Manual assembly assisted by artificial intelligence.

Computer vision can be used to assist manual assembly tasks by providing real-time feedback and guidance to human workers. This technology can help reduce errors, increase productivity, and improve quality control in manufacturing settings.

Here are some ways that computer vision can be used to assist manual assembly:

Part recognition: Computer vision can be used to recognize and identify parts as they are presented to a human worker. This can help ensure that the correct part is being used and prevent errors.

Guidance and feedback: Computer vision can provide visual guidance to the worker by highlighting the location where the part should be assembled or providing feedback on the quality of the assembly.

Error detection: Computer vision can be used to detect errors in the assembly process, such as missing or misaligned parts, and alert the worker to the issue.

Training and education: Computer vision can be used to train new workers by providing real-time guidance and feedback on their assembly tasks.

Manual assembly assisted by artificial intelligence.

In general, manual assembly processes are still very common in many companies and the repetitive nature of these tasks makes them prone to errors, effectively reducing the overall quality of the parts produced.

For this reason, we offer software that uses cameras to guide, analyze and optimize assembly processes. The computer vision manual assembly assisted by Artificial Intelligence also offers audible and visual alerts to operators when they make a mistake and provides lead time analysis.

Reduce errors and scraps with camera-assisted assembly

First, the camera-assisted manual assembly system tracks the individual steps of an assembly process. It also offers audible and visual alerts to help employees avoid mistakes.
Finally, it minimizes rework and scraps costs on the lines by reducing assembly errors by 60%.

Our program measures cycle times and their variations. This highlights process variability and also helps engineers balance lines as well as maximize process productivity.

Works with a normal webcam!

Another important added value is that the system uses standard and inexpensive cameras to track objects and movements during your processes!

Webcams can be used in combination with computer vision algorithms to enable a wide range of applications, such as object detection, face recognition, motion detection, and more.

Computer vision algorithms can analyze the video feed from a webcam in real-time, enabling the computer to understand and interpret the visual information captured by the camera. Here are some examples of how webcams can be used with computer vision:

  1. Object detection: Computer vision algorithms can be trained to detect and track specific objects in the video feed, such as people, cars, or animals.

  2. Face recognition: Webcams can be used to capture images of faces, which can then be analyzed and matched against a database of known faces for identification purposes.

  3. Motion detection: Computer vision algorithms can detect changes in the video feed and alert the user to movement or other activity in the area.

  4. Augmented reality: Webcams can be used to create immersive augmented reality experiences, where virtual objects are overlaid onto the real-world video feed.

  5. Surveillance: Webcams can be used for surveillance purposes, allowing users to monitor a specific area for security or safety reasons.

Webcams are affordable and widely available, making them a popular choice for computer vision applications that require visual input. With the help of computer vision algorithms, webcams can enable a wide range of applications and enable businesses and individuals to automate tasks, improve safety and security, and create engaging experiences.

Combination with a pick to light system

A pick-to-light system is a type of order picking technology used in warehouses and distribution centers to improve order accuracy and efficiency. It involves the use of light modules, typically mounted on shelves or storage bins, that illuminate to indicate the item to be picked for an order.

The system works by first receiving an order from a customer or picking list. The order information is then sent to a computer system that communicates with the pick-to-light modules.

The modules light up to indicate the location of the item to be picked, and the picker confirms the pick by pressing a button on the module. This confirmation is sent back to the computer system, which updates the order status and moves on to the next pick.

Pick-to-light systems are often used in conjunction with other order picking technologies, such as conveyor systems and automated storage and retrieval systems (AS/RS), to create a fully automated order fulfillment process.

They are particularly useful in high-volume and high-accuracy operations, such as e-commerce and pharmaceutical distribution centers.

Anomaly detection

Anomaly detection is an important area of interest in engineering. It can be used to avoid costly repairs of machines, industrial equipment and robots.

Accuracy

Ability to capture 95% errors at nearly 0% false positives.

Versatile

Acquisition of activities on moving lines, multi-cameras and high-mix processes.

Easy and fast

Develop a new process in hours or days instead of weeks or months.