How can the manufacturing industry keep up with the ever-increasing demand for efficiency and productivity? The answer lies in the incorporation of machine learning. With the ability to analyze data and make informed decisions in real-time, machine learning is revolutionizing the way we approach manufacturing processes.

According to Kings Research, the Machine Learning in Manufacturing Market size is projected to reach USD 8,776.7 million by 2030, growing at a CAGR of 33.35% from 2023 to 2030. From predicting machine failures to optimizing production lines, the technology’s potential applications are endless.

So, let's explore the impact of machine learning in manufacturing and discover how this cutting-edge technology is shaping the future of the industry. Firstly, let’s understand some basics.

Machine Learning: How it Works?

Although machine learning is a subset of artificial intelligence, not all AI-based technologies are machine learning. Other types of AI, like robotics, natural language processing, and computer vision, are used in numerous fields.

Machine learning algorithms use training data to fuel an algorithm that enables the software to solve a problem. This information may originate from various sources or real-time IoT sensors on a production floor. Neural networks and deep learning are two examples of the many techniques used in machine learning. To find patterns in a dataset and provide solutions for issues, neural networks mimic biological neurons. Multiple layers of neural networks are used in deep learning, with the first layer using raw data input and passing processed information to the next layer.

Why Do We Need Machine Learning in Manufacturing?

Before digging into the various use cases of machine learning in the manufacturing atmosphere, it’s crucial to understand the technology itself.

Machine learning comprises feeding algorithms with large amounts of data, allowing them to gradually learn and improve processes on their own, much like humans.

While machine learning and artificial intelligence (AI) are usually used interchangeably, they differ because AI refers to the broader concept of using computers and machines to simulate human thinking. In contrast, machine learning is an application of AI that allows computers and machines to learn without being explicitly programmed by humans.

Manufacturers use two major machine learning models that are ideally suited to the manufacturing methodology and its corresponding activities. These are: supervised and unsupervised.

Supervised ML entails using AI to extract patterns from massive data sets with a particular goal in mind. This is especially valuable in assessing a machine's remaining usable life and the likelihood of specific equipment breakdown.

Unsupervised machine learning, on the other hand, is concerned with discovering patterns in data sets whose outcomes are unknown. For example, engineers and supervisors can employ machine learning to identify unforeseen anomalies and problematic components in the manufacturing process. This makes machine learning particularly useful in a manufacturing facility.

Which Industries Benefit from Adopting Machine Learning in Manufacturing?

Manufacturers are constantly looking for innovative methods to incorporate novel technologies to optimize production processes. Some of the most common applications of machine learning in manufacturing today are:

1. Predictive Maintenance

The technician analyzes machine and equipment data to discover patterns that address potential failure. This enables the relevant operators and maintenance staff to repair machines before they break down, decreasing downtime, and keeping the line running.

2. Analytical and Logical Supply Chain Management

Manufacturers need to rely on their suppliers to provide appropriate and high-quality materials for the manufacturing process. Machine learning can sort through various supply-chain-related tasks such as warehouse and inventory management, inbound and outbound shipments, and product demand. This allows manufacturers to fulfill orders on time and increase productivity.

Walmart aims to achieve zero waste by 2025 by streamlining supply chain efficiency through ML & AI-powered initiatives, predicting demand, identifying bottlenecks, and streamlining logistics, reducing food waste, and optimizing inventory management.

3. Advanced Use of Digital Twins

Digital twins are virtual versions of real-world objects. This can be applied to new products, machinery, and even shop floor layouts in manufacturing settings. Manufacturing businesses also use machine learning to enhance product design and adjust the production line to work more effectively.

Recently, in January 2023, TPI Composites partnered with WindSTAR, a National Science Foundation-funded Industry-University Cooperative Research Center, to develop a digital twin manufacturing process for composite blades. The project uses machine learning (ML) and big data to provide real-time feedback, reduce defects, and increase efficiency. The team achieved around 95% predictive accuracy with 100 times faster analysis than physics-based simulations.

4. Energy Usage and Forecasting

Machine learning examines machine-generated data to identify patterns in current energy consumption. Manufacturers also employ ML technology to forecast future consumption for effective planning.

To cite an instance, Philadelphia is working on becoming carbon neutral by 2050 to reduce greenhouse gas emissions from energy use. To aid this, researchers at Drexel University's College of Engineering are developing a machine learning model called XGBoost to predict changes in energy use in neighborhoods.

The city faces challenges in customizing energy programs for each locality as building and zoning types vary widely. According to the National Institute of Health, this state-of-the-art program, XGBoost, can help predict future greenhouse gas emissions from buildings and suggest the area that needs to decrease energy use.

5. Predictive Quality and Yield

Manufacturers employ machine learning to determine potential sources of yield losses and quality faults. The technology identifies potential sources of waste, allowing firms to alter their processes accordingly. Machine learning is being used widely in F&B manufacturing.

For instance, Blue River Technology and Taranis are using AI and ML to optimize crop yields and minimize waste. Their See & Spray technology reduces herbicide consumption by 80%, resulting in economic savings and a lower environmental impact.

Concluding Thoughts

The applications of machine learning in manufacturing have the potential to transform the industry as we know it. From optimizing production lines to predicting maintenance needs, machine learning can help manufacturers increase efficiency, reduce waste, and eventually boost profitability.

Nonetheless, as with any emerging technology, there are challenges to overcome. Through continued research and development, industry leaders can work together to harness the power of machine learning and drive innovation in manufacturing in the years to come.