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市场调查报告书
商品编码
1698480

人工智慧品质检测市场：2025-2030 年预测

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个工作天内

价格
简介目录

预计2025年AI质检市场价值将达到231,586,000美元，到2030年将达到490,485,000美元，复合年增长率为16.19%。

AI 品质检测使用软体主导的人工智慧和视觉技术来帮助检测和处理半导体、药品、纺织品和汽车製造等产品中的不一致性。因此，由于其准确性和节省时间的能力，执行品质检查的人工智慧应用不仅在半导体行业变得普遍，而且在医疗保健、服饰製造、汽车製造等领域也变得普遍。

市场趋势

  • 製造业越来越多地使用基于人工智慧的品管软体：采用率的激增是由于产品品质不合格导致製造商的营运成本上升。例如，丰田因製造缺陷损失了13亿美元。有缺陷的部件经常被忽视并最终进入最终产品，从而增加营运成本并导致产品无法销售。
  • AI Vision：AI Vision 提供与基于规则的机器视觉系统类似的功能，增强品质检查，同时允许在人工监督下随着时间的推移进行迭代改进，从而提高其有效性。
  • 北美：北美引领全球人工智慧产业，正在积极投资扩大人工智慧软体的范围和功能，包括品管和检查中的应用。该地区领先的软体公司正在竞相推进人工智慧产品，以增强其产品和服务组合。

报告中介绍的主要企业包括英特尔公司、Kitov Systems、三丰美国公司、Landing AI、NEC 公司、罗伯特博世有限公司、Wenglor Deevio GmbH、Craftworks GmbH、Pleora Technologies Inc、IBM 公司、Qualitas Technologies、Lincode 和 Crayon AS。

本报告的主要优点

  • 深刻分析：获得涵盖主要地区和新兴地区的深入市场洞察，重点关注客户群、政府政策和社会经济因素、消费者偏好、垂直行业和其他子区隔。
  • 竞争格局：了解全球主要企业采取的策略策略，并了解正确策略的市场渗透潜力。
  • 市场趋势和驱动因素：探索动态因素和关键市场趋势以及它们将如何影响市场的未来发展。
  • 可行的建议：利用洞察力进行策略决策，在动态环境中开闢新的业务流和收益。
  • 受众广泛：对于新兴企业、研究机构、顾问公司、中小企业和大型企业都有益且具有成本效益。

它有什么用途？

产业和市场考量、商业机会评估、产品需求预测、打入市场策略、地理扩张、资本支出决策、法律规范与影响、新产品开发、竞争影响

研究范围

  • 2022 年至 2024 年的历史数据和 2025 年至 2030 年的预测数据
  • 成长机会、挑战、供应链前景、法律规范与趋势分析
  • 竞争定位、策略和市场占有率分析
  • 各细分市场和地区（包括国家）的收益成长和预测分析
  • 公司概况（策略、产品、财务资讯、主要趋势等）

目录

第一章执行摘要

第二章市场概述

  • 市场概览
  • 市场定义
  • 研究范围
  • 市场区隔

第三章 商业景气

  • 市场驱动因素
  • 市场限制
  • 市场机会
  • 波特五力分析
  • 产业价值链分析
  • 政策法规
  • 策略建议

第四章 技术展望

第五章 AI 质检市场类型

  • 介绍
  • 预先训练
  • 深度学习

第六章 AI 品质检测市场（按部署）

  • 介绍
  • 本地
  • 云端基础
  • 杂交种

第七章 AI 品质检测市场（按组件）

  • 介绍
  • 硬体
  • 软体
  • 服务

第八章 人工智慧品质检测市场（按最终用户）

  • 介绍
  • 半导体
  • 製药
  • 车
  • 纤维
  • 其他的

第九章 AI 质检市场（按地区）

  • 介绍
  • 北美洲
    • 按类型
    • 按部署
    • 按组件
    • 按最终用户
    • 按国家
      • 美国
      • 加拿大
      • 墨西哥
  • 南美洲
    • 按类型
    • 按部署
    • 按组件
    • 按最终用户
    • 按国家
      • 巴西
      • 阿根廷
      • 其他的
  • 欧洲
    • 按类型
    • 按部署
    • 按组件
    • 按最终用户
    • 按国家
      • 英国
      • 德国
      • 法国
      • 义大利
      • 西班牙
      • 其他的
  • 中东和非洲
    • 按类型
    • 按部署
    • 按组件
    • 按最终用户
    • 按国家
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 其他的
  • 亚太地区
    • 按类型
    • 按部署
    • 按组件
    • 按最终用户
    • 按国家
      • 中国
      • 日本
      • 印度
      • 韩国
      • 澳洲
      • 新加坡
      • 印尼
      • 其他的

第十章竞争格局及分析

  • 主要企业和策略分析
  • 市场占有率分析
  • 合併、收购、协议和合作
  • 竞争仪錶板

第十一章 公司简介

  • 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

第十二章 附录

  • 货币
  • 先决条件
  • 基准年和预测年时间表
  • 相关人员的主要利益
  • 调查方法
  • 简称
简介目录
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