The powerful combination of robotics and machine learning (ML) is poised to revolutionise the manufacturing sector in South Africa. If adopted by local businesses, this technology has the potential to dramatically increase output, efficiency, and competitiveness.
“Machine learning in automation and robotics allows for a significant increase in productivity within manufacturing processes. This will undoubtedly optimise most operations, leading to a higher degree of competitiveness,” Andrew Crackett, managing director at Yaskawa South Africa said, Zawya reported.
Enhancing current automation capabilities is a major benefit of incorporating machine learning into robotics. Because they follow pre-programmed instructions, traditional robotic systems are less flexible in changing environments.
On the other hand, ML has allowed these systems to analyse enormous volumes of data, learn from previous assignments, and make wise decisions on their own. This translates to enhanced adaptability, better system defect detection, and optimal performance without continual human intervention.
Collaborative Effort
The technology also promotes a safer and more collaborative manufacturing environment.
“Collaborative robots, or cobots, like the MOTOMAN HC Series robots, not only enhance productivity but also foster a safer and more collaborative manufacturing environment,” Crackett explains.
These cobots use cutting-edge machine learning algorithms to enable natural human-robot communication.
ML propels manufacturing operations toward ongoing improvement. ML algorithms can recognise patterns, anticipate system failures, and optimise maintenance schedules through a network of sensors, cameras, and other data sources.
“This predictive maintenance approach can significantly reduce downtime and maintenance costs, while also prolonging the lifespan of machinery,” Crackett said.
Furthermore, ML makes adaptive manufacturing possible, allowing production processes to be modified instantly in response to shifting consumer demands. A company’s position in the quick-paced global market is strengthened by its agility.
However, overcoming certain obstacles is necessary to fully utilise this revolutionary technology.
“Data quality, security concerns, and ethical considerations are amongst the more critical issues that need to be addressed to ensure the responsible use of ML-optimised robotic systems,” Crackett notes.