In this ever-evolving landscape, we embark on a journey to explore the boundless possibilities that machine learning brings to the manufacturing industry. Have you ever wondered how products are flawlessly assembled, defects detected with uncanny accuracy, and production cycles optimized for peak efficiency?
The answer lies in the groundbreaking applications of Machine Learning in manufacturing. In this article, we will delve into some of the ways machine learning has revolutionized this industry.
Are you ready? Let’s jump in!
How to Get Started with Machine Learning
The Importance of Machine Learning
Before beginning, we can take a look at some impressive numbers below according to a Forbes statistic that shows:
- 43% of enterprises say their artificial intelligence and machine learning initiatives matter “more than we thought.” One in four said artificial intelligence and ML should have been their top priority sooner.
- 50% of enterprises plan to spend more on artificial intelligence and machine learning.
- 56% of enterprises today rank governance, security, and auditability issues as their highest-priority concerns.
- 83% of IT leaders say artificial intelligence and machine learning are transforming customer engagement, and 69% say it is changing their businesses.
- The global machine learning market is projected to grow from $7.3B in 2020 to $30.6B in 2024, attaining a CAGR of 43%.
- In just over a third of enterprises surveyed (38%), data scientists spend more than 50% of their time on model deployment.
Such figures somewhat allow us to realize the importance and necessary development of machine learning. It’s no surprise to know that manufacturers worldwide are investing in machine learning solutions to improve their processes. So, how to get started with machine learning?
How to Kick Off Machine Learning
To start with ML, embark on this exciting journey as follow.
- Understand the Fundamentals: Grasp the core concepts of machine learning, where computers learn from data to make predictions without explicit programming. You can learn key terms like data, features, labels, and models through books and tutorials.
- Choose Python: Select Python as your programming language thanks to its simplicity and extensive ML libraries like TensorFlow and sci-kit-learn.
- Set up your environment: Install Python and necessary libraries, considering Anaconda for easy setup.
- Explore datasets: Prepare raw data, analyze, clean for appropriate data, process data, and visualize data to gain insights before using ML algorithms.
- Start with simple algorithms: Straightforward algorithms like Linear Regression or k-Nearest Neighbors (k-NN) will help you understand ML workflow and interpret results easily.
- Evaluate models: Learn to assess model performance using accuracy, precision, recall, and F1-score metrics. Optimize models with hyperparameter tuning.
- Experiment with Deep Learning (Optional): Explore neural networks for complex tasks like image and speech recognition. Use libraries like Keras and PyTorch for ease of use.
- Join ML communities: Connect with other learners and ML enthusiasts through online forums and meetups. Collaborating accelerates learning and keeps you motivated.
Applications of Machine Learning in Manufacturing
Supply Chain Optimization
A seamless and efficient supply chain is essential for success in today’s manufacturing industry. Manufacturers rely heavily on suppliers to deliver high-quality materials promptly, making supply chain optimization a top priority.
The most common way to optimize every part of the factory is to use sequential data measurements. It can be analyzed by machine learning algorithms with autoregressive models and deep neural networks. Artificial intelligence and ML technologies enable manufacturers to experience a transformative shift toward a cognitive supply chain management.
Cognitive supply chain management enhances:
- Demand Forecasting: Understanding customer behavior and preferences using time series analysis, feature engineering, and natural language processing (NLP).
- Warehouse Control: Ensuring quick good replenishment through stock control with advanced computer vision systems based on deep learning.
- Logistics and Transport Optimization: Allocating the most efficient shipping and transportation routes.
Quality Control & Recognize Defects
Traditionally, manufacturers have used quality control systems to ensure their products meet essential standards. This involves inspecting a sample of products at the end of the production process to identify faults or non-conformities.
However, this approach doesn’t prevent problems from occurring in the first place. It only detects them after they’ve happened. Manual inspection for each product can be time-consuming, expensive, and prone to human errors, especially for large-scale businesses.
Meanwhile, data and analytics permit producers to anticipate and prevent quality issues before they arise. You can achieve that by analyzing raw data from various sources like sensor readings, machine logs, and cross-check inspections.
Manufacturing machine learning can identify patterns and anomalies that signal potential future quality issues. This proactive approach enables them to take preemptive action and avoid production problems.
The main advantage of using ML to control the quality is its ability to learn from historical and static data. Instead of relying on a library of possible defects, the algorithms only need samples of good products during training. Developers then create algorithms that specifically compare samples to the most common types of defects, improving defect recognition accuracy.
Improving Product Design
Generative design, a powerful application of machine learning, does a great job of improving product design in the manufacturing sector. It utilizes algorithms to optimize product designs, such as automobiles, electronic devices, toys, etc. Machine learning investigates all conceivable configurations to identify the optimum design after being given data and a desired aim.
ML algorithms can be trained to optimize weight, shape, durability, cost, strength, and aesthetics. Three common approaches to generative design include reinforcement learning, deep learning, and genetic algorithms.
A practical example of generative design is BMW’s BMW iX Flow, which was showcased at CES 2022. The car features a unique e-ink wrap that can change color, creating shades of black and white. This innovative design was actualized through generative design processes, ensuring the vehicle segments reflect characteristic contours, resulting in captivating variations in light and shadow.
Digital Twins Utilization
Digital twin refers to a virtual recreation of the production process based on data from IoT sensors and real-time data. It means creating a digital replica of a machine, process, or entire factory. This twin operates in real time and receives data from sensors installed on the physical counterpart.
Implementing digital twin utilization in manufacturing with machine learning requires close collaboration between data scientists, engineers, and domain experts. Accurate data collection, model training, and constant updating are significant for successful deployment. Manufacturers can achieve increased efficiency, reduced costs, and enhanced product quality, giving them a competitive edge in the market.
Lower Production Costs
Embracing machine learning in manufacturing offers a powerful solution to enhance productivity and bolster financial performance. It achieves this through data analytics and predictive maintenance, optimizing production processes to enhance efficiency and cut expenses.
The predictive maintenance method will help drive cost reduction. Machine learning algorithms analyze sensor data from machinery, predicting maintenance needs in advance. This proactive strategy lets manufacturers avoid unplanned downtime and costly breakdowns, leading to substantial savings in repair and replacement costs.
Energy Consumption Prediction
Energy consumption prediction using machine learning assists manufacturers in accurately forecasting their energy usage.
Machine learning algorithm analyzes historical data and real-time sensor information to predict future energy requirements. Besides, it stimulates predictive maintenance by scheduling timely maintenance and reducing unnecessary downtime.
Energy consumption should be considered at every production stage for maximum efficiency. To achieve this, we use:
- Proactive energy management: Machine learning technologies analyze energy consumption patterns, identifying trends and seasonal variations. Manufacturers can adjust production schedules and energy usage based on predicted higher or lower energy demand.
- Integration with renewable energy sources: Manufacturers are increasingly adopting renewable energy sources like solar panels and wind turbines to reduce their carbon footprint. This encourages manufacturers to maximize clean energy use when it’s abundant and switch to conventional sources.
- Real-time energy monitoring: ML boosts real-time monitoring of energy consumption patterns using smart sensors and IoT devices. Manufacturers will receive instant alerts when energy consumption deviates from expected patterns, allowing prompt corrective actions to prevent energy wastage and unexpected spikes.
- Predictive maintenance for energy efficiency: ML models predict the optimal time for maintenance based on energy consumption patterns and equipment performance data. Timely maintenance ensures machinery operates at peak efficiency, minimizing energy wastage and extending equipment lifespan.
Successful Implementation of ML in Manufacturing
These real cases have successfully applied machine learning in the manufacturing industry.
General Electric (GE) – Predictive Maintenance in Aviation
General Electric, a multinational conglomerate, has effectively used machine learning to enhance aircraft maintenance. GE developed a predictive maintenance system with machine learning algorithms by utilizing sensor data from their aircraft engines.
This system analyzes real-time data, including temperature, pressure, and vibration, to predict potential failures before they occur. As a result, airlines have experienced significant cost savings by reducing unscheduled maintenance and minimizing downtime.
Siemens – Quality Control with Machine Vision
Siemens, a global technology company, improved quality control in their manufacturing processes through machine learning-based vision systems. In one case study, Siemens integrated machine vision cameras with ML algorithms into their production lines to automatically inspect and detect defects. This approach enhanced the accuracy and speed of quality checks, reducing the number of defective items and ensuring higher customer satisfaction.
Mercedes-Benz – Autonomous Driving with Machine Learning
Mercedes-Benz, a renowned automobile manufacturer, has been at the forefront of integrating machine learning into its vehicles’ autonomous driving capabilities.
They successfully developed advanced driver-assistance systems (ADAS) that analyze real-time sensor data from cameras, radars, and lidar. These algorithms enable the vehicles to accurately perceive their surroundings, identify obstacles, and make decisions to navigate safely and efficiently.
One specific area where Mercedes has applied machine learning is in developing their “Intelligent Drive” system. This system relies on deep learning models to continually improve its ability to detect and respond to various road conditions and potential hazards. Over time, with more data collected from their vehicles on the road, the ML algorithms iteratively update and enhance their performance, ensuring a safer and more reliable driving experience.
Toyota – Optimizing Production with Machine Learning Technologies
Toyota, one of the world’s largest automotive manufacturers, harnessed the power of ML to optimize its production processes.
It created ML algorithms that precisely estimate demand by examining previous production data and supply chain data. Businesses will efficiently change production schedules, lower inventory costs, and better satisfy consumer requests, increasing total productivity.
Ready to Apply Machine Learning in Manufacturing?
The fantastic applications of machine learning have forever changed the global smart manufacturing market. This transformative technology has given new life to factories, making them intelligent and efficient. Machine learning has shown its worth in enhancing productivity and profitability by predicting equipment failures and improving product quality.
If you’re looking for an AI partner to build machine learning systems to transform your manufacturing, we’re here to help. Neurond makes use of comprehensive machine-learning models and MLOps service to help you rapidly make critical and informed business decisions based on trusted insights.