Machine Vision Solution

Machine Vision Solution for Industrial Manufacturing

The company aimed to standardize its software solutions across all business units, ensuring scalability, efficiency, and uniformity.

Manufacturing

IoT

SAP

Стартова

Наші клієнти

Machine Vision Solution

Стартова

Наші клієнти

Machine Vision Solution

Client overview

Our client is a global leader in industrial machine vision, specializing in automated quality inspection systems for over 35 years. Their advanced camera-based vision technology ensures high-precision surface quality control across multiple industries including automotive, glass manufacturing, and battery production.

With a strong presence in Germany, the company aimed to standardize its software solutions across all business units, ensuring scalability, efficiency, and uniformity. They sought to develop a centralized web-based machine vision system that could process high-resolution imaging data and integrate seamlessly with their proprietary hardware while meeting strict industrial requirements.

Challenges

The client faced several critical technical and organizational challenges:

Fragmented Software Architecture

• Siloed applications developed independently by different business units

• Inconsistent data models and processing algorithms across applications

• Non-standardized UIs creating training difficulties for operators

• Redundant code maintenance across multiple codebases

High-Performance Data Processing Bottlenecks

• Processing of 4K image streams at 120fps requiring optimized algorithms

• Real-time defect detection requiring immediate data processing

• Memory management issues with large image datasets

• Excessive CPU/GPU resource utilization in existing implementations

Technical Debt and Legacy Systems

• Outdated hardware interfaces with proprietary communication protocols

• Memory leaks in existing C++ applications leading to system instability

• Monolithic architecture preventing effective scaling

• Limited componentization creating maintenance challenges

Fragmented Software Architecture

• Siloed applications developed independently by different business units

• Inconsistent data models and processing algorithms across applications

• Non-standardized UIs creating training difficulties for operators

• Redundant code maintenance across multiple codebases

High-Performance Data Processing Bottlenecks

• Processing of 4K image streams at 120fps requiring optimized algorithms

• Real-time defect detection requiring immediate data processing

• Memory management issues with large image datasets

• Excessive CPU/GPU resource utilization in existing implementations

Technical Debt and Legacy Systems

• Outdated hardware interfaces with proprietary communication protocols

• Memory leaks in existing C++ applications leading to system instability

• Monolithic architecture preventing effective scaling

• Limited componentization creating maintenance challenges

Fragmented Software Architecture

• Siloed applications developed independently by different business units

• Inconsistent data models and processing algorithms across applications

• Non-standardized UIs creating training difficulties for operators

• Redundant code maintenance across multiple codebases

High-Performance Data Processing Bottlenecks

• Processing of 4K image streams at 120fps requiring optimized algorithms

• Real-time defect detection requiring immediate data processing

• Memory management issues with large image datasets

• Excessive CPU/GPU resource utilization in existing implementations

Technical Debt and Legacy Systems

• Outdated hardware interfaces with proprietary communication protocols

• Memory leaks in existing C++ applications leading to system instability

• Monolithic architecture preventing effective scaling

• Limited componentization creating maintenance challenges

Integration Complexity

• Diverse hardware components (cameras, lighting systems, PLCs, motion controllers)

• Multiple enterprise systems requiring data exchange (SAP ERP, MES, QMS)

• Time-series data management for historical analysis and reporting

• Real-time alerts and monitoring requirements

Technical Skill Gaps

• Limited internal expertise in modern web technologies

• Knowledge siloed within specific business units

• Dependency on specific personnel for maintenance

• Need for training and knowledge transfer across global teams

Integration Complexity

• Diverse hardware components (cameras, lighting systems, PLCs, motion controllers)

• Multiple enterprise systems requiring data exchange (SAP ERP, MES, QMS)

• Time-series data management for historical analysis and reporting

• Real-time alerts and monitoring requirements

Technical Skill Gaps

• Limited internal expertise in modern web technologies

• Knowledge siloed within specific business units

• Dependency on specific personnel for maintenance

• Need for training and knowledge transfer across global teams

Technical solution architecture

We developed a comprehensive multi-layered architecture to address these challenges:

Hardware Layer

• High-Resolution Cameras. 4K industrial cameras operating at 120fps with specialized lenses

• Precision Lighting. Programmable LED array systems with adjustable intensity and angles

• Motion Control. Servo-based positioning systems for precise camera movement

• Production Line Integration. PLC connectivity for production tracking

• Optical Triggering. IR sensors for precise image capture timing

Application Layer

• Angular Frontend (v15+). Modern component-based UI architecture

• RxJS Integration. Reactive programming for realtime data streams

• Interactive Dashboards. Real-time visualization of inspection results

• Analytics Module. Statistical process control and trend analysis

• Configuration Manager. Business unit-specific profiles and settings

• Alert System. Push notifications and escalation workflows

Middleware Layer (Real-time Processing)

• gRPC Interface. Low-latency binary communication protocol between hardware and software

• Parallel Data Processing. Multi-threaded pipeline for image pre-processing

• ML-based Detection Engine. Convolutional neural networks for defect classification

• Zero-copy Memory Management. Optimized memory handling for large image data

• Redis Cache. Temporary storage for intermediate processing results

• Error Handling System. Fault tolerance with graceful degradation capabilities

Persistence Layer

• Time Series Database. InfluxDB for performance metrics and historical trends

• Image Repository. Object storage for defect images and reference samples

• Configuration Database. PostgreSQL for system configuration and user settings Integration Layer

• ERP Connectivity. SAP integration for material specifications and order data

• MES Integration. Production execution system data exchange

• QMS Connection. Quality management system compliance reporting

We developed a comprehensive multi-layered architecture to address these challenges:

Hardware Layer

• High-Resolution Cameras. 4K industrial cameras operating at 120fps with specialized lenses

• Precision Lighting. Programmable LED array systems with adjustable intensity and angles

• Motion Control. Servo-based positioning systems for precise camera movement

• Production Line Integration. PLC connectivity for production tracking

• Optical Triggering. IR sensors for precise image capture timing

Application Layer

• Angular Frontend (v15+). Modern component-based UI architecture

• RxJS Integration. Reactive programming for realtime data streams

• Interactive Dashboards. Real-time visualization of inspection results

• Analytics Module. Statistical process control and trend analysis

• Configuration Manager. Business unit-specific profiles and settings

• Alert System. Push notifications and escalation workflows

Middleware Layer (Real-time Processing)

• gRPC Interface. Low-latency binary communication protocol between hardware and software

• Parallel Data Processing. Multi-threaded pipeline for image pre-processing

• ML-based Detection Engine. Convolutional neural networks for defect classification

• Zero-copy Memory Management. Optimized memory handling for large image data

• Redis Cache. Temporary storage for intermediate processing results

• Error Handling System. Fault tolerance with graceful degradation capabilities

Persistence Layer

• Time Series Database. InfluxDB for performance metrics and historical trends

• Image Repository. Object storage for defect images and reference samples

• Configuration Database. PostgreSQL for system configuration and user settings Integration Layer

• ERP Connectivity. SAP integration for material specifications and order data

• MES Integration. Production execution system data exchange

• QMS Connection. Quality management system compliance reporting

We developed a comprehensive multi-layered architecture to address these challenges:

Hardware Layer

• High-Resolution Cameras. 4K industrial cameras operating at 120fps with specialized lenses

• Precision Lighting. Programmable LED array systems with adjustable intensity and angles

• Motion Control. Servo-based positioning systems for precise camera movement

• Production Line Integration. PLC connectivity for production tracking

• Optical Triggering. IR sensors for precise image capture timing

Application Layer

• Angular Frontend (v15+). Modern component-based UI architecture

• RxJS Integration. Reactive programming for realtime data streams

• Interactive Dashboards. Real-time visualization of inspection results

• Analytics Module. Statistical process control and trend analysis

• Configuration Manager. Business unit-specific profiles and settings

• Alert System. Push notifications and escalation workflows

Middleware Layer (Real-time Processing)

• gRPC Interface. Low-latency binary communication protocol between hardware and software

• Parallel Data Processing. Multi-threaded pipeline for image pre-processing

• ML-based Detection Engine. Convolutional neural networks for defect classification

• Zero-copy Memory Management. Optimized memory handling for large image data

• Redis Cache. Temporary storage for intermediate processing results

• Error Handling System. Fault tolerance with graceful degradation capabilities

Persistence Layer

• Time Series Database. InfluxDB for performance metrics and historical trends

• Image Repository. Object storage for defect images and reference samples

• Configuration Database. PostgreSQL for system configuration and user settings Integration Layer

• ERP Connectivity. SAP integration for material specifications and order data

• MES Integration. Production execution system data exchange

• QMS Connection. Quality management system compliance reporting

Implementation approach

Our implementation followed a phased approach to manage risk and deliver incremental value:

Phase 1: Foundation (4 months)

• Requirements gathering and technical architecture design

• Development of core middleware components and gRPC interfaces

• Implementation of memory management optimizations

• Creation of base Angular components and RxJS integration

Phase 2: Business Unit Integration (6 months)

• Implementation of car glass inspection use case as initial proof-of-concept

• Integration with first production line environment

• Development of analytics dashboard for quality metrics

• Establishment of image repository and time-series database

Phase 1: Foundation (4 months)

• Requirements gathering and technical architecture design

• Development of core middleware components and gRPC interfaces

• Implementation of memory management optimizations

• Creation of base Angular components and RxJS integration

Phase 2: Business Unit Integration (6 months)

• Implementation of car glass inspection use case as initial proof-of-concept

• Integration with first production line environment

• Development of analytics dashboard for quality metrics

• Establishment of image repository and time-series database

Phase 3: Enterprise Integration (3 months)

• Connection to enterprise systems (ERP, MES)

• Implementation of multi-tenant configuration

• Development of alert system and notification workflow

• Performance optimization and fine-tuning

Phase 4: Scaling and Enhancement (Ongoing)

• Expansion to additional business units and production lines

• Implementation of advanced ML algorithms for defect prediction

• Development of additional analytics capabilities

• Knowledge transfer and team capability building

Phase 3: Enterprise Integration (3 months)

• Connection to enterprise systems (ERP, MES)

• Implementation of multi-tenant configuration

• Development of alert system and notification workflow

• Performance optimization and fine-tuning

Phase 4: Scaling and Enhancement (Ongoing)

• Expansion to additional business units and production lines

• Implementation of advanced ML algorithms for defect prediction

• Development of additional analytics capabilities

• Knowledge transfer and team capability building

Quantifiable results

The implementation delivered significant measurable improvements:

40% Faster Image Processing

Reduced processing time from 120ms to 72ms per frame

45% Memory Optimization

Decreased RAM requirements from 32GB to 18GB per node

50% Reduction in System Downtime

From average of 2 hours/week to under 1 hour/week

99.8% Uptime Achievement

Improved from previous 97.5% system availability

3x Faster Defect Classification

Reduced classification time from 300ms to 100ms

Unified Software Ecosystem

Consolidated 7 separate applications into a single platform

Conclusion

The client is now exploring several advanced technical enhancements:

• Container Orchestration. Kubernetes implementation for dynamic scaling

• Edge Computing. Moving processing closer to image capture for latency reduction

• Advanced AI Models. Implementation of self-learning algorithms for adaptive inspection

• Digital Twin Integration. Creating virtual models of production lines for simulation

• Predictive Quality Analytics. Using historical data to predict potential quality issues

Through a carefully architected technical approach combining modern web technologies, optimized middleware, and enterprise integration, we successfully transformed the client’s fragmented machine vision systems into a unified, high-performance platform.