Machine learning is already key to the transformation of industrial production

We’ve been listening and reading for a while about the term machine learning and its possible applications to the industry. In fact, those who have decided to explore the benefits of it try to see how their business can benefit from one of the greatest technological advances that have occurred in recent years. But for this, it is necessary to be accompanied by experts in the field, from entities that have previous experience in their study and implementation, since it is not a mechanism simply adding to industrial production processes.

Machine learning is not a device that can be connected to a production line and make it work better than before incorporating it. Rather, it is a process that requires input from many devices to feed data and to collect, evaluate, and use it to develop knowledge about how a production line manufactures the parts or finished products it works on. Machine learning helps determine how a production line is able to achieve higher performance, work at lower cost, and do it more reliably.

Machine learning allows you to transform an industrial process into a system of systems capable of making the products you need available to the customer at a lower price so that you can continue to be competitive in your market and thus keep your customers satisfied through quantitative improvements and qualitative that occur during the process. If you’re going to tag that machine learning app, it’s a higher markup that will create more innovative products to make customers even happier.

Machine learning is a process-based learning, which allows to identify the industrial technology that has to be created or modified thanks to the use of machine learning computer algorithms, so necessary in the new times of intelligent manufacturing.

Machine learning provides data with which to teach the computer algorithm what to expect from the production machines it is monitoring to obtain quantitative information, based on pattern recognition and inference to develop the algorithm’s ability to make decisions and predictions, without having to write programming code to accomplish that task.

Training data is collected, processed, and evaluated in a structured sequence of steps to prepare that data for use in the machine learning algorithm. That structured sequence of steps is a process, and creating that process introduces new technologies that include IoT devices to create data, networks for data storage and processing, highly accurate and relevant data cleaning computer processes and applications. industrial and transformations attributed to machine learning.Since there are numerous new technologies that can be attributed to machine learning, we will see below which are the easiest to identify.

Predictive Maintenance

The ability to predict necessary outages on the production line before those outages occur is truly valuable to an industrial manufacturer. Unscheduled downtime affects profit and can also be uncomfortable for customers, and some of them may be lost.

They also disrupt the supply chain, causing an unwanted excess stock. Adopting machine learning in an industry enables increased predictive maintenance that eliminates unscheduled work stoppages.

IT, OT convergence and network security

The development of machine learning also affects many modifications of the business model in relation to the manufacturer’s standard operating procedures. Something that implies changes in the organizational composition of the company.

The computer network is located together with the operational sensors on the production machinery so that the data is collected and sent to the data warehouse as training data for machine learning purposes. Plant operators and technicians need to work more closely together so that they are not affected if the network becomes temporarily unreliable or if at any given time there is a hack through, for example, a denial of service attack ; a fact that leads to having to stop production.

Faced with a broader concept in the field of data analysis, job training production processes become increasingly flexible, transparent and intuitive. Novel technologies like the blockchain in manufacturing, the industrial internet of things and the robotics industry use machine learning to grow in potential and offer the best of themselves.

Smart manufacturing, design and digital innovation

The ultimate goal of artificial intelligence and machine learning is to enable the development of a digital twin on the production floor. A digital twin (digital twin) is a virtual replica of an object or system that simulates the behavior of its real counterpart in order to monitor it to analyze its behavior in certain situations and improve its efficiency.

The creation of the digital twin would be carried out as an effort under a model-based systems engineering (MBSE) process using machine learning algorithms and acquired knowledge as a basis. The digital twin would serve as a platform to run hypothetical scenarios to learn what is not yet known today. The digital twin can also serve as an end-to-end model for use in designing more reliable parts and adjusting interactions between machines on the production line to improve performance. The possibilities are endless.

Additional impacts

Machine learning enables the emergence of other advanced advantages for production in the industry. These include quality control and overall equipment effectiveness metrics by measuring the availability, performance, and quality of assembly equipment using improved neural networks that can learn machine weaknesses and minimize their effect on production. It also enables the possibility of building a fully connected supply chain, making logistics benefit and inventory management processes significantly improved through machine learning techniques.

Machine learning is no longer a rare bird in the industry but is becoming a standard practice over the years. It is, without a doubt, an investment resource that allows industrial companies to anticipate their competition, leaving behind practices that have already become obsolete.