I. Deep Learning for Defect Detection
In manufacturing, defect detection processes on production lines are becoming increasingly intelligent. Deep neural network integration enables computer systems to identify surface defects such as scratches, cracks, and leaks.
By applying image classification, object detection, and instance segmentation algorithms, data scientists can train visual inspection systems to detect defects for a given task. Combining high-resolution optical cameras and GPUs, deep learning-driven inspection systems will have significantly better perceptual capabilities than traditional machine vision.
For example, Coca-Cola built an AI-based visual inspection application. This application diagnoses facility systems and detects problems, then notifies technical experts of the issues detected, helping them take further action.
II. Predictive Maintenance Through Machine Learning
Rather than repairing or scheduling equipment checks after a failure occurs, it's better to predict problems before they happen.
By utilizing time-series data, machine learning algorithms can fine-tune predictive maintenance systems to analyze failure modes and predict potential problems. — This data is collected and processed by machine learning algorithms as sensors track parameters such as humidity, temperature, or density.
Depending on the prediction objective, such as the remaining time before failure, the probability of failure, or anomalies, there are several machine learning models that can predict equipment failures:
① Regression model for predicting remaining useful life (RUL). By utilizing historical and static data, this method can predict how many days are left before failure.
② A classification model for predicting failures within a predetermined time period. To define when a machine will fail, we can develop a model that predicts failure within a defined number of days.
③ Anomaly detection models can tag devices. This method can predict failures by identifying differences between normal system behavior and fault events.
The main benefits of predictive maintenance based on machine learning are accuracy and timeliness. By revealing anomalies in production equipment and analyzing their nature and frequency, performance can be optimized before failures occur.
III. Artificial intelligence will create digital twins
A digital twin is a virtual copy of a physical production system. In the manufacturing field, digital twins exist, consisting of specific mechanical assets, entire mechanical systems, or specific system components. The most common uses of digital twins are real-time diagnostics and evaluation of production processes, prediction and visualization of product performance, and so on.
To teach digital twin models how to optimize physical systems, data science engineers used supervised and unsupervised machine learning algorithms. By processing historical and unlabeled data collected from continuous real-time monitoring, machine learning algorithms can find behavioral patterns and identify anomalies. These algorithms help optimize production planning, improve quality, and maintain systems.
Furthermore, NLP technology can be used to process external data from research, industry reports, social networks, and mass media. It not only enhances the functionality of digital twins, enabling the design of future products, but also allows for the simulation of their performance.
IV. Generative Design in Intelligent Manufacturing
Generative design is based on machine learning, which generates all possible design options for a given product. By selecting parameters such as weight, size, material, operation, and manufacturing conditions in generative design software, engineers can generate numerous design solutions. They can then select the most suitable design for future products and put it into production.
The use of advanced deep learning algorithms has made generative design software intelligent. One of the new trends in artificial intelligence is Generative Adversarial Networks (GANs). GANs use two networks in sequence: a generator and a discriminator. The generator network generates new designs for a given product, while the discriminator network classifies and distinguishes between the designs of the real product and the generated products.
Therefore, data scientists develop and teach deep learning models to define all possible design variations. The computer becomes a so-called "design partner," generating unique design ideas based on the constraints given by the product designer.
V. Energy Consumption Prediction Based on ML
The growth of the Industrial Internet of Things (IIoT) has not only automated most production processes but also made them more economical. Energy consumption can be predicted by collecting historical data on temperature, humidity, lighting usage, and facility activity levels. Machine learning and artificial intelligence then took on much of the implementation work. The idea behind using machine learning for energy consumption management is to detect patterns and trends. By processing historical data on past energy consumption, machine learning models can predict future energy consumption.
The most common machine learning methods for predicting energy consumption are based on sequential data measurements. To do this, data scientists use autoregressive models and deep neural networks.
Autoregressive models are well-suited for defining trends, periodicity, irregularity, and seasonality. However, applying only one autoregressive-based approach is not always sufficient. To improve forecast accuracy, data scientists use several methods. The most common complementary method is feature engineering, which helps transform raw data into features, thus specifying the task for the forecasting algorithm.
Deep neural networks are well-suited for processing large datasets and quickly finding patterns. They can be trained to automatically extract features from input data without feature engineering.
To store information from previously input data using internal memory, data scientists utilize recurrent neural networks (RNNs), which excel at traversing patterns across long sequences. RNNs with recurrent connections can read input data and simultaneously transfer data across neurons. This helps in understanding temporal dependencies, defining patterns from past observations, and linking them to future predictions. Furthermore, RNNs can dynamically learn to define which input information is valuable and quickly change the context as needed.
Therefore, by leveraging machine learning and artificial intelligence, manufacturers can estimate energy bills, understand how energy is consumed, and make the optimization process more data-driven.
VI. Cognitive Supply Chain Driven by Artificial Intelligence and Machine Learning
When you realize the speed at which data volume grows alongside the Internet of Things, it becomes clear that a smart supply chain is simply a matter of choosing the right solutions.
Artificial intelligence and machine learning not only automate supply chain management but also enable cognitive management. Supply chain management systems based on machine learning algorithms can automatically analyze data such as material inventory, inbound shipments, work-in-process, market trends, consumer sentiment, and weather forecasts. Therefore, they are able to define optimal solutions and make data-driven decisions.
The entire cognitive supply chain management system may involve the following functions:
Demand forecasting. By applying time series analysis, functional engineering, and NLP techniques, machine learning predictive models can analyze customer behavior patterns and trends. Therefore, manufacturers can rely on data-driven forecasts to design new products and optimize logistics and manufacturing processes.
Adidas' demand forecasting system perfectly illustrates how machine learning algorithms can impact customer experience. By analyzing trends in purchasing behavior and involving consumers in product design, the company has significantly optimized its manufacturing and delivery processes.
Transportation optimization. Machine learning and deep learning algorithms can be used to evaluate transportation and deliverables and determine their impact on performance.
Logistics route optimization. General ML algorithms examine all possible routes and define the fastest route.
Warehouse control. Deep learning-based computer vision systems can detect inventory shortages and excesses, thereby optimizing timely replenishment.
An example of an intelligent inventory management system is the computer vision-based tracking technology integrated by Tyson Foods. By leveraging edge computing, cameras, and machine learning algorithms, the system can track the quantity of chicken passing through the supply chain.
Human resource planning. When machine learning algorithms collect and process production data, they can show how many employees are needed to perform certain tasks.
Supply chain security. Machine learning algorithms analyze data about request information: who needs it, where, and what information is required, and assess risk factors. Therefore, a cognitive supply chain can ensure data privacy and prevent hacking.
End-to-end transparency. Advanced IoT data analytics based on machine learning processes data received from IoT devices. Machine learning algorithms can uncover hidden interconnections between multiple processes in the supply chain and identify vulnerabilities requiring immediate response. Therefore, everyone involved in the supply chain operations can request the necessary information if needed.
Finally, the future of artificial intelligence in manufacturing is bright. A PwC report indicates that AI technology in manufacturing is expected to grow rapidly over the next five years.
