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A food manufacturer was looking for ways to automate a manual process. Due to a diverse range of meat substitutes with varying product properties, finding an efficient and flexible solution was a challenge. To investigate how Vision AI and robotics could contribute to this, QING was engaged to conduct a feasibility study.

Assignment

Pick-and-place process

Issue

Automating a diverse pick-and-place process

Applied tools and expertise:

Vision AI
Food Automation
Simulation

Diversity as a challenge

The current pick-and-place process is performed entirely manually: moving meat substitutes from a crate to a package. This creates high labor intensity and limited scalability. An automated solution must meet the following requirements:

Compact design with minimal impact on the existing production line.
Dealing with disordered and diverse products in a crate.
Ability to add new products to the system quickly and easily.

The biggest challenge was the diversity of the products. Different shapes, textures and surfaces required a flexible and precise approach.

The approach

QING conducted a feasibility study in the Development Lab to explore how Vision AI and robotics can automate the pick-and-place process.

The steps in the process:

Product segmentation and orientation: vision AI was deployed to detect products in unordered crates and determine their orientation. ‍
Gripper development: different grippers, including suction cups, were tested to find the right balance between grip and flexibility, even for complex or fragile products. ‍
Simulations: through simulations, we gained insight into optimal situations and space usage for robots in a production environment.
Prototyping: physical solutions were developed and tested in QING's Development Lab.

"The current vision AI model allows customers to process various products in an automated way. New products can easily be added to the system, making it increasingly flexible and ready for the future."

Bram van Riessen, Software engineer at QING

A future-proof solution

The feasibility study provided valuable insights:

Vision AI can effectively segment and orient products even in chaotic crates. ‍
Flexible grippers offer a solid solution for various product shapes, with adaptability for more complex items. ‍
Scalability: The system is easily expandable to new product types, making it future-proof.

Discover the Possibilities

Wondering how Vision AI and robotics can make your manufacturing processes more efficient and consistent? Contact us and find out how we can solve your challenges together.

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