The industrial world is changing with the emergence of smart industry. The consumers of today have been demanding more customisation & individualisation and to consciously handle resources as we start thinking about the impact on the environment. This trend has been forcing companies to embrace flexibility in their production lines as they are scaling up their large scaled customised production. Today’s machines are set up in a fixed, inflexible manner on the shop floor — commissioned, parameterised and tuned for one specific product produced repeatedly for months or even years. The manufacturing lines of tomorrow need to shorten production iteration cycles while maintaining their flexibility.
While developments around Industry 4.0 and Artificial Intelligence (AI) have been rapidly widening the limits of what is possible in the industrial world, engineers and scientists are facing the challenges of managing the growing complexity of software and an ever-increasing amount of data to create new business models and become market leaders.
Robotics – Adding flexibility to the production
With Industry 4.0, the vision must eventually come true to meet the requirement of full individualisation in production. To meet this, tomorrow’s production lines must be flexible – built from multiple mechatronic modules that can easily be rearranged, with more and more robots or ‘cobots’ (collaborative robots working hand-in-hand with human workers) and an AI that parameterises and tunes the machines according to the next – individualised – good that is manufactured on the line.
AI in manufacturing
AI has the potential to become a game changer for the manufacturing industry. This will go far beyond use cases like predictive maintenance, vision-based quality inspection, production optimisation that are already a reality today. AI-based systems will be in the centre of tomorrow’s flexible production facilities, orchestrating machines and flexibly rearranging entire production lines while minimising the consumption of energy and other resources. Several companies, including MathWorks, have been developing tools that help train and deploy AI algorithms aimed at achieving certain economic benefits.
Integration of AI into manufacturing
More engineers are realising that modelling is an important step in the AI workflow, but the model is not the end of the journey. The key element for success in practical AI implementation is uncovering any issues early on and knowing what aspects of the workflow to focus time and resources on for the best results — and it’s not always the most obvious steps.
Most often, AI is only a small piece of a larger manufacturing system, and it needs to work correctly in all scenarios with all other working parts of the continuously running manufacturing line. This includes data collected from sensors on the equipment through industrial communication protocols, like OPC UA as well as other pieces of the machine software, such as control, supervisory logic and HMI.
The AI-driven workflow
For full integration, engineers also need to focus on multiple aspects of AI. Beginning with data preparation, then modelling, followed by simulation and test, and then finally deployment, this four-step workflow allows for an AI model to be successfully integrated into a 24x7 manufacturing process.
Engineers with domain knowledge and knowledge to integrate technologies will be the most needed for future factories. Engineers have professional knowledge of the industry, equipment, processes etc., (commonly referred to as domain knowledge) and with tools for data preparation and designing models, they can get started even if they’re not AI experts, allowing them to leverage their existing areas of expertise. In this process, MATLAB provides a wealth of applet applications that can help engineers with specialised knowledge to quickly integrate new technologies, such as AI, into their practical work.
In summary
Future manufacturing set-ups will be more flexible
Production line flexibility will be accelerated by Industry 4.0 and AI technologies
Integration of AI into manufacturing can be made smooth by focusing on the four-step AI workflow
Engineers with domain knowledge who can integrate new technologies into their practical work will be the most needed for future factories
