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The main manufacturing goal, the path to which is endless, is to produce the highest quality products at the lowest cost. Introducing new technology is one of the most effective ways to improve quality and reduce costs. In this article, we will consider the advantages of implementing Machine Learning and Artificial Intelligence algorithms for manufacturing.
Data is a valuable resource, but with the development of technology it has become very cheap to collect and store it. This is a paradox from the point of view of market theory, and gives great advantages to those companies that are ready to start using this opportunity right now. AI and process-based ML in particular allow you to use data to improve production efficiency and employee safety.
Improving maintenance efficiency using ML
Maintenance makes up a significant part of the company’s total expenses. Companies conduct periodic monitoring and predictive maintenance in order to optimize their expenses. Previously, the predictive maintenance was performed using SCADA systems, with the operator manually setting up all parameters
This approach did not take into account all the data, and complex dynamic behavioral patterns of the machinery were ignored. For example, while the machine was undergoing sterilization, its sensor could detect a sharp increase in temperature. As the standard system can’t see that it is heat treatment and not an emergency that is causing the temperature to rise, it will proceed to trigger an emergency protocol.
ML algorithms take into account data collected from the production process (sensors, SCADA, etc.), as well as contextual data and manufacturing process information. ML allows AI to detect problems and anomalies and check them through various dataflows. The benefit of ML is the capacity to analyze large amounts of data and propose different courses of action. The condition of each system element is evaluated in real-time and displayed on a digital model. This allows you to anticipate possible failures and notice equipment deterioration at an early stage.
Enabling predictive quality analytics with ML
AI can help not only with equipment maintenance but also with product quality control and improvement. In some situations, you cannot avoid the deterioration of product quality and ML can predict the moment and degree of decline, which will help you stop the process in time, reduce the consumption of raw materials, and reduce the wastage of energy and time.
ML can be divided into two main methods – supervised and unsupervised.
Supervised machine learning is more commonly used in manufacturing than unsupervised ML. It helps to achieve the goal in a very simple and clear way: getting a function that connects incoming and outgoing data. Supervised ML requires a high level of involvement: data input, working with algorithms, data displaying, and so on. The result is a function that allows you to predict outputs when new information is inputted into the system.
The technique of supervised learning, in turn, is split into two approaches: classification and regression. Both have the same goal – to show the relationship between the input and the output information
Classification is useful when data exists in defined categories. For example, this algorithm is used by e-mail services when making a decision about whether an email should be sent to the spam folder, or not. The advantage of classification is that only a small amount of data is needed to achieve a high level of accuracy.
Predictive maintenance uses a multi-class classification since there are many probabilities of failure of the system’s various elements. These possible outcomes are classified as potential problems and calculated using additional variables.
Regression is used when working with data in a certain range (e.g. temperature, weight) collected, for example, from sensors. In manufacturing, this approach can be used to calculate the Remaining Useful Life (RUL) of an asset, i.e. a prediction of life span before a possible failure.
The most commonly used ML algorithm for regression is linear regression, which is relatively quick and simple to implement and allows the output data to be easily interpreted. For example, it can be used in a system that predicts temperature.
With Supervised ML we train the algorithm to achieve the expected result. With Unsupervised ML the outcome is unknown.
Sometimes, the outcome is not just unknown – even the information about the data labels is absent. The clustering of input data points with certain characteristics allows the discovery of basic patterns. Additionally, clustering can help to reduce noise when working with multiple variables.
Artificial neural networks
Neural networks can be an effective unsupervised tool for various applications in manufacturing, such as process modeling or predictive quality analytics. Consisting of around 100 billion neurons, the basic structure of a neural network makes it possible to process a large number of parameters on multiple levels. This feature makes neural networks a suitable tool for working with unstable processes.
Data is the most important thing in ML, so understanding some key aspects of data quality and type determines its effectiveness. For example, Predictive Maintenance allows focusing on failure events. In order to predict possible failures, one should start by collecting historical performance data. The longer the nominal life span of the equipment is, the more historical data you need to analyze. It is also necessary to consider other features of the system.
When preparing the data, it is necessary to answer the following questions:
Cost reduction due to predictive maintenance, predicting RUL, improved supply chain management, quality control, and improved Human-Robot relationships – all these are just some of the numerous benefits of introducing AI and ML in manufacturing. Every now and then, we consult our clients from the manufacturing sector on how to implement innovations in their processes. Companies’ interest in AI, ML, IoT, and other 4.0 technologies allows us to offer our clients the most innovative solutions, without limiting ourselves to industry-standard frameworks.
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