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  3. コーピー 本社
  4. We will exhibit at the 3rd "Smart Factory EXPO Autumn": showcasing exterior inspection AI, worker analysis AI, and data generation management solutions.
SEMINAR_EVENT
  • Aug 29, 2024
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Aug 29, 2024

We will exhibit at the 3rd "Smart Factory EXPO Autumn": showcasing exterior inspection AI, worker analysis AI, and data generation management solutions.

コーピー コーピー 本社
We will be exhibiting at the "3rd Smart Factory EXPO Autumn - Manufacturing Innovation Exhibition through IoT/AI/FA," which will be held at Makuhari Messe from September 4 (Wednesday) to September 6 (Friday), 2024. Our exhibited products will include data management tools, appearance inspection AI, and worker analysis AI (certified system for IT subsidies 2024). We hope you will come visit us. Exhibited Products: - Appearance Inspection AI: Detects defects in manufactured products through AI learning. https://factory.confide.tech/visual-inspection/ - Worker Analysis AI: Visualizes and analyzes worker behavior. https://factory.confide.tech/workflow-analysis - Data Management: Generation of defective product data, centralized management of image data, verification and assurance of data quality. https://www.youtube.com/watch?v=Lvz2i7AHoDg About Smart Factory EXPO Autumn: This exhibition showcases the latest technologies and solutions such as IoT solutions, AI, and FA/robots that realize DX and efficiency in production and manufacturing.
Date and time Wednesday, Sep 04, 2024 ~ Friday, Sep 06, 2024
10:00 AM ~ 05:00 PM
Capital Makuhari Messe 〒261-8550 Chiba City, Mihama Ward, Nakase 2-1
Entry fee Free Pre-registration is required.
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【【品質管理者向け】AIによる外観検査とデータ管理ソリューション.jpg

AI-Based Visual Inspection and Data Management Solutions for Quality Managers

Attention: Improve work efficiency with the automation of image inspection! Easily and high-quality implement good product detection and defective product image generation with AI that provides understandable reasoning.

This is an AI solution designed for the manufacturing industry. It dramatically improves the automation of factory inspection tasks, enhances work efficiency, and increases the accuracy of quality control. It centralizes the generation and management of NG (non-good) data, streamlining the creation and management of AI training data. Additionally, it standardizes the skills required for inspection tasks, achieving skill-less operation. Key Features: - Intuitive operation: No need for AI expertise, easily operable by anyone. - Learning with a small amount of data: Efficient learning enables the quick construction of high-precision models. - Visualization of decision-making rationale: XAI technology clarifies the rationale behind AI decisions, enhancing reliability. - Generation and management of defective product data: Reduces the burden of data collection and optimizes quality control. Target Audience: - Production Management Department: Planning and managing production schedules, improving production efficiency, reducing defective products. - Producers: Optimizing production processes, introducing new technologies, maintaining equipment, utilizing automation technologies. - Quality Control Department: Conducting product quality inspections, detecting and addressing defective products, establishing quality assurance processes, confirming compliance with standards. - DX Promotion Department: Promoting digital transformation, implementing AI, improving operations through data analysis. - Companies that have already introduced image inspection machines and are facing issues with over-detection. *Please contact us for more information.

  • Visual Inspection Equipment
  • Image Processing Software
  • Image analysis software

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【製品資料】AI学習の生産性を劇的に向上!データ生成管理ツール.jpg

【Product Information】Dramatically improve the productivity of AI learning! Data generation/management tool

A must-see for those considering AI implementation! Streamline NG image generation and data management; preprocessing for machine learning has become surprisingly easy!

This is the ideal data management tool for manufacturers considering AI implementation. Preparing AI training data becomes easier, leading to reduced inspection time and lighter workloads. Challenges include: - Complex data management: Organizing image data is difficult, resulting in longer AI training times. - Insufficient NG data: There is a lack of defective product data, which hinders AI accuracy. - Difficult quality control: Data quality is inconsistent, causing variability in inspection results. Implementation is very simple! - Install the tool: Easy setup with cloud provision. - Upload data: Upload existing image data to the tool. - Generate NG samples: Automatically generate the necessary defective product data. - Train AI: Effectively train AI with high-quality data. Also perfect for those who have already implemented visual inspection! - Streamlined data management: Easily integrate and manage current visual inspection data. - Quality improvement: Support for evaluating and enhancing the quality of existing data. - Flexible response: Smooth integration with existing visual inspection AI. - AI validation support: Verify the performance of the AI model after implementation and assist with optimization. For more details, please refer to the 'Related Catalog' and contact us.

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【品質管理者向け】判断根拠を理解。少量から学習可能な外観検査AI.jpg

[For Quality Managers] Understanding the Basis for Judgment. Appearance Inspection AI that can learn from small amounts of data.

Understanding the basis and risks of AI's judgments. Capable of learning from a small amount of data. Intuitive operation without the need for AI knowledge. Achieving continuous quality management that does not end with a PoC.

The practical "visual inspection AI" for manufacturing sites allows for high-precision learning and operation from a small amount of data with intuitive operation. It is a user-friendly and effective AI tool for site managers. Target Industries: Manufacturing (semiconductors, foundry, automotive, processed foods) / Chemicals / Pharmaceuticals Target Audience: - Production Management Department: Planning and managing production, improving production efficiency, reducing defective products - Production Technology Department: Optimizing production processes, introducing new technologies, maintaining equipment, utilizing automation technologies - Quality Control Department: Inspecting product quality, detecting and addressing defects, establishing quality assurance processes, confirming compliance with standards - DX Promotion Department: Promoting digital transformation, implementing AI, improving operations through data analysis - Companies that have already introduced image inspection machines and face challenges with over-detection Main Features: - Intuitive operation: No AI knowledge required. Easy from AI model creation to operation and quality management. - Learning from a small amount of data: Domain-specific data augmentation technology generates training data tailored to the on-site environment. - Quality management through visualization: XAI technology visualizes the reasoning behind AI decisions, making model improvement easier. - Quality verification for AI: QAAI enables quality verification of data and models, visualization, and management of change points.

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FAQ.jpg

[Free Offer] Comprehensive Coverage of Basic Knowledge with FAQ and Glossary on Appearance Inspection AI

【FAQ/Glossary】You can understand the overview of the FAQ and terminology related to the introduction of AI for difficult-to-understand visual inspections.

We will organize and provide FAQs and a glossary in an easy-to-understand manner for those considering the introduction of an appearance inspection AI system. This content is essential for companies that have already implemented appearance inspection and those that are considering doing so in the future. The appearance inspection AI system is a powerful tool designed to dramatically improve quality control in manufacturing. Our AI technology utilizes advanced image analysis and machine learning algorithms to detect defective products quickly and accurately. It achieves a highly transparent decision-making process through Explainable AI (XAI) and reliable quality assurance through Quality Assurance AI (QAAI). Furthermore, it can build high-precision models from a small amount of data using data augmentation techniques. It has the flexibility to learn from images taken with various cameras and can adapt to different imaging conditions. It maintains consistent detection accuracy even with images captured in different environments, such as those using rule-based image inspection, significantly improving factory production efficiency.

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【製造業向け】ワンストップソリューション(XAIQAAI).jpg

For the manufacturing industry: One-stop AI solution (XAI/QAAI)

Easy operation even for AI beginners! A next-generation AI platform that realizes factory automation and quality control.

This is an AI platform designed for the manufacturing industry. By using this tool, you can achieve factory automation and labor reduction, while also easily managing quality control. It supports tasks such as visual inspections and analysis of worker behavior with AI, assisting operations on the shop floor. Why it’s good: It offers intuitive operation that can be used without specialized AI knowledge. It can learn from a small amount of data, making implementation easy. Additionally, it incorporates the following advanced technologies: - XAI (Explainable AI): Clarifies the reasoning behind AI decisions, allowing users to understand AI behavior. - QAAI (Quality Assurance AI): Generates data and verifies quality while considering environmental changes and disturbances, providing a stable AI model. Why explainability and robust AI are necessary: In manufacturing environments, it is crucial to understand how AI makes decisions. XAI enhances reliability by making the reasoning behind AI decisions explicit, allowing for safe operation. Furthermore, QAAI provides a robust model capable of responding to disturbances and environmental changes, ensuring stable performance on the shop floor. *For more details, please refer to the "Related Catalog" and contact us.*

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【その他資料】無料:製造業技術者の暗黙知・属人化を解消する方法 (2).jpg

[Other Materials] Free: Methods to Eliminate Tacit Knowledge and Personalization in Manufacturing Engineers

DX transformation with operator analysis AI: Visualizing work processes and converting "tacit knowledge" into "explicit knowledge." We will streamline and optimize the manufacturing process!

We will clarify the tasks in manufacturing and logistics that are prone to errors and time-consuming, and achieve appropriate personnel placement, training, workload verification, and improvement of work mistakes. To this end, we will promote the digital transformation (DX) of operations, which can enhance productivity and quality. To address issues such as labor shortages and the transmission of skills, we will utilize the "Worker Analysis AI" to smoothly implement the SECI model (Socialization, Externalization, Combination, Internalization), promoting personnel training, efficient work, and knowledge sharing. The "Worker Analysis AI" strengthens corporate competitiveness and supports sustainable growth. **The Necessity of Converting Tacit Knowledge into Explicit Knowledge** In various operations, the experience of skilled workers tends to accumulate as tacit knowledge within individuals. Converting tacit knowledge into explicit knowledge offers the following benefits: - Standardization: Ensures consistency in work procedures and quality. - Efficient Training: Enables rapid training of new employees. - Knowledge Sharing: Shares knowledge across the organization, improving performance. - Risk Mitigation: Reduces risks associated with worker transfers and resignations. - Continuous Improvement: Promotes process improvements, optimizing efficiency and costs. *For more details, please refer to the "Related Catalog" and "Other Materials" and contact us.*

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【【現場管理者向け】映像から作業工程を分析・可視化:作業者解析AI.jpg

[For Site Managers] Analyze and Visualize Work Processes from Video: Worker Analysis AI

Significant efficiency improvements in manual processes with the help of AI! Analyzing work processes from videos of the manufacturing site.

What is Operator Analysis? Operator Analysis AI is an innovative tool that visualizes work on the manufacturing line in real-time and recorded footage, enabling efficient production processes. It can dramatically improve productivity and quality in manufacturing settings through accurate understanding of cycle times, optimization of process flows, improvement of work actions, optimization of personnel allocation, detection of work omissions, and estimation of work loads. Benefits of Work Analysis 1. Remarkable Efficiency - Eliminates waste through detailed analysis of work processes, significantly improving work efficiency. 2. Assurance of Top Quality - Reduces work omissions and errors through consistent procedures, dramatically enhancing product quality. 3. Improved Safety - Monitors work actions in real-time, allowing for early detection and correction of risky behaviors, thereby providing a safe working environment. 4. Cost Reduction - Reduces operational costs by eliminating unnecessary work and duplication, optimizing personnel and resources. Reasons to Choose Our Products We offer AI solutions specialized for the manufacturing industry, with extensive know-how and flexible customization options. We ensure transparency with XAI and QAAI technologies, achieving intuitive operability and rapid implementation. Our pre-validation minimizes risks and guarantees high implementation effectiveness.

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【製造業向け】動画をAI解析で現場作業を比較・分析 – ボトルネックを特定し、IE手法で作業効率を劇的に改善.jpg

Elimination of bottlenecks in the manufacturing line: Improving work efficiency with worker analysis AI.

[For the manufacturing industry] Analyze and compare on-site work using AI video analysis – Identify bottlenecks and dramatically improve work efficiency with IE methods.

The "Operator Analysis AI" visualizes routine work on the manufacturing line using AI, enabling the understanding of cycle times, optimization of process flows, improvement of work actions, optimization of personnel allocation, detection of work omissions, and estimation of work loads. 【Optimal Operations】 - Comparison among operators: Improve consistency and efficiency of work using the ECRS (Eliminate, Combine, Rearrange, Simplify) method. - Creation of manuals: Promote the 3S (Sort, Set in order, Shine) principles. - Continuous operation: Review and optimize the 3M (Muri, Muda, Mura). 【Target Audience】 - Production Technology Department: For those aiming for process improvement and efficiency, promoting TQC (Total Quality Control) methods. - Production Management: For those wanting to accurately grasp cycle times and optimize production flows, supporting KY (Kiken Yochi) activities. - Quality Control: For those aiming to improve quality through the detection of work omissions and estimation of work loads. - DX Promotion Department: For those promoting digital transformation and aiming for work efficiency through IE (Industrial Engineering) methods. - Shipping Management: For those looking to enhance the efficiency and accuracy of the shipping process. You can view actual implementation examples and demos. *For details, please refer to the "Separate Catalog" and contact us.

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イプロス画像(製品・サービス).jpg

[Case Study] Work Analysis of Yokogawa Manufacturing's Production Site Using AI

[Free Case Study Available] Discover efficiency improvement points in the assembly processes of the world's six major manufacturers using AI! Results of the comparison between veterans and newcomers included!

Facing significant challenges in the manufacturing industry, Yokogawa Manufacturing Corporation has implemented Copy's work analysis AI, achieving remarkable results. This case study introduces the detailed processes and specific outcomes. 【Challenges Addressed】 1. Personalization of tasks 2. Issues in human resource development and technology transfer 3. Workload of operators 4. Variability in quality 【Some of the Effects of Implementation】 1. Early detection of process anomalies: Using Copy's AI technology, outliers in process cycle times were identified, uncovering process anomalies such as missed preparations for necessary parts. 2. Realization of efficiency: Waiting times for assembly equipment were identified, and efficient cart operations by veteran workers were discovered. The work efficiency of veterans and newcomers was compared, clarifying each group's strengths and areas for improvement. 3. Comparative analysis of workers: The movements of newcomers and veterans were analyzed over approximately 200 cycles, clarifying differences in work efficiency and areas for improvement. Through these initiatives, reductions in work time, improvements in quality consistency, and contributions to human resource development have been achieved, establishing new standards for productivity improvement and quality management across the manufacturing industry. For detailed case studies, specific data, and analysis results, please download and view the "Related Catalog."

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朝日電装導入事例.png

[Case Study] Digitizing Asahi Electric's Manufacturing Site with AI

[Free Case Study Available] Achieving Efficiency and Quality Improvement in Production Sites with AI! Publicizing Asahi Densou's Success Case.

Asahi Denso Co., Ltd. has introduced Confide for Factory to address important challenges faced by the manufacturing industry, achieving remarkable results. This case study presents the detailed process and specific outcomes. 【Challenges Addressed】 - Complexity of paper-based document management - Decrease in data accuracy - Difficulty in grasping real-time progress and quality data - Improvement of operational efficiency 【Some Effects of the Implementation】 - Improved operational efficiency: Reduced data entry effort, resulting in smoother and more accurate work. - Visualization and sharing of information: Real-time information sharing enables effective progress management. - Reduction in working hours: A reduction of work hours equivalent to six people per day. By moving away from paper-based document management and embracing digitalization, transparency in business processes has improved. Employee productivity has significantly increased, and errors and mistakes have decreased. Additionally, communication between departments has become smoother, strengthening the overall cooperation of the team. Confide for Factory is evaluated as an essential tool that supports digital transformation in the manufacturing industry and enhances the overall competitiveness of companies.

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Catalog: Confide for Factory: Visual Inspection AI

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[White Paper] Appearance Inspection AI: FAQ and Glossary "Fundamental Knowledge and Practical Information on Appearance Inspection Using AI Technology!"

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Catalog: Confide for Factory: Worker Analysis AI (Workflow Analysis)

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[Other Materials] Utilization of the SECI Model Using Worker Analysis AI

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[Catalog] AI for Manufacturing: XAI/QAAI-Based Platform 'CONFIDE for Factory'

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[Case Study] "There's no going back!" Praised by the on-site personnel. The reason Asahi Densou implemented an electronic document system using AI.

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[Flyer] Last chance this fiscal year! Achieve DX promotion and AI implementation in the manufacturing and logistics industries with the IT introduction subsidy 2024!

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news

Airflow photography - Camera specialized for fine particle visualization

Maintenance Resilience 2026 We will exhibit a fine particle visualization system at the 52nd Plant Maintenance Show. (July 15 (Wed) - July 17 (Fri), 2026 / Tokyo Big Sight, East Hall 1, 1-H12)

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The particle visualization technology developed under our brand "ViEST" has a very high level of detection sensitivity, capable of visualizing the suspended or adhered states of micro and nano-sized particles in real time. We are engaged in the sale of visualization systems and contract services for evaluation (investigating particles and airflow in production processes, inside and outside manufacturing equipment, evaluating the performance of cleaning products, proposing yield improvement measures, etc.) both domestically and internationally. In manufacturing environments, invisible fine foreign substances, dust generation, particle adhesion due to static electricity, and airflow disturbances can cause quality defects and reduced yield. Our company clarifies these contamination factors through visualization technology and provides consistent support from identifying the source of the problem to considering and confirming improvement measures. Do you have any of the following concerns? - Unable to identify the cause of foreign matter contamination - Want to check airflow and cleanliness in clean rooms - Want to understand dust generation from manufacturing equipment and transport systems - Want to reduce particle adhesion to films and substrates - Want to improve contamination that leads to reduced yield

Jul 04, 2026

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7/14 Seminar on the Basics and Features of CADSUPERWorks II - The Appeal of Linking 2D Drawings and 3D Models

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We will introduce the modeling features that utilize drawings through "2D×3D integration," which allows for easy creation of 3D models from 2D drawings, as well as an efficient integration function that projects 3D assembly data onto assembly and part drawings in bulk, automatically generating 2D drawings. Additionally, you will be able to see through a live demo how seamlessly utilizing 2D and 3D can lead to improved design efficiency, reduced errors, and increased overall productivity in business operations. This content is recommended for those considering the introduction of CAD or feeling challenges in their current design work. We hope you will join us.

Jul 02, 2026

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[Seminar] The Latest Developments in Solar Cell Related Policies and the Panel Disposal Business

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[Key Lecture Content] 1. Outlook on Renewable Energy Policy Centered on Solar Cells Ministry of Economy, Trade and Industry Agency for Natural Resources and Energy Energy Conservation and New Energy Department New Energy Division, Deputy Director Akihiro Kanayama 1. Trends in Renewable Energy Policy 2. Towards the Introduction of Perovskite Solar Cells 3. Regarding the "Law on Promoting the Recycling of Solar Cell Waste, etc." Established in May of This Year 4. Q&A / Business Card Exchange 2. Potential of the Solar Panel Disposal Business from the Perspective of Solar Power Generation Equipment ~Verifying What Can Be Done Now at a Practical Level~ Environmental Energy Recycling Center, Executive Director Yosuke Anada 1. Market Trends of Solar Power Generation Equipment and Movements Regarding Disposal 2. What is Happening Now with Solar Power Generation Equipment? How Does Disposal Occur? 3. Businesses Emerging and Activating Due to Mandatory Recycling 4. What Should Be Considered and Done Now 5. Q&A / Business Card Exchange

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[Seminar] Revised Equal Pay for Equal Work and Practical Responses for Non-Regular Employees

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[Instructor] Koichi Sato, Representative Partner / Certified Social Insurance and Labor Consultant, HR Plus Social Insurance and Labor Consultant Corporation [Key Lecture Content] Recently, approximately 40% of the labor force consists of non-regular employees, and the declining birthrate is progressing due to insufficient income. In response, the government has been making successive legal revisions, including the indefinite conversion rule and the so-called "equal pay for equal work." Additionally, against the backdrop of a recent labor shortage, there has been an increase in cases of employing contractors and freelancers, which has raised the risk of disguised contracting due to inappropriate practices. This course will touch on the contents of the "Equal Pay for Equal Work Guidelines," which are scheduled to be revised in October this year, and will also organize the latest "current situation" for each employment category of non-regular employees.

Jul 02, 2026

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<Free Webinar> Is Your Mold Management Okay? Corrective Action Measures Webinar

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  • SEMINAR_EVENT

In recent years, have you noticed an increase in corrective recommendations regarding "free storage of molds"? Cases where molds are stored for long periods without orders or where usage records cannot be confirmed are increasingly viewed as problematic from the perspective of the Subcontract Act (Act on the Proper Transaction of Small and Medium-Sized Enterprises). In fact, many companies may feel uneasy about management situations such as "not knowing where they are" or "not knowing when they were last used." Therefore, we will hold a webinar to explain practical methods for managing loaned molds as a response to these corrective recommendations. ✅ Recommended for: - Those who feel uneasy about the management status of loaned molds - Those who want to strengthen operations in preparation for legal compliance (Subcontract Act) - Those who have challenges in establishing mold management rules with business partners - Those who want to visualize the location, usage history, and evidence management of molds In this webinar, we will not only introduce management methods but also clearly explain how to advance mold management at a practical operational level. Now is the perfect opportunity to prepare for risks while you still think "it's okay." If you have any interest, please join us.

Jul 02, 2026

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