First, let’s define what we mean by machine learning and the various forms it can adopt. Machine learning is the process of training computers to think like human beings. This means giving them the inputs—i.e. vast quantities of real-world data—to develop their own autonomous “thought processes” over time. Machine learning is generally classified as supervised, unsupervised or semi-supervised and reinforcement earning. The two models commonly used within manufacturing are:
Can be trained using predefined criteria, to identify patterns in data. This is typically applied using one of two models:
Infers its own patterns from sets of data, without any predefined outcomes and, therefore, can’t be trained in the same way as supervised learning. Common applications include:
Predictive maintenance is one of the key use cases for ML in manufacturing because it can preempt the failure of vital machinery or components using algorithms. This, in turn, could save manufacturers significant time and money since it allows them to tackle specific issues exactly when needed—and in a highly focused way. This benefits manufacturers by:
As consumer demand grows in line with an expanding population, process-based losses are becoming harder for manufacturers to tolerate. AI and machine learning can enable businesses to get to the root cause of losses related to quality, yield, energy efficiency and so on, thereby protecting their bottom line and enabling them to remain competitive. It does so using continuous, multivariate analysis via process-tailored ML algorithms, and also through machine learning-enabled Root Cause Analysis (RCA).
ML and AI-driven RCA, in particular, is a powerful tool in tackling process-based wastage and is far more effective than manual RCA, for the following reasons:
A digital twin—a real-time digital representation of a physical object or, indeed, a process—can be used by manufacturers to carry out instant diagnostics, evaluate production processes, and make performance predictions. But more than this, digital twins can help manufacturers revolutionise their engineering practices while offering full design, production and operational customisation. The benefits of ML-enabled digital twins in manufacturing include:
According to Reportlinker, the global smart manufacturing market is predicted to be worth $314 billion by 2026. AI and machine learning have the capability to create an almost infinite number of design solutions to match any problem/product, based on preset factors like size, materials, weight, etc. This allows engineers to find the very best design solution for a product before it goes into production. Machine learning uses generator and discriminator models to:
Manufacturers can now use machine learning algorithms that process data on factors like temperature, lighting, activity levels within a facility and more, to build predictive models of likely energy consumption in the future. They do this using:
With the proliferation of IIoT technologies, it’s only a matter of time before smart supply chains completely redefine how manufacturers carry out their operations. Automation is the first rung on the ladder, but soon entire supply chains could be “cognitive”. This means that they can use AI and machine learning algorithms to perform automatic analysis of datasets including inbound and outbound shipments, inventory, consumer preferences, market trends, and even weather forecasts for predicting optimal shipping conditions. Key areas enhanced by cognitive supply chain management will be:
The potential benefits of ML within the sector are huge. However, some of the most compelling reasons to employ machine learning and AI within manufacturing are:
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