The manufacturing process is a complex, multidimensional, and challenging task. From procuring raw materials or components and organizing your factory’s staff to maintaining machinery, equipment, and tools, you must ensure all manufacturing elements operate in sync.
With today’s technological evolution, a manufacturing process can be significantly simplified and improved for maximum results.
In this article, we will talk about the ways you can optimize your manufacturing process with the help of the Internet of Things (IoT) and turn your production plant into a “smart” factory.
Let’s cover some basics first!
What is IoT?
IoT is a system of interconnected physical objects that rely on sensors, software, and other technologies to connect and share data with other devices and systems over the Internet.
What is the Industrial Internet of Things (IIoT)?
IIoT refers to interconnected smart devices that are typically in the automobile industry, logistics and transport, retail, and for optimizing the manufacturing process.
The main difference between IoT and IIoT is in their application. While IoT is focused on consumer usage in general, IIoT is more oriented toward optimizing industrial processes.
Now that you know what IoT is, let’s see how you can use it to improve your manufacturing process.
Optimizing the manufacturing process with IoT
IoT applications in manufacturing include the following:
- Implementing smart sensors
- Setting machine-to-machine communication
- Using machine learning algorithms
- Utilizing cloud computing technologies
- Performing big data analytics
Let’s examine these IoT components in manufacturing in more detail.
Implementing smart sensors
Smart sensors enable real-time monitoring of different production processes. The four key sensor types used in production are:
1. Level sensors
Level sensors monitor, maintain, and measure liquid (sometimes even solid) levels in containers. Once these sensors detect liquid levels, they convert the gathered data into electric signals.
We can differentiate between point-level sensors and continuous-level sensors. The first is designed to indicate if the liquid has reached a particular point in a container, while the second one is used for rendering precise liquid level measurements.
We can further divide level sensors into invasive and non-contact sensors. Invasive sensors have direct contact with the measured substance, while non-contact sensors operate on sound or microwaves.
Level sensors can help you detect any liquid leakage in real-time and provide you with accurate control of the liquid level in your containers. This is why level sensors are crucial for the safety of your industrial processes.
2. Temperature sensors
Monitoring temperature is crucial in many manufacturing processes, as many industrial applications require accurate and constant temperature control. There are many sensors in this category, but the five most commonly used in manufacturing are:
- The resistance temperature detector is a thin film device with great stability and accuracy made of platinum.
- Thermistors are accurate, cost-effective sensors that can be divided into two types –NTC (measuring negative temperature coefficient) and PTC (measuring positive temperature coefficient).
- Thermostats use two conductions that are mechanically joined together. Upon exposure to various temperatures, these conductors expand and contract at different rates. These actions create a “push” or “pull” force that is transmitted as a binary on/off signal.
- Thermocouples operate on the Seebeck effect. This effect is the occurrence in which a temperature difference between two different electrical conductors or semiconductors produces a difference in voltage between the two substances.
- IR temperature sensors operate on an array of hot and cold junctions and are used for detecting an object’s surface temperature remotely.
With precise and continuous temperature control, these sensors can help you predict and prevent potential machine failures.
3. Pressure sensors
A pressure sensor, as its name implies, measures pressure and collects real-time data about the equipment’s overall condition. The two most common pressure sensors in the manufacturing process are:
- Strain gauge sensors operate on an in-built spring element that deforms upon force application, thus using these changes in the spring’s dimensions to measure pressure.
- Piezoelectric sensors operate on the piezoelectric effect. This effect refers to the generation of an electric charge as a reaction to a material’s physical changes.
Pressure sensors can predict equipment failure patterns and help you develop a more effective maintenance strategy.
4. Proximity sensors
Proximity sensors serve to detect the presence of objects or materials and initiate a certain action or mark their existence or nonexistence. The most common types of proximity sensors are:
- Capacitive sensors detect a change in capacitance.
- Inductive sensors measure inductance changes in metal objects.
- Ultrasonic sensors detect objects with high-frequency sound waves.
Proximity sensors can help you enhance your manufacturing processes’ safety and optimize your inventory management.
5. Mass spectrometers
Mass spectrometers measure the mass and concentration of individual molecules and atoms by generating spectra. How does a spectrometer work?
An in-built lamp produces a beam of light that hits the diffraction grating. This grating then operates as a prism and separates the light into its constituent wavelengths.
After that, the light interacts with the sample and the detector measures the transmittance (the amount of light that passes completely through the sample) and the absorbance (the amount of light that is absorbed by the sample). Finally, it converts all this information into a digital display.
Mass spectrometers are mostly used for manufacturing processes in the following industries:
- Food and beverage
- Iron and steel
Spectrometers provide fast and accurate composition analysis, which can be crucial for pharmaceutical industries where potential mistakes can have massive consequences.
In general, implementing smart sensors in your manufacturing process will help you reduce operational costs, increase efficiency, and lower your replacement asset value.
Setting machine-to-machine (M2M) communication
Machine-to-machine communications aim to automate the data transmittance and measurements between electronic or mechanical devices.
In simple words, M2M is an automated data exchange between devices, that doesn’t require any human intervention.
How does M2M work? The M2M communication relays on sensors. These sensors detect the data and transmit it wirelessly onto a network, where it is then routed to a server.
The two main things you need to consider before establishing M2M communication are:
1. Network – you will need wireless connectivity so that sensors can send information directly to the place where it’s needed. There are many wireless networks to choose from, including Wi-Fi, Bluetooth, NFC, etc.
Each wireless connection has a different range and speed. For instance, a dual-band Wi-Fi router will be able to transfer data faster at 5GHz but is not a great choice if your application requires the signal to go through walls or ceilings.
2. Security – the best solution is to choose M2M devices that have inbuilt security features, but you might also want to consider implementing passwords and firewalls for additional protection.
Establishing M2M communication can help you monitor and control your manufacturing process remotely and improve the safety of your production line.
Using machine learning algorithms
Machine learning (ML) refers to a sub-field of artificial intelligence (AL) that uses software applications to predict outcomes more accurately without human intervention.
ML algorithms receive input data and predict output values. This new data is then fed to the existing algorithms, where they learn and optimize their performance to improve further operations.
When machine learning algorithms are applied to IIoT, you get a better performing, cost-efficient, and time-effective manufacturing process.
Utilizing cloud computing technologies
Cloud computing is the on-demand delivery of different services over the Internet. These services include servers, storage, software, networking, intelligence, and analytics.
Cloud-based computing will allow you to access data from anywhere at any time and will also help all your software and applications run faster and smoother.
Simply put, you will need massive storage for all the data collected from various sensors and devices. This is where you will benefit the most from cloud computing, as an on-premise data storage facility can be costly and hard to scale, while cloud storage offers greater capacity and easier scalability.
Performing big data analytics
Big data refers to a larger volume of data collected from various sensors, devices, networks, and video/audio files. This data can be structured or unstructured.
Big data analysis is the process of uncovering patterns and connections in the vast volume of data.
Big data analysis can’t be performed traditionally. Instead, you will need to use one of the software for analyzing big data. The top five tools for analyzing big data currently on the market are:
. Performing data analysis will help you improve the following manufacturing processes:
- Production optimization
- Maintenance regulation
- Quality checks
- Tool optimization
- Supply chain management
Implementing IoT into your manufacturing process will help you minimize unplanned machine downtime, boost production visibility, improve your inventory management, and build an overall safer working environment.
In the words of Kevin Ashton, the inventor of the term “The Internet of Things”: “If we had computers that know everything there was to know about things – using data they gathered without any help from us – we would be able to track and count everything and greatly reduce waste, loss, and cost.”
We hope our article helped you understand the importance of using IoT for your production processes and gave you valuable insights into what you need to do to integrate it properly.
Travis Dillard is a business consultant and an organizational psychologist based in Arlington, Texas. Passionate about marketing, social networks, and business in general. In his spare time, he writes a lot about new business strategies and digital marketing for SEO Turnover.