Machine learning is revolutionizing manufacturing. Here’s how to upgrade your business.

Machine Learning (ML) is a way of analyzing data that falls under the broader category of Artificial Intelligence (AI). It uses special models to learn from data and improve over time. By applying this knowledge, ML helps achieve specific goals or outcomes.

ML’s insights can be used in endless ways across the manufacturing industry. They can be deployed through a wide range of predictive processes, from inventory management to protecting networks from cyberattacks.

This benefits factory processes, particularly when it comes to detecting product anomalies and defects, and makes ML ideal for use in quality inspection and process control.

This article dives deeper into machine learning’s role in manufacturing, including process control and supply chain management, discusses various models used in manufacturing (supervised, unsupervised, and reinforcement learning), and concludes with a discussion of some of the most useful algorithms used across manufacturing processes.

Machine Learning in Manufacturing

learning uses neural networks to rapidly increase its own understanding of the data it is fed. This data could be images of defects located on the surfaces of specific source materials or product compositions at various stages of the manufacturing process. Data can also be extracted from automated systems, providing a quantitative, comprehensive report of production protocols.

When using ML in manufacturing, companies can automate the process of identifying, classifying, and assigning causality to any feature of interest required for process control purposes, updating parameters as needed.

Beyond automation, ML is uniquely able to recognize patterns and improve detection accuracy, enabling greater overall efficiency of manufacturing plants. ML can be used to optimize processes including inventory control, machine maintenance and energy use.

When ML correctly identifies a desired feature, the feature is added to its library of knowledge. If it predicts an occurrence incorrectly, it adjusts to avoid the same mistake in the future through reinforcement learning, or through supervised or unsupervised learning.

ML is able to make reliable assessments and handle complex decision-making and predictive tasks. It is also well-suited for repetitive manufacturing procedures, such as optimizing automated factory lighting, controlling factory temperatures, managing inventory supply, and ordering materials.

Integrating AI and ML into existing manufacturing systems involves several key processes aimed at enhancing efficiency, productivity, and decision-making capabilities.

These include:

• Identifying use cases
• Assessing data
• Selecting the right AI tools
• Ensuring compatibility and scalability with existing systems
• Pilot testing

AI and Machine Learning in Process Control

As a branch of AI, advanced ML technology gathers data from systems that use optical microscopy, computational super-resolution, and robotics. This technology benefits various industries, including aerospace, automotive, and genomics.

ML’s capabilities extend beyond traditional inspection stages. Nanotronics’ nControl tool monitors all manufacturing stations, assisting operators in observing new metrics to generate greater process efficiency. This allows for greater focus on often overlooked manufacturing stages like:

• Uncovering contamination risks, and alerting operators to potential malicious activity in chemical plants and graphene production facilities
• Recognizing, classifying, and measuring nanoscopic flaws in semiconductor materials
• Increasing yield and reducing costs across every stage of the consumer electronics supply chain by inspecting glass, camera sensors, processing chips, LEDs, and more

Applying ML at every production stage reduces costs and significantly increases yield for manufacturers. Defective materials are able to be discarded earlier in the production process, saving money. Production errors are corrected more quickly. Shipping and receiving is more efficient, as is order fulfillment.

Across industries:

• Car manufacturers use ML to test for safety and regulatory compliance, component inspection, and autonomous driving features
• The energy management industry uses ML to reduce costs at client facilities leveraging predictive behavior and automation
• The food industry incorporates ML in its facilities to regulate hygiene standards and maintain health code compliance.

Machine Learning in Supply Chain Management

Like with process control, ML supports supply chain management by enhancing efficiency and effectiveness within industrial and logistical operations. Process control plays a vital role in factories by ensuring that production processes are optimized, which directly impacts the overall efficiency of supply chains. Effective process control helps reduce production costs, maintain quality standards, and prevent errors across every step of the supply chain.

AI and machine learning are helping manufacturing industries automate their processes, reduce costs, and improve product quality.

Supply chain management systems currently face a number of challenges, such as a lack of human resources, shifting customer demands, transportation complications, product quality concerns, and the lingering effects of the COVID-19 pandemic.

Here’s how machine learning can help.

• Improved Forecasting Accuracy. ML for supply chain forecasting helps organizations predict demand in advance to ensure that inventory remains accurate. ML algorithms (examples in the next section) can analyze previous demand data and find hidden patterns to predict demand and detect issues before they disrupt operations.
• Easy System Integration. ML supply chain optimization functions can easily integrate into existing systems so manufacturers can keep track of inventory, improve quality control, optimize ordering, and confidently predict future needs.
• Reduced Costs. Insights derived from ML agents can help organizations optimize their logistic processes, leading to reduced delivery time and enhanced productivity. By improving quality control and anticipating customer demand, ML solutions can reduce waste and increase yields while ensuring product quality.

For businesses to thrive in a volatile marketplace, it is critical to resolve supply chain issues quickly and identify future opportunities accurately. These real-world applications make a huge difference.

• Logistics Management. Businesses can use ML to track goods from pickup to delivery and analyze real-time data to predict optimal routes and transportation modes.
• Inventory and Warehouse Management. ML algorithms can analyze historical data, trends and market movement to optimize inventory storage. It can also track products’ locations, storage conditions, and staff availability for optimal warehouse management.
• Automated Optical Inspection. ML improves quality control. Using computer vision, machine learning, robotics, and optical microscopy, manufacturers can identify and measure nanoscopic flaws during production, ensuring consistent product quality. These cutting-edge manufacturing technologies are an excellent example.
• Fraud Prevention. Supply chains using ML can analyze large data sets to detect fraudulent transactions and activities. ML can also streamline processes by automating audits and inspections while preventing credential abuse.
• Customer Service. ML enhances order visibility and tracking through real-time monitoring and predictive capabilities. Companies can update customers on their order status, which increases consumer satisfaction.

Manufacturing Algorithms

Let’s look at some machine learning algorithms and the ways they are improving operations. These algorithms are widely adopted in manufacturing for quality control, automation, assembly, defect detection, and predictive maintenance.

Each algorithm falls into one of four main categories:

• Supervised. Supervised ML uses human-labeled input and output data to train the algorithm to recognize patterns and develop the ability to make accurate predictions for new data.
• Unsupervised. Unsupervised ML involves algorithms that analyze unlabeled data to uncover patterns, structures, or relationships without being trained on any predefined outcome targets.
• Semi-Supervised. Semi-supervised ML combines labeled and unlabeled data to improve model performance, using labeled data to help guide the algorithm’s understanding of unlabeled inputs.
• Reinforcement Learning. In reinforcement learning, ML algorithms learn to make sequential decisions by acting in a controlled environment. ChatGPT is a notable example of a highly successful project that makes use of reinforcement learning.

This field may seem open-ended, but in all cases, the algorithm is directed by rewards or penalties to develop an understanding of rules, intent, and complex logic.

The twelve machine learning algorithms in manufacturing below illuminate how different ML methods are used for unique, highly specialized purposes.

• Linear Regression. This algorithm predicts outcomes by analyzing the relationship between variables. Imagine predicting sales based on advertising spend; linear regression helps establish how changes in one factor might affect another.
• Logistic Regression. Unlike linear regression, logistic regression predicts binary outcomes (yes/no, true/false). For example, determining whether an email is spam or not based on its content.
• Artificial Neural Networks (ANN). Inspired by the human brain, ANNs process data through interconnected nodes, similar to neurons. They’re used in tasks like image and speech recognition, learning patterns from vast amounts of data.
• Decision Trees. These are like flowcharts that help make decisions by splitting data into branches based on different criteria. They're useful for classifying data into categories, like sorting fruit based on size and color.
• Random Forests. This algorithm improves decision trees by creating a “forest” of trees, each trained on different data samples. It aggregates their outputs for more accurate predictions, much like consulting multiple experts before making a decision.
• Isolation Forest. Used for anomaly detection, this algorithm isolates outliers in data. It's like finding a needle in a haystack, useful in fraud detection where unusual patterns need to be identified.
• Naïve Bayes Classifier. Based on probability, this algorithm classifies data by assuming independence between features. It works well for use cases like analyzing email content for spam.
• K-Means Clustering. This groups data into clusters based on similarities. Imagine organizing books by genre — K-means helps identify which books belong together without prior labels.
• Support Vector Machine (SVM). SVM classifies data by finding the best boundary that separates different categories—like drawing a line in the sand to separate two groups of people.
• K-Nearest Neighbors (KNN). This algorithm classifies data points based on their proximity to other data points. This is analogous to predicting a person’s interests based on their friends’ interests.
• Principal Component Analysis (PCA). PCA reduces the complexity of data by transforming it into fewer dimensions while retaining essential information, like summarizing a book with a synopsis.
• Dimensionality Reduction. This technique simplifies large datasets by focusing on important features, making computations easier. This algorithm is useful in preprocessing data for other algorithms, ensuring only relevant information is analyzed.

Learn how you can implement nControl alongside our suite of state-of-the-art, ML-powered products to improve your operational efficiency, uncover cost-saving insights, and maximize uptime with ease.