Sensing stands out as a fundamental technology for the Internet of Things (IoT). Sensing and wireless data communication using the Internet of Things are the technologies required to advance data usage in the manufacturing industry.
In this article, we will explain the meaning of Sensing, its types and areas of use, the benefits of introducing it, common problems in data collection and how they can be solved, and usage examples in the manufacturing industry.
What is Sensing?
Sensing is a technology that uses detectors called sensors to measure objects and obtain quantitative information.
Many sensors around us replace human senses.
Sensing technology can be generally divided into two categories:
Smart Sensing A technology that measures and detects by placing sensors near objects. Remote Sensing A technology that allows obtaining quantitative information without touching an object.
Sensing technology has already started to be used in all sectors and is used to control devices according to the information obtained, analyze information, and transform it into high-value-added information. The use of the above-mentioned intelligent sensing technology in the manufacturing industry is considered effective, and many companies are making progress in introducing this technology.
Types of Sensing Technology
The following are common sensor types and application examples.
| Image Sensor (Image Sensor) | A sensor that measures the properties of an object based on images captured by a camera. Used to perform external inspections using cameras and to understand the status of workers and equipment. |
| Laser sensor | A sensor that detects objects by emitting laser light. It is used to measure the distance and dimensions of an object, such as width, thickness, and height. |
| Acceleration Sensor | It is a sensor that measures acceleration, that is, the change in speed per unit of time. It is used to measure the amount of movement of objects and detect impacts. |
| Air volume sensor | A sensor that measures the amount and speed of airflow. It is used in the maintenance of air conditioning equipment and devices. Vibration sensor A sensor that measures the vibration of objects. It is used to detect abnormal vibrations in equipment for the early detection of malfunctions and damage. |
| Water level sensor | A sensor that measures the height of the liquid surface. It is used to determine the amount of liquid remaining in a tank or container. |
| A Current sensor | A sensor that measures the current in a circuit. It is used to detect the operating status of equipment and to detect the status of motors that require current control. |
| Temperature sensor | A sensor that measures the temperature of an object or location. It is used to detect abnormal temperature changes in equipment and to control products and materials at the appropriate temperature. |
| Vibration sensor | A sensor that measures the vibration of objects. It is used to detect abnormal vibrations in equipment for early detection of failures and damage. |
Benefits of applying sensing technology in the manufacturing industry
Next, we will introduce the benefits of applying sensing technology in the manufacturing industry.
Improving the level of quality control
Sensing technology can capture information with higher accuracy than human senses. Through visual inspection, automatic inspection, and reduction of human errors, defects that are difficult to detect can improve quality. In addition, by measuring and analyzing the processing conditions of the equipment, it is possible to detect problems during processing and use them to improve quality.
In addition, sensing technology is effective in ensuring traceability. Keeping records of production conditions and quality on paper is time-consuming, but by collecting the information captured by sensing technology as data, you can quickly access the information you need.
Improve equipment maintenance efficiency.
Many manufacturing facilities conduct human patrols and inspections to understand the operating status of the equipment. It would take hours for maintenance personnel to patrol the factory and conduct visual or manual inspections, but it is possible to save labor by using sensing technology.
By installing sensors on equipment to collect information such as temperature and current, and establishing a system that can monitor the equipment remotely, maintenance personnel will be able to quickly detect and intervene in problems. In addition, in recent years, it has become possible to prevent equipment problems and minimize damage by using artificial intelligence for predictive maintenance and failure prediction.
Better productivity
Using sensing technology, it is possible to know all kinds of information such as work progress, equipment operating status, and product and parts location in real-time. It is possible to increase productivity by quickly detecting and responding to production delays, reducing material stagnation, and shortening delivery times.
Another advantage is that the “picture” of the production site shows where improvements are needed. Increasing production efficiency through targeted improvement activities will lead to improvements in quality, cost,t, and delivery.
Data collection challenges and solutions in the manufacturing sector
While the country is actively promoting DX (Digital Transformation), there is a need to introduce the best tools such as artificial intelligence and production management systems in the manufacturing industry. Collecting data using sensing technology is essential to effectively use these tools, but there are several data collection issues that hinder DX in the manufacturing industry.
Here we will explain the difficulties of data collection in the manufacturing industry and how to solve them.
I don’t know how to collect data.
For example, even if they want to collect data directly from devices and use it for production management, etc., we often hear that existing devices are too old to collect data. In fact, in manufacturing plants in Japan, there are many cases where devices from 10 to 20 years ago are still actively used and it is difficult to collect data from such devices, which limits the adoption of IoT. The data collection function may be standard in new devices, but many people may wonder how to collect data from existing old devices.
There are also cases where the method of collecting data directly from the device is a black box and can only be understood by the device manufacturer. Especially in small and medium-sized enterprises, there are very few production engineers who can handle the maintenance of the equipment, so they leave this job to the manufacturer or vendor and do not know the technical specifications themselves.
The recommended course of action for companies experiencing these problems is to remanufacture the sensor. There are many sensors available today that can be retrofitted to existing devices, making it possible to create a system that will collect and use the necessary data even from old devices. Since you can collect data without having to contact the device manufacturer every time, you can proceed as you wish with IoT and DX.
I don’t know what data to collect.
In manufacturing facilities, there are many targets for data collection, such as workers and equipment, and the types of data are also diverse. If you try to collect such a large amount of data, you will not only spend a lot of money, but you will also not be able to use the data you collect as you should.
In addition to the type of data, you should also pay attention to the level of detail. For example, when measuring temperature, the required accuracy of the sensor depends on whether the temperature is measured in units of 1°C or 0.01°C. It is understandable that you want to collect more detailed data, but collecting too much data that does not serve your purpose will only increase your costs.
Therefore, when collecting data using sensing technology, it is important to choose the type and level of detail of the data collected according to the purpose and use of the company, rather than blindly collecting data. However, there are still very few companies that know what kind of data they need to collect and at what level of detail. We recommend consulting an external vendor with knowledge of sensing technology and the Internet of Things and gradually collecting data as you go through the PDCA cycle.
Examples of sensing initiatives in the manufacturing industry.
Nakayo Co., Ltd., which designs and manufactures information and communication devices such as telephones, is actively using sensing technology in its production facilities and has achieved outstanding results.
Here, we will introduce examples of sensing measures at Nakayo.
1. “Snapshot” of equipment start-up time
The installation of sensors to understand the operating status of molding machines showed that it took up to 90 minutes for all molding machines to start after the job started.
According to these results, when we analyzed the current situation, we encountered problems such as the equipment start-up time varying according to the person or group, the temperature of the molding machine heater being slow to gr, ow, and taking time to start. As an improvement measure, we established a procedure guide for starting the equipment and used a timer to eliminate waiting for the heater to warm up, which reduced the time before starting the process by 75%. was very successful.
2. Monitoring the amount of remaining molding material
The materials used for resin molding should be dry enough to prevent molding defects. It is important to put the materials in a special dryer in advance and dry them, but we found that if we forget to fill the materials, we have to wait for the materials to dry.
As an improvement, we installed a sensor and alarm device in the dryer to detect the amount of remaining material and established a system to prevent forgetting to fill the material. As a result, we managed to reduce the downtime caused by the drying of the material by 100%.
3. Fan abnormality detection
Most molding machines are equipped with cooling fans to prevent the equipment from overheating. While monitoring the air volume by installing an air volume sensor on the cooling fan, the air volume suddenly dropped and the alarm started to sound.
When we examined the cooling fan, we found that the airflow was poor due to dirt and bearing damage, so we cleaned and replaced the bearings. As a result, the cooling fan air volume was restored, and we were able to prevent major repair costs and long-term equipment outages due to malfunctions.
4. Daily inspection and symptom monitoring
To prevent defects caused by dirt in the cleaning fluid, washing machines for sheet parts should be replaced regularly. By attaching a conductivity meter to a washing machine and looking at data on liquid contamination, such as conductivity and hydrogen concentration ion index, it is now possible to determine the appropriate time for a liquid change.
By changing the liquid according to visual results, we were able to increase the replacement cycle by an average of 1.3 months per year and successfully reduce the cost of waste liquid treatment by 23%.
Summary
Sensing and wireless data communication using the Internet of Things are key technologies that are driving data-enabled DX for the manufacturing industry. As seen in the sensing initiatives mentioned above, companies that actively use sensing technology are improving efficiency, and quality, and making equipment maintenance more efficient.
Factory NYC (Nice), an IoT system for the manufacturing industry by Otsuka Shokai, can collect data from existing equipment by upgrading sensors regardless of the equipment model or year. The collected data can also be “visualized” using graphs and used for analysis and improvement. If you are interested, please feel free to contact us.