**3. Results**

The research results based on field study, interviews, literature, and secondary data analysis are compiled.

### *3.1. Motor Production Line Analysis*

Approximately three years ago, the cooperation with company M was implemented. The company went to Vietnam several times to review the motor production line and check problems related to failure mode, design, and equipment. Approximately 5–8 persons come from Japan every visit, which is approximately 1–2 weeks every two to three months. Starting September 2019, the products have been manufactured through small-scale production. Thus far, the total output is approximately 200,000 motors, and considerable performance test data are available. The failure mode verification along the production line involves checking the 200,000 motors. In the production, the selection of materials is gradually adjusted, and the testing process is improved. One example is the defect

reported by a customer that was not observed in the production line testing but was observed during use. The problem was traced in the assembly press-fitting.

Status of production lines at the "SM" Motor Plant in Vietnam: In the pre-production line, there are three production lines, each of which has 3–6 workbenches. Each workbench has 1–2 operating procedures; only one person is responsible for each workbench. Table 3 summarizes the status of the first production line.



There is one coil assembly production line in the "SM" Motor Plant in Vietnam. The production line has six workbenches. Each workbench has 1–2 working procedures, and only one person is responsible for each workbench.

The motor assembly line in the "SM" motor plant in Vietnam only has one production line. The production line has seven workbenches, each of which has 1–3 operating procedures and handled by one person. The status of the motor assembly line is summarized in Table 4.

#### *3.2. Process Analysis of Smart Manufacturing Line*

1. Smart manufacturing status of production lines at the "SM" Motor Plant in Vietnam.

Sensors have been installed in the machines of the production line of the "SM" Motor Plant in Vietnam. Apart from detecting machine failure, the sensors are mainly used to detect the operator's negligence. When an operation error occurs, a warning is given, and the machine is automatically stopped to avoid operator injury and product damage. Sensors are installed to record the number of operations in the coil, commutator cutting, and spot welding. They can be used to push back the total production volume (e.g., finished products and defective products) so that the person in charge of the production line can record and check the output. In the motor performance test, the data after passing the test are supposed to be retained; however, these are not retained because of limited storage space, and those that have already been stored are not used.


**Table 4.** Status of motor assembly line production.

The production line frontliners are responsible for implementing withstand voltage test and insulation size inspection procedures. Some production lines have introduced a special model, which has an arm that can automatically put parts into the machine for testing. The workflow is a semi-automated state as personnel operate the machine. The Solen company have already introduced automatic arms in other production lines. The main plan is to automatically take the rotor to the intermediate process after rotor assembly. This can save time and allow the allocation of manpower to where it is more necessary. The company will keep the production line in a semi-automated state in the short term. At most, a small number of intelligent systems will be introduced because of cost considerations. If future orders increase or there are other influencing external factors, then there will be opportunities to plan for more work processes, such as adding more functions (e.g., robotic arms) and intelligent systems.

#### 2. What intelligent systems can be imported into the production line?

This study recommends three possible intelligent systems.

The first is an image recognition system. There are many processes in the production line that check the appearance of parts. If the image recognition system can be used to automatically check the parts, the manpower responsible for visual inspection can be reduced. At this time, however, it is not feasible to acquire an image recognition system because of the lack of background data. According to certain existing conditions and negotiations with equipment manufacturers, the company's practical experts can first install the CCTV for each part that should be identified. The images can then be stored as background information for future image recognition.

The second is the use of a robot arm. In the same production line, the arm is still used to pick up parts and operate the machine. Only the conveyor belt is employed to transfer the parts to the next workbench, and the finished product is transported through different production lines; all these processes are performed by humans. If robot arms are introduced, then a more efficient production can be achieved.

The third recommendation is the use of a big data analysis system. In the production line, there are numerous operational processes that should be tested; however, not all test data are retained. If most of the available data are retained for analysis, these can be employed to facilely identify problems and fix errors as soon as they occur.

#### **4. Discussion**

Based on secondary data (SOP and working time chart), it is found that the manpower currently used in the production line has been reduced from 50 to 25. According to the analysis of secondary data and expert interviews, the statistical table of the working time of the production line. Expert experience indicates the total working time of the original design, which was 600 seconds, and the total working time after the introduction of the intelligent system was 306.1 seconds. This also proves the efficiency of the current production line. In the future, related intelligent systems, such as big data and image recognition systems, will be introduced. It is expected that the production line manpower will be reduced to 4–5 persons; it will then become a demonstration production line of Industry 4.0 and resolve the manpower shortage in various countries.

#### 1. Implementation of intelligent systems

The introduction of systems, such as big data and image recognition, can make the production line intelligent as well as link data that are originally independent in each machine. Such a connection can achieve the machine-to-machine effect and reduce manpower. This study achieved the effectiveness of the intelligent system through the improvement of the process of the coil and motor assembly line. Based on the list in Table 3, there are currently six workbenches in the coil assembly line, and each workbench is assigned to a person to operate the machine. With the introduction of a robotic arm and an image recognition system, the number of personnel will be reduced from six to two. One personnel will be responsible for setting the winding, commutator spot welding, and cutting machines, and the other will be responsible for the second half of the pressure cleaning of parts and testing machine operation. Both workers are responsible for checking the machine at any time and immediately report any problematic situation.

Based on the list in Table 4, there are currently seven workbenches in the motor assembly line. Each workbench is assigned to a worker to operate the machine. A total of seven workers are in this production line. If robot arms, image recognition systems, and big data systems are introduced, the number of workers may be reduced to three. One person is responsible for the assembly and inspection of coils, brushes, and washers; the other is responsible for the assembly, press-fitting, and performance testing of the motor, and final visual inspection. The first two workers should check the machine at any time. If a problem is detected in the machine, this should be reported immediately. The third personnel will be responsible for packaging and shipping the finished product. In the future, it should be evaluated whether the introduction of automatic packaging machines will be useful to the entire production line.

2. Changes in process management

After the introduction of the intelligent system, the process in each production line will inevitably change and follow a systematic process. The following describes the expected changes in the processes of the coil and motor assembly lines after the introduction of the intelligent system. It is mainly divided into three parts.

(1) Robot arm

The main purposes of introducing a robot arm is to change the original process where humans get the parts and put them into the machine. Moreover, with the robot arm, the number of machine operators will be reduced. The following will explain the function of the two production lines after the robot arm is introduced and replaces manpower. The process sequence is as follows. Put the rotor in the winding machine, the coil in the spot welder, the commutator in the cutting machine, the bolt gauge tool on the cut commutator, and the washer in the test machine, bearing, and coil. Put the stamping machines and bearings into the gap inspection machines. In Figure 5, the processing sequence of the part is as follows. Put the coil into the machine, the brush into the machine for assembly, the brush into the machine to assemble the washer, the motor into the machine for stamping, the motor into the gap machine, and the motor into the performance inspection machine.

**Figure 5.** Coil assembly intelligent line.

At present, to replace manpower, robotic arms may be introduced only to the robotic assembly line. Taking parts from the coil assembly line to the motor assembly line is labor-intensive. Future research may start on investigating the delivery of parts among the production lines.

## (2) Image recognition

The main purpose of introducing image recognition is to replace the original process where humans visually inspect parts, thereby increasing efficiency while maintaining product quality. After image recognition is introduced to the two production lines, the intelligent system will automatically perform visual inspection. The process sequence of visual inspection is as follows: the appearance of the rotor before and after winding, the appearance of the commutator spot welding, the appearance of the commutator cutting, the appearance of the cleanliness of the commutator pressure, and the appearance of the rear assembly of the bearing. In Figure 6, according to the sequence, the fitting conditions of the washer and iron frame are checked, the appearance of brushes is inspected, the appearance of rear parts assembled by the washer is checked, the eight gaps of the parts are visually inspected, and the final appearance is checked. Image recognition allows the machine to accurately identify people and

things in an image faster and more efficiently than humans. It also further improves the product quality and manufacturing efficiency of the industry.

**Figure 6.** Motor assembly intelligent line.

### (3) Big data analysis

The image recognition system includes big data analysis. In addition to the identification of parts based on the original images provided, it can also perform image data analysis. It can automatically capture, analyze, classify, and understand useful information from a single image or a series of images. The images provide information to achieve better productivity and quality.

The motor assembly line is shown in Figure 6. In the motor performance inspection process, the inspection machine is provided by a partner manufacturer. A total of 88 motor performance data points are tested, and the information on a product that passed the inspection is retained. According to the opinions of the three practical experts interviewed, these data are only stored and not used for any purpose. In the future, these 88 data points can be studied and analyzed. Models can also be created to improve the production line (e.g., increasing production capacity or identifying process problems and optimizations).

#### **5. Conclusions**

In this study, it is demonstrated that the implementation of intelligent systems (i.e., robotic arms, image recognition, and big data analysis) can aid the coil and motor assembly lines; it also has a practical reference value. Firstly, the introduction of a big data analysis system is reported. In the motor performance testing part of the motor assembly line, big data analysis is introduced to aid the motor production line by collecting 88 motor data points generated by the machine. The motor coil assembly production line and image recognition system included in the motor assembly line introduce big data analysis, and the system then makes judgments based on the pre-set images. Subsequently, the system classifies and analyzes the acquired images to allow the image recognition system to make more accurate and quick judgments or even identify the defects that may cause motor problems. According to the research argument of Mayr et al. [10], the Industry 4.0 technology can significantly optimize motor production, particularly with the use of big data analysis. It has considerable potential and can be used in a wide range of motor production. The conclusion (i.e., the introduction of big data analysis into the motor production line) is found to be consistent. Secondly, Mayr et al. [10] only reported that the introduction of an intelligent system impacts the motor production, but the changes in the production process were not discussed. This study therefore proposes a change in the intelligent system for the production process. Moreover, a robotic arm and an image recognition system are introduced. As a result, manpower is reduced, and the operation process is optimized.

Finally, Mayr et al. [10] theoretically confirmed that the Industry 4.0 technology can optimize motor production but did not discuss practicality. This means that the introduction of the technology may cause practical impossibility. Through interviews conducted with practical experts, this research evaluates the theoretical results obtained by literature analysis as well as the possibility of implementation based on the inputs of practical experts. Among the many Industry 4.0 technologies, the robotic arm, image recognition, and big data analysis are obtained. These three techniques can be practically used in the company's production line.

As a result of this research, the "SM" production line can be introduced with a robotic arm and image recognition system to make the current production line more intelligent. Research question 1 has been answered. The research question 2 is also answered through the research method. The coil assembly line can be turned into an automated production line when using an intelligent system.

#### *Limitations of Study*

This study only investigates and proves the feasibility of the introduction of intelligent systems to the coil and motor assembly lines of the Solen company. The three intelligent systems that can be used in the steps of the motor production line are proposed; some parts, however, have not yet been explored. Firstly, 88 big data points are not analyzed. In the future, the correlation between the values and 88 motor test data points will be determined. This should correspond to the operating process of the motor production line so that it can be used to build a model to facilitate the company's continuous optimization, monitoring, and management of the production line. Secondly, the study results do not indicate whether the intelligent systems can be used in other motor production lines or whether the production line of the manufacturing industry can achieve other effects (e.g., manpower reduction and production process optimization). These can be included in a future research to supplement the contents of this study.

**Author Contributions:** Conceptualization, Y.-C.L., C.-C.Y. and W.-H.C.; methodology, Y.-C.L. and W.-H.C.; validation, Y.-C.L., and W.-H.C.; formal analysis, W.-H.C. and K.-Y.H.; investigation, W.-H.C. and K.-Y.H.; resources, Y.-C.L. draft preparation, W.-H.C. and K.-Y.H.; writing—review and editing, Y.-C.L. and W.-H.C.; visualization, W.-H.C. and K.-Y.H.; supervision, Y.-C.L. and C.-C.Y.; project administration, W.-H.C.; funding acquisition, Y.-C.L. and C.-C.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** This research was funded by the Taiwan Ministry of Science and Technology (MOST 108-2745-8-155-001).

**Conflicts of Interest:** The authors declare no conflict of interest.
