No industry can afford, at this point in time, to ignore the advantages that Artificial Intelligence (AI) and machine learning (ML) offer in terms of process improvement, occupational well-being, increased productivity and increased competitiveness. In 2019, investment by companies in machine learning software increased to $37.5 billion globally. By 2023, this figure will increase by a factor of 2.5 to $97.9 billion, according to International Data Corporation (IDC) estimates.
The maturity of machine learning is setting the trends in industrial automation of 2020. One of the most revolutionary areas is quality control. Machine learning is already an ally for engineering professionals working in these departments. For this reason, BETWEEN Technology wants to help you discover the most promising applications of machine learning in quality control within the manufacturing industry. Don´t let them pass you by!
What is machine learning and what is it used for?
Machine learning or automated learning is a data processing methodology that enables machines to learn, make smart decisions, based on analysing historical data, and carry out actions without the need for human intervention. A machine learning algorithm is able to identify patterns, use them to predict what will happen in the future and improve its hit rate over time.
Machine learning can be applied to a wide range of fields such as health, digital marketing, mobility, cybersecurity, finance, and of course, manufacturing, where it acts as a major driver of the changes promoted by the Fourth Industrial Revolution.
Applications of machine learning in industry 4.0
The applications of machine learning in industry 4.0 have ceased to only benefit a limited number of companies, with sufficient resources and talent to take advantage of them. Nowadays, they have become widespread thanks to the availability to programmers of open source libraries such as Google's TensorFlow. Furthermore, the sophistication of image analysis and object recognition technologies have simplified the sensorisation and monitoring of spaces.
Specifically, machine learning has introduced changes in core industry tasks such as:
- Machinery maintenance. With Machine Learning, breakdowns are detected instantly. Furthermore, the system warns in advance when it thinks one of these failures is going to occur, allowing preventive measures to be taken before the machine breaks down. The advantage? No more production downtime or budget overruns.
- Demand forecasting. Thanks to analysing statistical data and looking for patterns and correlations, machine learning identifies the signs that precede a drop or a surge in demand. In this way, it is possible to adjust resources in advance to stay ahead of the curve.
- Optimisation of the production chain. When problems arise, it takes us humans minutes, hours or even days to become aware of them. With Machine learning, detection is immediate, and problems can be solved almost instantly. This minimises interruptions and mitigates setbacks.
- Customer service. The incorporation of chatbots provides increasingly fast and customised solutions to common issues.
- Quality control. Machine learning focuses on the smallest flaws. Thus, they will be corrected in the early stages of production.
How is the quality of a product ensured with machine learning?
Using machine learning in quality control in the manufacturing industry reaches its peak in industries such as the food, software development, packaging or manufacturing industries.
Machine learning in the food industry
The digital processing of images is very helpful in monitoring food quality. Machine learning enables Petri dishes to be automatically analysed in laboratories, as well as the conditions of fruits, vegetables, meat or fish to be evaluated with a very high percentage of reliability.
Software quality control
Manually reviewing lines upon lines of code is a daunting task that takes forever; not to mention the possibility of overlooking some small details. On the other hand, with machine learning, this process can be completed in less time and with less risk of inaccuracies. This guarantees more stable and fail-safe software.
Machine learning in the manufacturing industry
The manufacturing industry, and, in particular, the automotive industry, has come across a gold mine in Artificial Intelligence and machine learning. By means of a profuse network of sensors, it is possible to monitor the entire vehicle assembly chain down to the very last detail, which prevents vehicles with manufacturing defects from being put on the market.
Reviewing containers and packaging
Failed deliveries, customer dissatisfaction, financial losses... Incorrect labelling can be the source of dissatisfaction. However, machine learning enables the exhaustive and automatic inspection of each element before it leaves the warehouse, which ensures that all the necessary information is included in the container or packaging.
Despite the current applications of machine learning in quality control in the manufacturing industry being impressive, this technology still has a lot of room for development. The engineers of tomorrow have a great ally to achieve their goals. Get the most out of machine learning, and keep growing in your career with BETWEEN!
