How AI Technology Makes Manufacturing SmarterÂ
The Industrial Internet of Things (IIoT), artificial intelligence (AI), machine learning, and sophisticated analytics are all advancing at breakneck speed, accelerating the transition to Industry 4.0. Companies are utilising artificial intelligence-powered Internet of Things solutions to notify manufacturers of equipment faults, issue maintenance reminders, increase quality control procedures, and automate processes to remain competitive.
Machine learning for Processes OptimizationÂ
Machine learning algorithms can be applied to historical data on energy consumption in the past to find patterns and trends, and this is known as pattern recognition. Then it will be possible to forecast future energy usage.
For example, if your manufacturing facility is producing high-quality items but incurring high production expenses, statistics may suggest that your facility is consuming excessive amounts of energy. The use of autoregressive models, which indicate cyclic patterns of energy consumption, is an option available to data scientists. Deep neural networks are capable of defining and detecting trends as well as forecasting energy use in real-time, allowing you to adjust your output as needed.
It is important to improve data quality to guarantee that the information acquired by your AI-powered Internet of Things sensors is accurate, personalised, and relevant to your needs and tools.
Machine Learning for Predictive Maintenance
For predictive maintenance, manufacturing facilities with a high volume of work or long working hours can benefit from machine learning. In many cases, machine learning models can be used to prevent shut-downs altogether, or at the very least to reduce the amount of time that is lost during the production process.
As previously said, when developing machine learning models, the quality of the data is critical. However, selecting the most appropriate model for the outcomes you need is an equally crucial step. Several machine learning models are being utilised to address difficulties that are frequently encountered in the manufacturing industry.
An anomaly detection method compares normal system behaviour to failure occurrences to find anomalies. When a piece of equipment deviates from the expected behaviour, the model can alert the user. Supervisors and maintenance personnel can then investigate the problem and, if necessary, rectify it on the spot.
It is possible to anticipate the remaining useful life (RUL) of a piece of equipment by using regression models. They make comparisons between usage history and static data to determine how long a piece of equipment can continue to produce before failing. This enables manufacturers to determine how long and how hard they can drive equipment to fulfil a deadline before scheduling downtime for preventative maintenance.
Classification models are capable of predicting a failure within a specified time frame. From a large deficiency to a routine issue, the failure might take many different forms. When and how maintenance should be scheduled can be determined by a maintenance team.
Computer Vision for Quality Control
While artificial intelligence can be used to improve the production process, it can also be used to aid with product quality control. An algorithm powered by artificial intelligence can efficiently verify quality using computer imaging techniques and clean picture data.
Visual inspection using artificial intelligence is a cost-effective method for producers to verify product quality in real-time. The use of computer vision for quality control is especially beneficial for businesses that operate in highly regulated industries such as pharmaceuticals and aerospace. Audi and other automobile manufacturers, for example, have employed artificial intelligence to assist with quality control tests.
Using high-resolution pictures and GPU technology, a camera-based computer vision system examines parts to determine their condition. Artificial intelligence quality control checks can alert supervisors to sudden decreases in quality during production, thanks to real-time video processing.
The scan of each part is compared to previously captured photos of perfect parts. This is accomplished through the use of deep learning neural network integration techniques. Because it is constructed using instance segmentation methods, this technology has a higher level of accuracy than other computer screening choices. Quality control is always improving since the system is constantly collecting photos of surface faults, which allows for continuous improvement. AI programme flags and removes parts from the sorting area that do not meet the historical requirements.
Edge AI: The Future of Manufacturing
Manufacturers can leverage edge artificial intelligence to improve the performance of their industrial Internet of Things processes, as demonstrated by IoT use cases.
For information to be communicated, edge AI technology does not rely on the cloud or high-speed internet connections. Incorporating this technology into a production environment can help to decrease cross-communication issues that can occur when a large number of devices are connected to the same network and bandwidth limits. The ability to accelerate the manufacturing cycle and boost throughput eventually result as a result of this.
Numerous examples of how developing technologies are transforming an ecosystem into a more intelligent one may be found. Daihen Corporation, a Japanese industrial electronics manufacturer, was able to reduce 5,000 hours of manual data entry per year for the construction of electric transformers by implementing edge artificial intelligence and data analytics.
Manufacturing systems today are only capable of achieving 90 per cent efficiency, and they are unable to achieve the remaining 10 per cent because machines fail. The underlying concept of edge AI is to ensure that the production system is never shut down. As more manufacturers invest in smart systems, the world’s economic efficiency will improve dramatically as a result of this investment.
