Industry 4.0 has revolutionized the way manufacturing companies operate, enabling them to deliver high-quality products with optimized factory processes. Through the Internet of Things (IoT) technology, industry 4.0 has enabled intercommunication between manufacturing equipment, factory workers, and computer systems. With the incorporation of Machine Learning (ML) into large production data volumes, the manufacturing industry has further improved its production processes. This phenomenon has led to the creation of smart manufacturing systems that can make better-informed decisions, driving the business to achieve its goals with enhanced operational efficiency, productivity, and safety.
In this article, we’ll review the application of machine learning in manufacturing facilities and how it can boost your production processes and overall business position in the industry.
Applications of machine learning in the manufacturing industry
The manufacturing industry largely benefits from different machine learning applications. Here are some common ways machine learning is used in the manufacturing industry worldwide.
Manufacturing equipment maintenance is one of the largest expenses in manufacturing companies. Even in an unexpected downtime of a single machine, the entire production line can halt, costing the business billions of dollars. Resultantly, predictive maintenance has become a critical process every manufacturing process should incorporate into their business.
Predictive maintenance is the maintenance of the equipment before it fails, based on data and analytics. Manufacturing companies use complex machine learning models to get forecasts about equipment conditions. For example, regression models provide the equipment's Remaining Useful Life (RUL) or how much time the equipment has until it can fail. Anomaly detection algorithms help detect unusual behavioral patterns of equipment and classification models for failure prediction.
Predictive maintenance is achieved by sensors placed on equipment which collect data about the equipment's performance and condition. Then machine learning algorithms use these data to predict when maintenance will be required.
There are many benefits of predictive maintenance for manufacturing companies. Predictive maintenance increases equipment uptime by 10 to 20% while reducing overall maintenance costs by 5 to 10% and maintenance planning time by 20 to 50%. Also, companies can improve equipment reliability by performing maintenance before a failure occurs.
Not only the processes but also machine learning is being used in manufacturing to improve different products using generative design. Generative design is the process of optimizing product design to solve engineering challenges. Engineers can use machine learning models to generate different product models by defining their required parameters, like weight, height, material, durability, shape, and strength options. Then machine learning can be used to determine the most optimized design choice by iterating through these options. Deep Learning, Genetic algorithms, and reinforcement learning are the three types of machine learning used in this process.
Using the generative design, General Motors found an optimized seat bracket design lighter and stronger than the original, reducing its vehicle weight. BMW, in 2022, introduced a real color-changing body surface with BMW iX Flow. Generative design has been used to “reflect the characteristic contours of the vehicle and the resulting variations in light and shadow.” The Airplane manufacturer Airbus also applied generative design to reduce the weight of an interior partition of the A320 aircraft
Supply Chain Management
Supply chain management is a critical part of manufacturing, where machine learning is widely used to optimize related tasks. Inventory management, warehouse management, and logistics route optimization are the main applications of machine learning in supply chains.
In warehouse inventory management, machine learning models help reduce the under or over-stocking of materials by forecasting the demand. Using computer vision (CV), items can be classified and detect any damages. machine learning also automates manual tasks inside warehouses. For instance, CV systems help predict blockages in conveyor belts, Optical Character Recognition (OCR) and Natural Language Processing (NLP) help automatically detect package arrivals, scan barcodes, and change delivery status. Also, programmed autonomous vehicles and robots help in tasks like help transportation of goods and loading and unloading boxes.
In logistics and transportation, machine learning models are applied to track the location of goods when they are being transported. Sensors are being used to detect the transport conditions of packages, like temperature and humidity. Also, ML models help optimize logistics routes by tracking road conditions and recommending the optimized and cost-effective route to transport the packages.
Using digital twin to optimize manufacturing processes
A digital twin is a virtual representation of a physical object or system that enables you to simulate and analyze its performance in a virtual setting. Sensors, cloud storage and machine learning are the major components of a digital twin. Advanced digital twin utilization uses digital twins to optimize and improve the efficiency of manufacturing processes.
Manufacturers can use digital twins to get information about equipment or product performance using advanced machine learning algorithms. These data can then assess product quality before producing and releasing it to the market and improve product design and development. Digital twins are also being used as prototypes to build and test innovative products without a lower cost to the manufacturers. Also, manufacturing companies can use digital twins to monitor machines and equipment conditions and detect any errors or faults to avoid downtimes.
Forecast energy consumption
Energy consumption in machinery is a challenge for manufacturing plants as higher energy consumption can be costly for the business. Machine learning models help forecast the energy consumption of machinery using patterns from the raw data.
Deep Learning models, for example, can automatically extract features and quickly detect patterns of energy consumption. Other machine learning models used for this application include Autoregressive models that can detect parameters of energy consumption and Neural networks like Recurrent neural networks (RNN), Attention-based neural networks, and (Long short-term memory (LSTM) to store previously used energy consumption data.
One of the deciding factors of customer satisfaction is product quality. Thus, it is extremely critical to control product quality during manufacturing. Machine Learning plays a critical role in improving the quality of the manufacturing process. Machine learning models combined with real-time video processing and optical cameras can help monitor the quality and defects of assembly equipment and products. Digital twins also help assess the quality before manufacturing and launching the product.
How machine learning improves your production processes and overall business position?
● Reduce the time to market a product
For example, digital twins are used to assess product performance and quality before producing it real. This enables manufacturing companies to reduce the time to market a product.
● Reduce production costs
Through lowering maintenance costs, better supply chain management, reducing wastages, and optimizing manufacturing processes, manufacturing companies can reduce overall production costs.
● Improving operating efficiency
ML models in supply chains, for example, help automate and optimize most processes, thereby improving operational efficiency.
● Promote innovation
Applications like generative design and digital designs help manufacturers go beyond human imagination and generate new innovative ideas that can gain competitive advantage.
● Enhanced supply chain management
With machine learning models for, Inventory management, warehouse management, and logistics route optimization, the factories can ensure adequate materials are available on time. This helps them to complete customer orders on time and improves customer satisfaction.
● Enhance employee safety
By proactively detecting faulty machinery, the machine learning model help reduces accidents that they can cause, thereby improving the safety of factory workers.