Artificial intelligence (AI) and machine learning (ML) are two technologies that are revolutionizing the industrial sector. The manufacturing area is no exception. Developing a Smart Factory is an opportunity to be competitive, to optimize timelines and make product design and production more efficient. Quality, worker safety, and sustainability are the fundamental pieces where these technologies can participate in the redesign towards high productivity, much safer, and more sustainable manufacturing.
Manufacturing companies that are committed to finding their applications, understanding market trends and changes, to remain competitive. Seeking leadership will facilitate compliance with industry regulations and standards, improving safety and addressing environmental impact concerns.
Artificial Intelligence (AI) and Machine Learning (ML) Applications in Manufacturing
Solving the problems that have affected the industrial sector for decades is a trend present throughout the production and supply chain. The intelligence derived from the analysis and monitoring in real-time is essential to generate profitable and sustainable solutions. Avoiding bottlenecks within production chains is not a chimaera since it is possible to visualize the processes at all times. You can say goodbye to last-minute failures and costly downtime emergencies. Being able to predict and make repairs quickly with effective preventative maintenance. Here are some of the benefits of a data-driven process revolution.
Let’s take a closer look at some of its applications:
Predictive maintenance in production processes
As mentioned, machine learning facilitates predictive maintenance, anticipating equipment failures, scheduling maintenance at the right time, and reducing unnecessary downtime. The reality is that manufacturers spend too much time troubleshooting rather than allocating resources to plan maintenance. By implementing machine learning and predictive analytics, efficiency can increase between 65% and 85% (Gartner).
There are several equipment failure machine learning models that depend on the objective or approach of the prediction that is sought. These might be:
- RUL (Remaining Useful Life) models: Regression models to predict the remaining useful life.
- Historical and statistical data are used to predict how many days until a failure occurs.
- Classification models to predict a failure in a defined period.
- Used to define a model that predicts failures within a defined number of days.
- Anomaly detection model to identify items with potential problems.
- The approach predicts failures by comparing and identifying differences between normal system behaviour and failure events.
These are some models that can support maintenance accurately and quickly. They also allow us to analyze their nature and frequency, to generate guidelines for optimization and continuous improvement.
Digital Twins in Manufacturing.
In the production area, active machinery Digital Twins, or even the entire complete system, provide real-time diagnostics and evaluations of the production, the performance and monitoring prediction, and the visualization process of all kinds of key parameters. To generate the models that understand the physical systems, machine learning unsupervised algorithms are used. As the data is processed, these algorithms look for patterns of behaviour and detect anomalies. They also can process external data, such as research, industry data, social media, and media.
Digital Twins are a tool not only applicable for product design, but also for simulating the performance of existing physical products.
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Quality control in Manufacturing
Machine Learning can be applied for product inspection and quality controls. ML-based algorithms learn from historical data that distinguish good products from those with defects, thus automating the inspection and supervision process.
On the other hand, it is possible to develop algorithms that compare samples with the most common types of defects. It is an automated process where failure detection rates can increase up to 90% (Forbes).
Deep Learning
Deep learning can improve quality control tasks on large assembly lines. Using deep learning architectures, such as convolutional neural networks, the human operators in charge of detecting visual clues indicative of quality problems in products and parts in highly complex assembly processes can be replaced. The advantage of this branch of machine learning is that it is much more scalable through image learning and object detection.
Quality improvement
Faced with a market surrounded by short deadlines and a higher level of complexity of the products, it is difficult to comply with quality standards and regulations.
The customer expects flawless products, as defective products cause non-conformities that damage the reputation of the company and its profit margins. AI can foresee quality problems right down the production line, even the most subtle ones.
Artificial vision is an example of an Artificial Intelligence solution, in which high-resolution cameras are used to monitor defects, even better than a human being. This can be combined with a cloud-based data processing framework to generate an automated response. You can also track products already on the market, which generate data to make better strategic decisions in the future.
AI & ML for logistics and inventory management
The manufacturing industry requires extensive logistics capabilities to run the production process. Machine learning is a solution that allows you to automate various logistics tasks, increasing efficiency and reducing costs. A basic opportunity is a possibility of performing this automation in routine tasks, saving thousands of hours of human work per year. On the other hand, ML algorithms can also be applied for resource management.
Up to 9 applications can be identified in which ML can transform Supply Chain management.
Google, through its own AI, DeepMind, has managed to reduce its data centre cooling bill by 40%. Let’s see what Deep Mind is:
AI for product development
Product development is one of the most common applications for machine learning. For both new developments and improvements to existing ones, an exhaustive data analysis is required to optimize and offer the best results.
Using AI solutions, a large amount of product data can be collected and analysed to understand consumer needs, uncover hidden flaws, and identify business opportunities. This facilitates existing products redesign while allowing the development of better products and business models for the company. Using these tools is key to innovation, reducing the risks associated with the development of new products, facilitating much more informed decision making.
Generative Design for Manufacturing
It is a design branch focused on the union of creativity with machine learning. Where AI facilitates all possible design options for a given product. By selecting parameters such as weight, size, materials, manufacturing and operating conditions with generative design software, engineers can generate different design solutions in a short time. To later select the appropriate design to put it into production.
These technologies application in different phases of the production process helps find new opportunities to develop more efficient processes, reducing waste and saving money. These technologies will continue developing and they will be a key tool in the face of continuous market changes. It is imperative that manufacturing becomes a process that is as optimized as possible in terms of human resources, time, and materials optimization.
Companies that do not apply Artificial Intelligence and Machine Learning in their production processes will cease to be competitive in an ever-closer future.
