While humans are still better than computers at creating consumer appeal, Artificial Intelligence (AI) and Machine Learning (ML) influence several different performance characteristics. By leaning on AI and ML tools, design teams can spend more time being creative rather than spending hours on trial-and-inefficient experimentation.
Artificial intelligence and machine learning are transforming the world – not least the manufacturing industry. From significant cuts in unplanned downtime to better-designed products, manufacturers are applying AI-powered analytics to data to improve efficiency, product quality and the safety of employees. Significant changes from AI are already being seen across manufacturing from the design process to the production floor itself.
This is one of the key use cases for ML in manufacturing because it can pre-empt the failure of vital machinery or components using algorithms. Predicting when machinery will need maintenance could save manufacturers significant time and money. It allows them to tackle specific issues exactly when needed, reducing both planned and unplanned downtime and prolonging the remaining useful life of machinery by preventing any secondary damage during repairs.
As consumer demand grows in line with an expanding population, process-based losses become harder for manufacturers to sanction. AI and machine learning can enable businesses to get to the root cause of losses related to quality, yield and energy efficiency and can enable them to remain competitive.
Machines using AI are better at spotting manufacturing defects. AI cameras act as automated optical inspection machines that look for defects in manufactured items. Prior to AI cameras, factories used basic image recognition technology and then sent products with possible defects to human workers to look for problems. In many cases, up to 40% of products sent were not actually defective, causing huge wastes of time and effort.
AI is changing the way companies design products too. Detailed briefs of products are put into an AI algorithm which then explores the best ways to make the product and the best solutions for any anticipated problems. Generative design software will explore every possible way to design the product, and then decide on the best method. It’s completely objective and doesn’t use any human assumptions. Everything is tested according to its actual performance.
Digital twins can help manufacturers revolutionise their engineering practices while offering full design, production and operational customisation. This can lead to significant reductions in costs, improved reliability of production lines, optimised performance and productivity and reduced risks on the shop floor
AI can be used to optimise supply chains and help companies predict market changes, giving them a huge advantage to be strategic with products and updates, and plan ahead rather than just responding to changes. Companies can be ahead of the game, releasing new products and updates at the exact time the consumer market is searching for them.
The potential benefits of ML and AI within manufacturing are huge giving organisations the ability to significantly reduce process-driven loss, boost capacity through process optimisation, extend the life of machinery and equipment, enhance quality control and improve supply chain management.
In the coming years, humans and industrial robots are going to collaborate more and more requiring humans to be trained in advanced AI management which will open up more jobs in design, maintenance and programming.