- The main objective of this project is to showcase advanced robotics solutions that increase the agility of the automotive cable production industry.
Three use-cases are exploited:
- Collaborative robotics, demonstrate a collaborative assembly of different car fuse boxes between a human and a robot. Safety areas and a safety line are projected on the assembly station to keep the worker aware of his working space;
- Human-Robot Interface (HRI) using an Augmented Reality (AR) application for training-on-the-job and assisting workers in maintenance operations;
- Bin-picking with machine vision where a robot detects different types of connectors in unpredictable positions, determines the best grabbing position and sort them in different output trays, depending on the connector type.
Allbesmart, a Portuguese SME that provides R&D services on ICT for several industries, specialized in communication networks, IoT solutions, AI and AR applications.
This demonstrator includes collaboration with StoneShield (www.stshield.com), a robotic integrator that is the adopter of the developed technology.
StoneShield has a solid understanding of the automotive cable assembly industry, its requirements, and main challenges in its digital transformation.
- Collaborative assembly reduction of 30%: Collaborative assembly has the potential to considerably reduce assembly times, whilst ensuring, reduction in human errors by inspecting the final product quality. They may also increase workplace ergonomics. Using a mid-range collaborative robot, assembly times can be reduced by, at least, 30%.
- Training-time reduction by 50%: Augmented Reality (AR) based solutions improve knowledge transfer, and reduced training time by 50%. They also improve field service quality and productivity, reducing overtime spend with maintenance operations.
- Cost savings up to 70%: AR with remote video assistance considerably reduces, the need for travels related to maintenance.
- Automated bin-picking with cycle times below 10s: AI machine vision is currently an accessible technology that can be coupled with affordable robotic machines to perform automated tasks of bin-picking with ordered sort, which are applicable in many industry use cases. Mid-range robots can achieve cycle times below 10s, depending on the type of target objects and the robot gripper (clamp or suction).
Manufacturers of robotic machines:
- Machine vision algorithms to assist automated tasks and perform quality control (visual inspection) of assembled products.
- Augmented reality applications for training-on-the-job and assistance in machine maintenance operations.
Industries with manual assembly processes:
- Collaborative robots (cobots) assisted by machine vision and safety systems, for a quicker, more efficient assembly process, also ensuring quality in assembled products by reducing worker fatigue.
- Augmented reality solutions to support technicians in the maintenance operations for many different types of machines (e.g., heating, ventilation and air conditioning (HVAC)).
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