
  • 雲端基礎
  • 混合

第7章 AI 品質檢測市場（按組件）

  • 介紹
  • 硬體
  • 軟體
  • 服務

第8章 人工智慧品質檢測市場（按最終用戶）

  • 介紹
  • 半導體
  • 製藥
  • 車
  • 纖維
  • 其他

第9章 AI 質檢市場（按地區）

  • 介紹
  • 北美洲
    • 按類型
    • 按部署
    • 按組件
    • 按最終用戶
    • 按國家
      • 美國
      • 加拿大
      • 墨西哥
  • 南美洲
    • 按類型
    • 按部署
    • 按組件
    • 按最終用戶
    • 按國家
      • 巴西
      • 阿根廷
      • 其他
  • 歐洲
    • 按類型
    • 按部署
    • 按組件
    • 按最終用戶
    • 按國家
      • 英國
      • 德國
      • 法國
      • 義大利
      • 西班牙
      • 其他
  • 中東和非洲
    • 按類型
    • 按部署
    • 按組件
    • 按最終用戶
    • 按國家
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 其他
  • 亞太地區
    • 按類型
    • 按部署
    • 按組件
    • 按最終用戶
    • 按國家
      • 中國
      • 日本
      • 印度
      • 韓國
      • 澳洲
      • 新加坡
      • 印尼
      • 其他

第10章競爭格局及分析

  • 主要企業和策略分析
  • 市場佔有率分析
  • 合併、收購、協議和合作
  • 競爭儀錶板

第11章 公司簡介

  • Intel Corp.
  • Kitov Systems
  • Mitutoyo America Corporation
  • Landing AI
  • NEC Corporation
  • Robert Bosch GmbH
  • Wenglor Deevio GmbH
  • Craftworks GmbH
  • Pleora Technologies Inc.
  • IBM Corporation
  • Qualitas Technologies
  • Lincode
  • Crayon AS

第12章 附錄

  • 貨幣
  • 先決條件
  • 基準年和預測年時間表
  • 相關人員的主要利益
  • 調查方法
  • 簡稱
簡介目錄
Product Code: KSI061614653

The AI Quality Inspection Market, valued at US$490.485 million in 2030 from US$231.586 million in 2025, is projected to grow at a CAGR of 16.19%.

When using software-driven artificial intelligence and vision technologies, AI quality inspection helps detect and process inconsistencies in products, including semiconductors, pharmaceuticals, textiles, and automotive manufacturing. Hence, due to their precision and time-saving capabilities, AI-powered applications that make quality checks are becoming more common in the semiconductor industry, as well as in medicine, clothing production, car-making industries, and other sectors.

Market Trends:

  • Rising Use of AI-Based Quality Control Software in Manufacturing: The surge in adoption stems from escalating operating costs for manufacturers caused by substandard product quality. For example, Toyota faced a $1.3 billion loss due to production flaws. When defective parts go unnoticed, they are often incorporated into final products, inflating operational expenses and resulting in unsellable goods. This issue is especially common among firms mass-producing items in batches.
  • AI Vision: AI vision enhances quality inspections by offering capabilities akin to rules-based machine vision systems while allowing for iterative improvements over time with human oversight, boosting its effectiveness.
  • North America: As a leader in the global artificial intelligence landscape, North America is heavily investing in broadening the reach and functionality of AI software, including applications in quality control and inspection. Leading software firms in the region are focused on advancing their AI offerings, competing to strengthen their product and service portfolios.

Some of the major players covered in this report include Intel Corp, Kitov Systems, Mitutoyo America Corporation, Landing AI, NEC Corporation, Robert Bosch GmbH, Wenglor Deevio GmbH, Craftworks GmbH, Pleora Technologies Inc, IBM Corporation, Qualitas Technologies, Lincode, and Crayon AS, among others:

Key Benefits of this Report:

  • Insightful Analysis: Gain detailed market insights covering major as well as emerging geographical regions, focusing on customer segments, government policies and socio-economic factors, consumer preferences, industry verticals, and other sub-segments.
  • Competitive Landscape: Understand the strategic maneuvers employed by key players globally to understand possible market penetration with the correct strategy.
  • Market Drivers & Future Trends: Explore the dynamic factors and pivotal market trends and how they will shape future market developments.
  • Actionable Recommendations: Utilize the insights to exercise strategic decisions to uncover new business streams and revenues in a dynamic environment.
  • Caters to a Wide Audience: Beneficial and cost-effective for startups, research institutions, consultants, SMEs, and large enterprises.

What do businesses use our reports for?

Industry and Market Insights, Opportunity Assessment, Product Demand Forecasting, Market Entry Strategy, Geographical Expansion, Capital Investment Decisions, Regulatory Framework & Implications, New Product Development, Competitive Intelligence

Report Coverage:

  • Historical data from 2022 to 2024 & forecast data from 2025 to 2030
  • Growth Opportunities, Challenges, Supply Chain Outlook, Regulatory Framework, and Trend Analysis
  • Competitive Positioning, Strategies, and Market Share Analysis
  • Revenue Growth and Forecast Assessment of segments and regions including countries
  • Company Profiling (Strategies, Products, Financial Information, and Key Developments among others)

The AI Quality Inspection Market is analyzed into the following segments:

By Type

  • Pre-trained
  • Deep Learning

By Deployment

  • On-Premises
  • Cloud-Based
  • Hybrid

By Component

  • Hardware
  • Software
  • Services

By End-Users

  • Semiconductor
  • Pharmaceutical
  • Automotive
  • Textile
  • Others

By Region

  • North America
  • USA
  • Canada
  • Mexico
  • South America
  • Brazil
  • Argentina
  • Others
  • Europe
  • United Kingdom
  • Germany
  • France
  • Italy
  • Spain
  • Others
  • Middle East and Africa
  • Saudi Arabia
  • UAE
  • Others
  • Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Singapore
  • Indonesia
  • Others

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

  • 2.1. Market Overview
  • 2.2. Market Definition
  • 2.3. Scope of the Study
  • 2.4. Market Segmentation

3. BUSINESS LANDSCAPE

  • 3.1. Market Drivers
  • 3.2. Market Restraints
  • 3.3. Market Opportunities
  • 3.4. Porter's Five Forces Analysis
  • 3.5. Industry Value Chain Analysis
  • 3.6. Policies and Regulations
  • 3.7. Strategic Recommendations

4. TECHNOLOGICAL OUTLOOK

5. AI QUALITY INSPECTION MARKET BY TYPE

  • 5.1. Introduction
  • 5.2. Pre-trained
  • 5.3. Deep learning

6. AI QUALITY INSPECTION MARKET BY DEPLOYMENT

  • 6.1. Introduction
  • 6.2. On-Premises
  • 6.3. Cloud-Based
  • 6.4. Hybrid

7. AI QUALITY INSPECTION MARKET BY COMPONENT

  • 7.1. Introduction
  • 7.2. Hardware
  • 7.3. Software
  • 7.4. Services

8. AI QUALITY INSPECTION MARKET BY END-USERS

  • 8.1. Introduction
  • 8.2. Semiconductor
  • 8.3. Pharmaceutical
  • 8.4. Automotive
  • 8.5. Textile
  • 8.6. Others

9. AI QUALITY INSPECTION MARKET BY GEOGRAPHY

  • 9.1. Introduction
  • 9.2. North America
    • 9.2.1. By Type
    • 9.2.2. By Deployment
    • 9.2.3. By Component
    • 9.2.4. By End-Users
    • 9.2.5. By Country
      • 9.2.5.1. USA
      • 9.2.5.2. Canada
      • 9.2.5.3. Mexico
  • 9.3. South America
    • 9.3.1. By Type
    • 9.3.2. By Deployment
    • 9.3.3. By Component
    • 9.3.4. By End-Users
    • 9.3.5. By Country
      • 9.3.5.1. Brazil
      • 9.3.5.2. Argentina
      • 9.3.5.3. Others
  • 9.4. Europe
    • 9.4.1. By Type
    • 9.4.2. By Deployment
    • 9.4.3. By Component
    • 9.4.4. By End-Users
    • 9.4.5. By Country
      • 9.4.5.1. United Kingdom
      • 9.4.5.2. Germany
      • 9.4.5.3. France
      • 9.4.5.4. Italy
      • 9.4.5.5. Spain
      • 9.4.5.6. Others
  • 9.5. Middle East and Africa
    • 9.5.1. By Type
    • 9.5.2. By Deployment
    • 9.5.3. By Component
    • 9.5.4. By End-Users
    • 9.5.5. By Country
      • 9.5.5.1. Saudi Arabia
      • 9.5.5.2. UAE
      • 9.5.5.3. Others
  • 9.6. Asia Pacific
    • 9.6.1. By Type
    • 9.6.2. By Deployment
    • 9.6.3. By Component
    • 9.6.4. By End-Users
    • 9.6.5. By Country
      • 9.6.5.1. China
      • 9.6.5.2. Japan
      • 9.6.5.3. India
      • 9.6.5.4. South Korea
      • 9.6.5.5. Australia
      • 9.6.5.6. Singapore
      • 9.6.5.7. Indonesia
      • 9.6.5.8. Others

10. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 10.1. Major Players and Strategy Analysis
  • 10.2. Market Share Analysis
  • 10.3. Mergers, Acquisitions, Agreements, and Collaborations
  • 10.4. Competitive Dashboard

11. COMPANY PROFILES

  • 11.1. Intel Corp.
  • 11.2. Kitov Systems
  • 11.3. Mitutoyo America Corporation
  • 11.4. Landing AI
  • 11.5. NEC Corporation
  • 11.6. Robert Bosch GmbH
  • 11.7. Wenglor Deevio GmbH
  • 11.8. Craftworks GmbH
  • 11.9. Pleora Technologies Inc.
  • 11.10. IBM Corporation
  • 11.11. Qualitas Technologies
  • 11.12. Lincode
  • 11.13. Crayon AS

12. APPENDIX

  • 12.1. Currency
  • 12.2. Assumptions
  • 12.3. Base and Forecast Years Timeline
  • 12.4. Key benefits for the stakeholders
  • 12.5. Research Methodology
  • 12.6. Abbreviations