The Difference Between the Internet of Things and Machine-Based Learning
Both machine learning and the IoT are revolutionizing industries worldwide, driving efficiency, business growth and technological advancements. In this guide, we’ll delve into the specifics of IoT and machine learning, highlighting their unique characteristics and how they differentiate from each other.
What is Internet of Things (IoT)?
The Internet of Things refers to a network of interconnected physical objects, commonly known as “things”. These objects incorporate sensors and connectivity tools to collect and exchange data over the internet. IoT has gained prominence in various aspects of everyday life, such as smart homes utilizing energy-saving devices like smart meters, lights, plugs and enhanced security systems like smart doorbells. Additionally, IoT has found widespread use in additive manufacturing, significantly enhancing production efficiency.
How is IoT Used in Additive Manufacturing?
IoT finds extensive application in additive manufacturing across diverse sectors, including aerospace, automotive, defense, energy, medical and more.
For instance, in the automotive industry, IoT plays a vital role in additive manufacturing processes. Sensors integrated into manufacturing equipment can alert operators about filament replacement requirements or serve as real-time quality control mechanisms. When fabricating a 3D printed component like a dashboard vent, IoT sensors can measure critical factors influencing the end product’s quality, such as temperature, humidity, material flow and printer performance. Analyzing this data helps identify patterns associated with defective prints, leading to reduced defects and improved overall quality.
What is Machine Learning (ML)?
Machine Learning is a form of artificial intelligence that enables computers to automatically learn and adapt without explicit instructions. By utilizing algorithms and statistical models, machine learning analyzes patterns and behaviours in data to derive insights and make informed decisions.
How is Machine Learning Used in Additive Manufacturing?
Machine learning has proven instrumental in optimizing additive manufacturing processes. For example, in the aerospace industry, machine learning techniques are employed to detect potential maintenance requirements in 3D printers, effectively reducing potential downtime. Additionally, machine learning models analyze data from non-destructive testing methods to identify defects that may not be discernible to the human eye, ensuring the highest quality standards in spacecraft manufacturing.
The difference between the Internet of Things and machine-based learning
While the Internet of Things and machine-based learning are two separate concepts, they often intersect and complement each other in various industries.
The IoT focuses on device connectivity and data exchange, whereas machine learning involves developing algorithms that enable computers to learn from data and make predictions without explicit programming.
IoT systems collect and transmit data to centralized platforms or cloud-based systems, while machine learning algorithms utilize the data to make predictions or decisions.
Machine learning models can continuously improve their performance through exposure to historical data or labelled data with known outcomes.
In essence, IoT emphasizes connectivity and data exchange, while machine learning focuses on leveraging data to make intelligent and autonomous decisions.
How IoT and Machine Learning work together in additive manufacturing
In additive manufacturing, IoT and Machine Learning can be utilized together, to enhance efficiency and quality control. By incorporating IoT sensors into 3D printers, data can be collected and transmitted into central systems or cloud platforms. Machine learning models can then analyze the data to continuously improve the effectiveness of 3D printers. Feedback from IoT devices helps to refine processes, adjust machine settings and drive overall optimization.
How can Alexander Daniels Global Help?
At Alexander Daniels Global, we employ targeted headhunting and direct sourcing techniques to reach both inactive candidates and tap into our extensive network of active candidates. Our specialist team can seamlessly handle your recruitment needs in the additive and advanced manufacturing industry, including IoT recruitment, ensuring you meet the right candidates at the right time.
If you’re a professional in the additive manufacturing industry or advanced manufacturing field, explore our career portal to apply for a range of job vacancies.
To learn more about our services and how we can assist you, get in touch with our team of experts today. We also offer a wealth of HR resources and insights including our annual salary survey to help you stay informed about the latest hiring trends.
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