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Quality control

7 reasons why GPT-4o is the pivotal point in Industry 4.0

The fourth industrial revolution, commonly known as Industry 4.0, has transformed manufacturing by integrating digital technologies, automation, and data-driven processes. In this era of smart factories, artificial intelligence (AI) plays a pivotal role. Enter GPT-4o—the latest innovation from OpenAI. In this article, we explore how GPT-4o is reshaping manufacturing and why it’s a game-changer.

In the context of the manufacturing industry, GPT-4 plays a crucial role in optimizing quality control processes. By analyzing vast amounts of data, GPT-4 can accurately detect defects and predict potential problems. This enables manufacturers to act proactively to improve the reliability of their products.

GPT-4o (“o” for “omni”) is OpenAI’s new flagship model that can reason across audio, vision, and text in real-time. It accepts any combination of text, audio, and image inputs and generates corresponding outputs. For manufacturers, this means faster and more natural human-computer interaction.

GPT-4o responds to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds—similar to human response time in a conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages. Additionally, it excels at vision and audio understanding compared to existing models.

Manufacturers can leverage GPT-4o for real-time decision-making, whether it’s analyzing sensor data, providing maintenance recommendations, or assisting operators on the shop floor.

GPT-4o’s ability to process text, audio, and image inputs within the same neural network is a game-changer. Prior to GPT-4o, models like GPT-3.5 and GPT-4 used separate pipelines for audio-to-text transcription and text-to-audio conversion, resulting in information loss.

With GPT-4o, manufacturers can directly observe tone, multiple speakers, background noises, and even output laughter, singing, or express emotion. This multimodal capability enhances communication and understanding in manufacturing scenarios.

GPT-4o can analyze historical data, sensor readings, and maintenance logs to predict equipment failures. By identifying potential issues early, manufacturers can schedule maintenance activities efficiently, reduce downtime, and prevent costly breakdowns.

Additionally, GPT-4o can optimize manufacturing processes by suggesting improvements based on data-driven insights. Whether it’s adjusting production parameters or streamlining supply chains, GPT-4o’s intelligence can drive efficiency.

GPT-4o’s multimodal capabilities allow designers to input text, images, and audio describing their design requirements. The model can generate detailed design suggestions, considering material compatibility, structural integrity, and aesthetics.

GPT-4o can assist in rapid prototyping by simulating different design variations. Engineers can explore trade-offs, evaluate stress distribution, and optimize geometries. Faster iterations lead to quicker product development cycles.

GPT-4o can analyze real-time sensor data from production lines. It detects anomalies, predicts equipment failures, and triggers maintenance alerts. Manufacturers achieve better uptime and reduce unplanned downtime.

GPT-4o can analyze historical data to identify process bottlenecks. It suggests improvements based on patterns and correlations. Manufacturers can implement changes iteratively for ongoing efficiency gains.

GPT-4o can analyze the environmental impact of a product throughout its life cycle. It considers raw material extraction, production, use, and disposal. Manufacturers can make informed decisions to minimize ecological footprints.

AI Visual Inspection Solutions

GPT-4o supports circular economy principles. Models can suggest designs that facilitate recycling and reuse, recommend eco-friendly alternatives & identify opportunities to minimize waste.

In summary, GPT-4o’s real-time interaction, multimodal capabilities, and performance improvements make it a valuable tool for manufacturers in Industry 4.0. By leveraging this AI model, manufacturers can enhance quality control, decision-making, and overall operational efficiency.

Categories
Quality control

Carbon Soot detection using Custom Object Detection model

Defect Detection Solution for Manufacturing Plants - Carbon Soot Detection

Carbon soot can detrimentally impact the nail-producing industry by compromising product quality with blemishes, increasing equipment maintenance needs, posing health risks to workers through inhalation, and triggering environmental regulations due to emissions. Controlling soot contamination is crucial to uphold quality, worker safety, and regulatory compliance in nail production. So. this carbon soot should be removed if detected.

Scanflow is an advanced AI scanning tool designed for smart devices, enabling seamless data capture and workflow automation. With the Scanflow application, users can perform tasks such as Quality Checks, ID card identification, Label Scanning, and more. We offer an automated solution to industrial challenges using state-of-the-art technologies.

Here, we’ve developed a real-time solution utilizing a bespoke object detection model to identify carbon soot. This component is an integral part of the automated solution we’ve constructed within Scanflow for our industrial client.

To train an AI model for carbon soot particle recognition, we meticulously separated a video containing the particles into individual image frames, creating a diverse dataset that exposes the model to a wide range of representations for robust learning. The following steps are used to train and test the detection with Jetson Orin Nano.

Carbon soot-containing video data is utilized for training models, with annotation performed via a custom labeling tool. Frames are extracted from the video, then preprocessed and augmented within the tool. This version of the dataset is employed for training and testing the custom object detection model.

We utilized the labeled dataset within the custom labeling tool to train an advanced AI model using a custom object detection algorithm, known for its exceptional speed and accuracy in detecting specific objects within images or videos. This approach streamlined the training process, notably decreasing both time and resource requirements.

After successful training, we deployed the AI model onto the NVIDIA Jetson Orin Nano, a compact and energy-efficient edge computing platform. Initial tests on the Jetson Orin Nano showed promising results, achieving an inference speed of approximately 22–25 frames per second (FPS) in the custom object detection model (the largest variant in custom object detection).

Throughout the training process, we faced numerous challenges pertaining to accuracy, detection performance, model size, and layer optimization. Despite our efforts to fine-tune hyperparameters for improved accuracy, and to develop lightweight models suitable for deployment on edge devices, we encountered unexpected environmental factors during real-time testing that adversely affected carbon soot detection. However, we effectively addressed these issues through augmentation techniques and further refinement of the model, ultimately ensuring robust detection capabilities even in challenging environmental conditions.

This demo video, featured on the Scanflow YouTube channel, showcases our custom object detection model in action, detecting carbon soot in real-time setups. The model is implemented on the Jetson Orin Nanodevices, offering impressive performance.

Defect Detection Solution for Manufacturing

In summary, by utilizing a custom labeling tool for data collection and annotation, training a custom object detection model with GPU acceleration, and deploying it onto the Jetson Orin Nano for inference, we’ve established an end-to-end pipeline for efficient and accurate object detection in carbon soot-containing video data. This approach not only demonstrates the adaptability of AI models to specific tasks but also showcases the integration of cutting-edge hardware platforms for real-time inference in edge computing environments.

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