Artificial Intelligence (AI) technology will be even more crucial in the future. This is reflected in Gartner’s report Deliver Artificial Intelligence Business Value which contains the opinions of company officials from various industries on the utilization of technology.

According to the report, respondents consistently point to AI as the technology that has the greatest impact on the company’s operations. Utilization of AI is considered to have a more significant impact than other technologies such as APIs, Internet of Things, or Blockchain.

AI technology can also be used in a variety of industries, including manufacturing industries. This was expressed by Andhik Yudhi (IT General Manager of Toyota Motor Manufacturing Indonesia) in his capacity as a technology observer in the automotive field.

According to Andhik, the challenge of the automotive industry today is increasingly intense competition. Automotive companies are required to increase productivity in order to produce competitive products. On the other hand, productivity on the production line is now close to 100%. Therefore, technology-based approaches such as AI are important. “Because if done manually, it is no longer possible” said Andhik.

One example of AI implementation in the automotive industry is visual inspection for camshaft components. Just for information, camshaft is a component in the car engine that serves to regulate valve openings. Due to its crucial function, a camshaft must have very high quality. The quality of a camshaft is determined by the presence of defects whose size ranges in the range of micrometers.

During this time, camshaft inspections are carried out manually by trained technicians. However, this way has some limitations. For example, the manual inspection process takes about 65 seconds. Manual checks also cause eye fatigue for technicians, so they have to be replaced every two hours. Creating talent for inspection is also not easy. “It took me three months to train that kind of ability” says Andhik.

It is these limitations that are trying to be overcome with AI-based visual inspection technology. The basic concept of this solution is the the use of a camera machine that can “see” defects in a camshaft. Hopefully this machine will have the precision like a trained technician, so it can decide whether the camshaft is qualified or not.

In order for the machine to detect camshaft quality, the process stages begin by creating an automation system for visual capture. After that, it is necessary to develop ai model data to “teach” the machine in order to distinguish the camshaft that passes the standard and does not. After the learning process was deemed adequate, the machine began conducting inspections accompanied by trained technicians.

AI for Predictive Maintenance

In addition to visual inspection, AI-based initiatives can also be implemented in the predictive maintenance area. The main purpose of this initiative is to find a balance point between reactive maintenance (which poses a risk of failure) and preventive maintenance (which is high cost). With predictive maintenance, the hope will be realized zero down time with the most efficient cost.

The predictive maintenance implementation process is relatively similar to visual inspection. It’s just that getting a database for modelling data is a challenge because it requires sensor and IoT technology to capture machine condition data.

Therefore, the support of production machine providers is also urgently needed. Based on experience, Andhik mentions that production machine providers rarely provide information on what and how to capture the parameters of the condition of the machine. Therefore, Andhik wanted that at the time of purchase of the machine, the buyer must ask the seller for support to get the necessary parameters.

The predictive maintenance implementation process is relatively similar to visual inspection. It’s just that getting a database for modelling data is a challenge because it requires sensor and IoT technology to capture machine condition data.

Therefore, the support of production machine providers is also urgently needed. Based on experience, Andhik mentions that production machine providers rarely provide information on what and how to capture the parameters of the condition of the machine. Therefore, Andhik wanted that at the time of purchase of the machine, the buyer must ask the seller for support to get the necessary parameters.

Finding Utilization Areas

In addition to quality control and maintenance, the utilization of AI in the automotive industry is still wide open. The big question is how to identify business processes suitable for AI implementation.

According to Andhik, the identification process can be done looking at the areas that have the most effect on the main purpose of the company. In the automotive industry, the area is logistics and maintenance. Because the increase in productivity in the two areas will create a significant effect on the final product.

“So the big goal is efficient production system, competitive products, and customer’s smile” said Andhik.

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Gallery of Examples of Utilization of Artificial Intelligence in the Automotive Industry