Artificial Intelligence in Manufacturing

AI models for defect detection, pattern recognition, and predictions in manufacturing.

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Production process optimization

We optimize production processes by integrating analysis, custom solution design, and machinery development. With end-to-end solutions tailored to manufacturing needs, we ensure advanced data collection and analysis for real-time insights and decision-making. Employing deep learning algorithms, we predict behaviors, assess quality parameters, and identify discrepancies.
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AI-powered defect detection

Our AI-driven computer vision system excels at identifying defects in complex products on production lines. The system collects high-resolution image data from cameras and sensors, which is then preprocessed and analyzed in real-time. This technology is resistant to variations in light intensity, material structure, and product positioning, ensuring high accuracy and consistency.
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Segmentation model for product variations

AI segmentation models like U-net and YOLO have the ability to provide detailed, pixel-level classification, coupled with robustness to geometric and lighting changes, making them superior to traditional “golden template” algorithms. By reducing the need for retraining and improving accuracy, these models enhance efficiency, reliability, and overall product quality.
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Identifying critical parameter patterns

By analyzing large volumes of data collected from sensors, IoT devices, and historical records, AI models can identify patterns indicative of future events or conditions. These patterns can significantly impact machine lifespan by revealing critical parameters that predict optimal maintenance intervals, thereby ensuring maximum time before failure or excessive wear.
Elestor BV
Estebanell Energia SA
Hafenstrom AS
Halogaland Kraft (Noranett)
Plastika Skaza
Stadtwerk Hassfurt
SWW Wunsiedel

Specialized AI solutions for optimal manufacturing performance

Applying AI models in manufacturing enhances anomaly detection by providing high accuracy and real-time monitoring to identify subtle anomalies that traditional methods might miss. Our expertise lies in machine and computer vision, acoustic detection, predictive maintenance, and predictive energy management.

In machine vision, AI detects defects and ensures product consistency, while in acoustic detection, it monitors machine health by analysing sound data. Predictive maintenance benefits from AI’s ability to forecast equipment failures and optimize maintenance schedules, reducing downtime and costs. In energy management, AI optimizes energy consumption and detects irregularities, preventing waste and improving efficiency. These specialized applications significantly boost product quality, operational efficiency, and cost savings in manufacturing.

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Development and integration of
AI solutions

Developing and integrating an AI solution involves setting up a system that closely mirrors the final implementation – capturing and augmenting sufficient data, following with training and optimizing the model. The trained model is then integrated into the main program for real-time inference, determining the suitability of each piece.

Our AI models are designed to be adaptable to various hardware setups, with new equipment added only when necessary. They can be integrated into existing applications, regardless of the programming language.

These models support incremental learning, allowing updates with new data without requiring a complete retraining, and with that reducing the time needed to adapt production lines to new parts.

 

Deep learning technology:
YOLO model

Deep learning technology, particularly through the use of deep multilayer neural networks, is integral to tasks like speech and image recognition, text understanding, and experiential learning. In manufacturing, we employ the YOLO (You Only Look Once) model for its extremely fast and accurate object detection capabilities. This approach allows for precise control over output information, albeit requiring substantial input data for training. By analysing sample inputs, the system learns to recognize deviations in pixel contrast according to specified thresholds and tolerance limits, enabling it to identify defects accurately.

A significant advantage of using AI models like YOLO in machine vision is their robustness to changes in input data, such as camera shifts or lighting variations. The algorithm’s flexibility, based on comprehensive training data, ensures consistent outcomes despite environmental impacts, making it an invaluable tool for maintaining high quality and efficiency in manufacturing processes.

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AI Model Development

1

Simulation setup

Setting up a version of the system that closely resembles the final implementation. A 3D model of the setup is created, and all custom components are manufactured.
2

Image capturing

Capturing images and performing data augmentation, identifying and annotating the type, size, and location of defects on each image, creating a comprehensive dataset for training the AI model.
3

Model training

Training and optimizing the AI model through multiple cycles, followed by building the main software for equipment control, production line communication, and AI prediction.
4

Integration

The trained model is integrated into the main program to perform inference on individual images, determining the suitability of each piece.

Applications

AI machine and
computer vision

AI solutions for machine and computer vision in defect detection and pattern recognition leverage high-resolution image data collected using advanced cameras and sensors. This data is pre-processed and subjected to feature extraction to enhance its quality.

Predictive
maintenance

Predictive maintenance leverages AI to monitor equipment condition and performance by analysing data from numerous sensors tracking relevant parameters over time. By identifying critical patterns, AI predicts optimal maintenance intervals, maximizing time before failure or excessive wear.

Predictive energy
management

AI Predictive Energy Management collects real-time data from sensors and IoT devices, supplemented by historical usage records. This data is pre-processed and feature-engineered for analysis. AI and ML models, such as time-series forecasting and anomaly detection, are trained using supervised and unsupervised learning techniques.

Acoustic
detection

AI acoustic detection monitors and analyses sound waves from machinery to identify abnormalities, predict maintenance needs, and improve operational efficiency. Sound data is collected using microphones and sensors, then pre-processed to identify relevant features.

Related Solutions

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Visual Inspection

Automated, in-line AI-driven visual inspection systems for precise and consistent defect detection and quality assessment use high-resolution imaging to eliminate human error . Inspections are non-destructive, ensuring all products meet stringent standards without any damage.
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inSpect Bent Pin

In-line visual inspection technology for detecting bent pins in electronic connectors. It provides high-resolution images for defect detection and precise measurement of pin heights, mitigates reflections, and accommodates variability in pin dimensions and materials.
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inSpect Fuel Cell

In-line visual inspection technology for high-volume roll-to-roll fuel cell production, essential for fuel cell quality assurance. It detects and analyses defects such as scratches, cracks, particles, and pattern irregularities in real-time while monitoring crucial process parameters.
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inSpect CAT / DPF

In-line vision inspection technology, using high-resolution line scan cameras, backlighting, and advanced image processing algorithms to identify cell blockages in catalytic converters and optical leaks in DPFs, as well as any deformations like chips, cracks, or gauges.

Frequently Asked Questions

At INEA, we primarily develop our AI models using the Python programming language within the Visual Studio environment. This allows us to tailor our solutions specifically to the unique needs of each project, ensuring optimal performance and flexibility.
The optimal model is selected during requirement analysis, and necessary equipment procured. A 3D setup model is created, and custom components manufactured. Initially, real images are collected for AI model development. The customer and engineering team define defect types and locations, to create the training dataset. After training and optimization cycles, the main control program is finalized. The system is then integrated into the production line, followed by a fine-tuning period to improve the models further.
The timeframe for developing an AI solution varies depending on the complexity of the required models. The first phase typically involves the physical setup of a version of the system that closely resembles the final implementation. The second phase includes capturing enough images / data and performing data augmentation. The third phase is dedicated to training and optimizing the model. In the fourth phase, the prepared model is integrated into the main program to perform inference on individual images / data and determine the suitability of each piece.
If the initial training strictly adheres to the quality expert's assessment, any change in acceptance criteria requires retraining or updating the AI model. However, a preferred approach involves labeling and categorizing all defects during data preparation by size and contrast. This method allows for determining product suitability using adjustable analytical thresholds, making it unnecessary to retrain the model with each change in acceptance criteria.
At INEA, we primarily develop our AI models using the Python programming language within the Visual Studio environment. This allows us to tailor our solutions specifically to the unique needs of each project, ensuring optimal performance and flexibility.
The optimal model is selected during requirement analysis, and necessary equipment procured. A 3D setup model is created, and custom components manufactured. Initially, real images are collected for AI model development. The customer and engineering team define defect types and locations, to create the training dataset. After training and optimization cycles, the main control program is finalized. The system is then integrated into the production line, followed by a fine-tuning period to improve the models further.
The timeframe for developing an AI solution varies depending on the complexity of the required models. The first phase typically involves the physical setup of a version of the system that closely resembles the final implementation. The second phase includes capturing enough images / data and performing data augmentation. The third phase is dedicated to training and optimizing the model. In the fourth phase, the prepared model is integrated into the main program to perform inference on individual images / data and determine the suitability of each piece.
If the initial training strictly adheres to the quality expert's assessment, any change in acceptance criteria requires retraining or updating the AI model. However, a preferred approach involves labeling and categorizing all defects during data preparation by size and contrast. This method allows for determining product suitability using adjustable analytical thresholds, making it unnecessary to retrain the model with each change in acceptance criteria.

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