Incorporating machine learning in manufacturing helps to increase efficiency and improve production operations. Machine learning is a smart manufacturing technology that helps industrial facilities by utilizing production data to improve machine performance. This technology goes hand in hand with artificial intelligence (AI), as machine learning is an application of AI that enables computers and machines to learn.
Industry 4.0 has shown manufacturers the importance of utilizing smart technologies. Manufacturers that implement machine learning and other advanced technology are able to save on production and repair costs, decrease errors, reduce waste, and minimize downtime. The numerous applications of machine learning in a manufacturing setting have enabled this technology to have a breakthrough in the industrial sector.
The Benefits of Machine Learning in Manufacturing
Multiple smart technologies are available to manufacturers, so why should machine learning be a focus? Machine learning is more than just a tool for manufacturers; this technology can help transform the production process.
Driven by AI, machine learning programs identify focus areas in the production process to collect data and provide actionable insights for improvements. The program then provides algorithms with the necessary data to enable learning. Once this process is initiated, machines can continuously learn and improve processes independently without direct programming from human workers.
By utilizing the data collected by machine learning programs, manufacturers can make more informed production decisions to drive improvement and business growth. Implementing machine learning techniques provides a lot of additional benefits as well. These vary based on the two different models of machine learning: supervised and unsupervised.
Supervised machine learning is used to draw patterns from data sets with a predefined end. This model helps with predicting equipment failure and a machine’s useful lifespan. The accuracy of these predictions enables manufacturers to increase production speed and productivity by minimizing production downtime.
Unsupervised machine learning, while still focusing on patterns and data, is a different model that functions with an unknown outcome. Manufacturers use the unsupervised model to identify faulty equipment and anomalies in the production process. This information can be used to reduce production losses and mitigate the costs associated with them.
Machine learning helps manufacturers with an ever-present goal: to produce higher-quality products faster and more cost-effectively. The various applications of machine learning in manufacturing allow companies to achieve this goal.
Machine Learning in Manufacturing Applications
Knowing all the benefits of machine learning helps manufacturers see the potential of this smart technology, but how can it actually be used throughout a production facility? Machine learning has several use cases within manufacturing, all of which can help to optimize the production process and maximize efficiency.
Machine maintenance schedules aren’t always accurate, which can often lead to costly downtimes. Predictive maintenance combats this by using machine learning algorithms to identify potential machine issues before they become a major problem. Human personnel are then alerted to the issue and can take the necessary steps to resolve it, whether it involves repairing a machine, or replacing a part or piece of equipment.
Predictive maintenance techniques enable manufacturers to preemptively fix machines before they break down, and eliminate the need for a fixed maintenance schedule. By employing this machine learning application, manufacturers can minimize unplanned downtime, reduce maintenance costs, and increase a machine’s useful life and availability.
Supply Chain Management
Machine learning algorithms can analyze data from suppliers, transportation companies, and other entities within a manufacturer’s supply chain to gain necessary insights. The program can then use this information to perform several supply chain management tasks, including warehouse and inventory management, logistics, and demand forecasting.
In warehousing and inventory, machine learning can control and replenish inventory as needed. Within logistics, this technology is managing inbound and outbound shipping as well as assessing the optimal route for transportation of products. Demand forecasting includes determining consumer demand for products by analyzing behaviors and preferences.
Efficient inventory management and controlled production flow based on demand help manufacturers fulfill orders on time. Additionally, monitoring and analyzing warehouse, logistics, and demand data enables manufacturers to optimize their supply chain by ensuring the right products are available at the right time.
Another application of machine learning in manufacturing is quality control, which is a focus area for any manufacturers looking to improve their products and processes. On the product side, machine learning algorithms can be used to analyze products and detect defects. By improving detection and scrapping defective parts, manufacturers can increase product quality. Early-on identification of defects can also help to reduce waste, as it enables manufacturers to remedy the issue. Improving product quality can also go a long way toward increasing customer satisfaction.
Another form of quality control is on the process side. Production losses that are the result of a defective process can result in a considerable amount of waste for a manufacturing facility. Machine learning algorithms can identify the probable cause of process-driven production losses and even make recommendations to adjust operations to prevent loss. Maintaining good production processes reduces the number of defective parts coming off an assembly line.
Inspection and Monitoring
Inspection and monitoring are other aspects of quality control, but manufacturers can take them a step further by pairing machine learning with computer vision systems. Combining sensor cameras and machine learning algorithms results in quick and accurate part inspection. This amalgamation enables sensors to “learn” to swiftly identify defects and anomalies in parts, leading to better defect detection and an increase in the number of quality parts that are shipped to clients.
Aside from part inspection, computer vision systems can also be used to continually monitor production processes. When the machine learning component is added to these vision monitoring systems, process inefficiencies are discovered sooner and more easily. Better detection due to constant monitoring reduces process-driven losses and increases process efficiency.
Machine learning can analyze the energy usage of a manufacturing facility and determine how much energy is used on average. The patterns found by machine learning algorithms can also be used to predict the energy needs of a plant. Forecasting energy usage helps manufacturers plan their energy requirements to ensure they can meet production demand and avoid production delays among fluctuations in energy pricing and availability.
Additionally, having an in-depth look into a factory’s energy consumption can help to optimize energy usage. Sustainability is becoming more and more important for manufacturers and consumers. By being conscious about energy usage and working to reduce energy waste, manufacturers can improve their sustainability efforts.
Digital twin technology allows manufacturers to create a virtual representation of a physical object or process. This can include creating virtual copies of product designs, production floor layouts, and manufacturing equipment. With the help of digital twins, manufacturers can virtually make changes to product designs and production processes to figure out how to improve their physical versions.
This virtual testing is where machine learning comes in. Machine learning algorithms can analyze digital twin data and test different scenarios, or make predictions about how the physical version of the twin would react. Pairing digital twin technology with machine learning enables manufacturers to test products and processes, and to make changes that improve quality and increase efficiency.
Automation technologies can perform physical and digital tasks without the intervention of human workers. When paired with machine learning algorithms, automated systems have increased capabilities to do even more complex work. Machine learning can take a robot from simple part assembly to “learning” how to weld or fabricate parts.
Robots enhanced with machine learning can also work better alongside their human workers, given the ability to learn their operational limits and avoid areas where human workers are. This helps to improve employee safety in factories with automation technologies. In general, automation leads to increased efficiency, reduced waste, and improved safety, all of which can be further augmented with machine learning.
Customization and personalization are increasing in popularity with consumers, and those served by the manufacturing industry are jumping on the trend as well. However, it has long been time-consuming and expensive to manufacture customized products. Creating individualized product lines to meet this need results in a lot of waste, and human workers don’t always have the skill set to customize products.
But in the Industry 4.0 era, smart technologies have made it possible to scale customization. Machine learning algorithms can take customer specifications and generate custom designs based on that information. And manufacturing these designs doesn’t necessarily require a specific production line — 3D printers can be used to create the customized product designs. Thanks to machine learning and 3D printing, manufacturers can cost-effectively produce customized products.
Machine Learning in Manufacturing
The numerous applications and benefits of machine learning have firmly positioned it as an important Industry 4.0 technology that warrants the attention of manufacturers. As a technology that falls under the umbrella of artificial intelligence, machine learning has far-reaching capabilities that allow for several possible use cases. Manufacturers can use it for optimization, customization, or sustainability, just to name a few.
Implementing machine learning helps optimize production, reduce process-driven losses, save costs, improve product quality, and gives manufacturing operations an overall advantage. Manufacturers can also pair machine learning with other advanced technologies, like automation and digital twins, to further enhance its benefits.
If you’re interested in all the possibilities of machine learning in manufacturing and want to connect with Industry 4.0-focused manufacturing companies, attend the Smart Manufacturing Experience.