Design of an Affordable Cross-Platform Monitoring Application Based on a Website Creation Tool and Its Implementation on a CNC Lathe Machine

Abstract

Currently, monitoring applications are being designed to provide comprehensive, flexible access. Monitoring applications are also required to function on several platforms (e.g., desktops and mobile phones). However, designers often have difficulty meeting these needs, owing to a lack of expertise in various fields. Here, we aim to convey a simple concept to achieve this goal. Visualization data on the system can be displayed and updated online using Google Sites, which can be accessed on several platforms. The data were displayed using dynamic image HTML embedded code. They were generated from the URL of an image stored in Google Drive. The image was taken from the monitoring system user interface created with the Windows Forms app and periodically uploaded to Google Drive. Therefore, using this concept, a cross-platform monitoring application (CPMA) can be created in a short time without complicated database processing or subscriptions to paid applications. Additionally, a messaging feature from the user platform was developed to enable messaging features between an operator at the machining site and another user/manager of the CPMA. This concept was applied to revisualize the collected data from the data acquisition system and the results of the predicted parameters from the main user interface through image uploadbased data visualization and to monitor two CNC lathe machines (with different sensors attached) at different locations.

1. Introduction

Monitoring systems continue to play an essential role in machine tool development. Such systems transmit data regarding the status of procedures using a data acquisition device [1]. A monitoring system comprises the process of measuring various machine characteristics [2]. With a monitoring system, it is possible to determine whether a machine is in good condition. If the condition of the machine is poor, the monitoring system can identify the source of the problem. Such systems are intertwined with several contributions to the development of measurement technologies. Decision support systems employ neural networks, fuzzy logic, and Bayesian networks [3]. These technologies have been enhanced by the Internet of Things (IoT) and can therefore accomplish their objectives as a cyber-physical system machine tool (CPMT). Therefore, considerable efforts have been invested in the development of monitoring devices with IoT-based systems for machine tools.

Mori et al. [4] developed a remote monitoring and maintenance system (RMMS) for machine tools. This technology was created to provide remote monitoring, diagnosis, and service maintenance to thousands of machine tools connected to a central server through the Internet or mobile phone networks. It has been demonstrated that the preventative maintenance feature of an RMMS may prevent machine tool downtime. Such technology has been deployed in Japan by interconnecting more than 8000 machine tools globally. For the advancement of computerized numerical control (CNC) workshops, wireless technologies and a CAD/CAM system were incorporated in a study by Saedi et al. [5]. IoT-based CNC monitoring automates the recording of production data, thereby reducing paperwork, aiding in decision making, promoting labor productivity, facilitating predictive maintenance, and enhancing occupational health and safety. In addition, wireless modules can be transformed into wireless direct numerical control (DNC) network systems. Tedeschi et al. [6] estimated the cost of implementing IoT architecture within an old system, creating a modular IoT infrastructure for the remote monitoring and repair of machine tools. Lu et al. [7] created an IoT-based optical noncontact, five-axis machine tool calibration system that can rapidly and precisely detect the relative linear movement of a five-axis machining tool. Furthermore, this gadget was constructed using 3D detection signals, an incorporated data-calculating engine, and a Wi-Fi connection module, with the advantages of a straightforward setup, low price, and high-accuracy features for the calibration of the five-axis motor and static errors. Murty et al. [8] constructed an IoT-based monitoring system for lathes primarily designed to monitor the vibration state of the lathe during milling. Sensors were implanted at three distinct locations using a commercial accelerometer. The data are concurrently sent directly to the cloud through the node MCU. Mori et al. [9] created a remote monitoring and maintenance system for machine toolmakers. The technology was designed to remotely monitor the operational state of machine tools to improve client productivity, with remote maintenance accessible via mobile phones, allowing the client and machine toolmaker to communicate. This technology has been deployed in more than a hundred thousand machine tools. Tedeschi et al. [10] utilized an IoT-based gadget for machine-tool maintenance. This IoT gadget includes data-collecting devices capable of auto-learning during milling. The device can gather acceleration and temperature data at 1 kHz with a 100 kbps baud rate and beyond 12 °C with a 400 kbps baud rate with a single 14 byte read. In addition, this gadget was fitted with a camera module to capture infrared light within the wavelength range of 8–14 µm. Kim et al. [11] presented an innovative monitoring method for firms with limited resources and labor-intensive tactics (small- and medium-sized enterprises). This system utilizes a KEM (keep an eye on your machine) system with an open-source, low-cost vision gadget. This gadget monitors the machining parameters of a three-axis milling machine by recording images from the HMI display. The proposed solution also demonstrates its potential for universal applicability to other numerically controlled equipment with an appropriate operator display. Using RFID and wireless communications, Zhong et al. [12] created an IoT-based platform for real-time monitoring of machine conditions. These technologies are used for real-time machine-status monitoring. After analysis by several data models and cloud-based services on smartphones, all collected data are shown on a graphical dashboard.

Several factors need to be considered when building an IoT-based monitoring system. In a study by Kim et al. [11], the authors compiled comparison table of small- and medium-sized enterprises (SMEs) vs. large enterprises. The table demonstrates that R&D in SMEs is still short-term, intuitive, and devoid of experience. This is the primary reason for the development of a low-cost vision-based monitoring system for SMEs. In addition, observational research has revealed that IoT is an intriguing technology, attracting interest related to applications in various fields. However, lack of understanding and ignorance continue to represent impediments to the utilization of this technology [13]. Therefore, IoT-based monitoring systems are made by creating low-cost devices or using open-source technology, which is cheaper and easier to access and develop than traditional technology [14,15,16,17,18,19,20,21]. Although few studies have implemented low-cost monitoring systems for machine tools, their existence demonstrates that this concept is achievable. The creation of a low-cost IoT-based monitoring system for the operation of machine tools can be achieved as follows. Xing et al. [22] proposed a method to obtain data from machine tools to reduce human error, cost, and time requirements. The proposed method involved a new monitoring system that used a cheap device, such as an Arduino, a cheap camera, and an open-source computing platform (Node-RED). The system can also process data remotely and work with wireless communication. Liu et al. [23] utilized an Arduino device to read real-time manufacturing data from an MTConnect-based CPMT, which supports many data formats. These data can be acquired and handled successfully and efficiently.

The development of IoT-based technology has led to Internet-based applications, such as cross-platform monitoring systems. Through the boundless Internet, a cross-platform monitoring application allows a monitoring system to be accessed via any platform, anywhere and a any time (not only accessible in the factory). Thanawattano et al. [24] developed a wearable wireless electrocardiogram (ECG) sensor for cross-platform monitoring. This application uses a ZigBee-to-Wi-Fi gateway to access ECG signals via web browsers on OS-based machines connected to local Wi-Fi access points. Gistescu et al. [25] developed a remote patient monitoring system that can be accessed on any smartphone, tablet, or computer in any operating system. The development of this cross-platform application is applied to the health sector, as well as other fields [26,27,28,29].

In addition, the development of such applications can be achieved with various technologies, which can be categorized into web-view-based JavaScript frameworks, native-like solutions, and actually native solutions [27]. The technology for such cross-platform application continue to be updated, as marked by the introduction of a new technology from Microsoft, the NET Multi-platform App User Interface (MAUI), which was released at the end of 2021 [30].

Furthermore, cross-platform applications cannot be separated into several essential aspects that require further attention. In evaluating the development of cross-platform apps, Rieger et al. [31] proposed general considerations for developing such applications. These considerations are divided into four perspectives: infrastructure, development, apps, and usage. In this study, we focus on the development and usage perspectives, particularly regarding preparation time, speed of development, and user management. With respect to preparation time, a cross-platform application must consider the learning curve, i.e., the developer time needed to explore “getting started” guides, such as tutorials, programming code examples, and community support backup. By focusing on this aspect, the initial learning efforts can be minimized, in line with the consideration of development speed, as it relates to the time it takes to develop applications. Moreover, in terms of user management, a cross-platform application must support various types of user access (web-based, phone-based, and desktop-based).

These are the main aspects that underlie the development of an affordable cross-platform monitoring application based on website creation tools. To shorten the development time and increase the ease of development, the proposed application allows the developer to create a cross-platform monitoring system that can be developed and accessed from various platforms through Google Sites. To reduce the development cost of the data acquisition device, the proposed monitoring system uses an Arduino microcontroller board in combination with commonly available hardware components to wirelessly collect data from the desired implementation.

Furthermore, the proposed system was applied to various types of applications, such as data collection systems and parameter monitoring in CNC lathe machines. In addition, the system was applied to monitor two different CNC lathe machines and revisualize the results in the CPMA. This method is similar to that proposed by Naggar et al., who developed condition monitoring based on IoT for predictive maintenance of four CNC machines [32]. Therefore, the monitoring of multiple machines can be performed using only one device in the future.

2. Materials and Methods

In this section, we elaborate on the initial ideas and materials needed to develop an affordable cross-platform monitoring application (CPMA).

2.1. Design Requirements

We conducted both hardware and software requirement analyses. The hardware requirements include an Arduino DUE, an nRF24L01 transceiver, a MEMS accelerometer sensor, and a piezoelectric sensor. Both sensors were used to collect data from the cutting tool of the lathe machine. The transceiver was used to wirelessly transmit data between the transmitter and receiver modules. Therefore, the possibility of entangling the cable from the data acquisition system must be avoided because the sensor installation in this machine is exposed and can be interfered with by the production of the metal chip. The Arduino unit was used as the main device in the data acquisition system.

Furthermore, the following statements define the requirements with respect to limiting the development process of the proposed CPMA. These requirements are not technical specifications but statements outlining some of the features that will be implemented.

• The application requires data from the data collection system/sensor reading;
• A user interface must be created to act as the main data visualization system display;
• Data processing on the main UI must be performed in several ways to enable additional data analysis in cross-platform applications. This depends on the behavior of the data to be studied based on physical phenomena;
• The main UI must store the collected and displayed data in the cloud; and
• The application must be applicable across platforms and operating systems.

Thus, in terms of software systems, a Windows-based application with C# programming language was used to develop the main application to visualize, process, and save data captured from the hardware system. The Google Drive application was used to save data to the cloud system, and Google Sites was chosen to display the data captured from the main UI as a cross-platform application. Figure 1 shows the overall architecture of the proposed monitoring system (including the hardware arrangement).

2.2. Hardware

The main components of the monitoring system are an Arduino DUE microcontroller, a wireless communication device, and sensors.

2.2.1. Arduino DUE

Arduino DUE is a microcontroller board powered by an Atmel SAM3 × 8E ARM Cortex-M3 processor. This is the first Arduino board to incorporate a 32 bit ARM microcontroller. It has 54 digital input/output pins, 12 of which are PWM outputs, 12 analog inputs, 4 hardware serial ports (UARTs), an 84 MHz clock, two digital-to-analog controllers (DAC), a power jack, an SPI header, a reset button, and an erase button. Considering the use of the microcontroller in the monitoring system, a high-performance Arduino DUE was selected as the microcontroller for the transmitter and receiver modules due to its 12 bit ADC resolution, which is superior to that of rival products, to improve signal interpretation during the development of the monitoring system. The Arduino DUE microcontroller performs the process of reading/collecting the data. This microcontroller is expected to provide data reading results for real-time monitoring superior to those of other Arduino microcontrollers. The use of an Arduino in monitoring systems has been investigated in several studies [22,33,34,35,36,37,38]. Barański et al. [39] conducted a study on the feasibility of using an Arduino unit for data acquisition. During the present study, we concluded that using an Arduino enables the building of a data acquisition system that provides reliable results.

2.2.2. Wireless Communication

The data are wirelessly transferred using radio waves, eliminating the need for poles or cables. An antenna and radio device were installed at both ends of a point-to-point wireless connection. Multiple technologies are employed in point-to-point wireless communication systems. Numerous parties have produced wireless components specific to various applications. These components are typically classified based on their capabilities, features, and uses. An nRF24L01wireless transceiver module was selected for the monitoring system. Designed to enable ultralow-power wireless applications, the nRF24L01 module is a 2.4 GHz single-chip transceiver.

2.2.3. Sensors

The data in this application comprise data readings of from an ADXL1002Z MEMS accelerometer sensor and a piezoelectric PIC255. Both these sensor systems were self-developed and are currently under further development in another study. Those sensors shown in Figure 2.

2.3. Cross-Platform Application System Architecture

In general, CPMA relies on a continuous process of storing and synchronizing data using a cloud-based storage application installed on a personal computer. The uploaded and updated data are images of the sensor readings captured from the main UI within a specific time interval. Furthermore, each uploaded image is associated with a URL for Google Drive storage. The URL remains the same, although the image of the local storage will always be replaced with a new capture version. This URL is then converted into dynamic HTML image-embedded code and displayed on the website. Furthermore, the messaging function is executed by sending messages from the website to the main UI. This function uses features available in specific website creation tools and APIs. The overall operation of this system is illustrated in Figure 3.

Based on the above considerations, several technologies/tools were integrated into a cross-platform monitoring application.

• The Windows Forms app with C# programming language is the primary tool implemented in this system. It was used to build the main UI that displayed the results of the sensor data readings in real time;
• The Google Drive application synchronizes periodically captured image storage from the main UI;
• Google Sites is a website creation tool that is used as a data viewer for images stored on Google Drive;
• Google Forms is used for the messenger feature;
• Google Sheets is responsible for processing messages sent on the main UI;
• The Google Sheets API is used for interconnection between Google Sheets and the main UI.

3. Design Results

The design process of the proposed CPMA is divided into two processes: the main UI design with the Windows Forms app and website design with Google Sites. The following subsections present the results of each design phase.

3.1. Main UI Design

The main UI built with the Windows Forms app must be able to provide complete data-processing information so that the information generated from the sensor readings can represent the monitoring needs without the need for any data processing on the website. The function of the website as a second platform is only to display the results of the monitoring process. The main processing results from the sensor readings are shown in two ways: as data graphs with the time and frequency domains. In the time-domain graph, data processing is also equipped with signal property values, such as the root mean square value, maximum data value, and minimum value. In the frequency-domain graph, the data processing is only equipped with the designation of the frequency value of the highest amplitude. Figure 4 shows the main UI display.

Figure 4 also shows the four areas of the UI that are captured and uploaded regularly. This area includes important data and information from the UI, such as the signal graph of the data-collection system and its signal properties. This area can be expanded based on the information visualized in the CPMA. Each graph comprises the raw data gathered from the two sensors. The amount of data shown in the graph depends on the sampling rate of the acquisition device. Areas 1 and 3 are the processed accelerometer MEMS data in the time and frequency domains, respectively. The accelerometer sensor gathers orthogonal vibration data of the cutting tool from the machine. Areas 2 and 4 display data from the piezoelectric sensor in graphs. This sensor collects the dimensionless signal (uncalibrated voltage signal) generated by pressure from the cutting tool during the machining process. This signal is another aspect that should be investigated in future studies on tool-wear monitoring systems. The synchronization settings in this system are set in the synchronization settings panel, as shown in Figure 5.

This settings panel manages the synchronization technique and synchronization time. Under default settings, the checkbox is not selected, and synchronization is only performed when data processing from the UI is completed (i.e., the disconnect button is pressed). When the checkbox is selected, synchronization continues at the time interval that has been set. The four captured images are stored in a local folder where the Windows app is installed and are automatically uploaded to the cloud-based drive with the help of the Google Drive application on the PC. Figure 6 illustrates the app working process.

3.2. Website Creation

Google Sites was chosen as the web creation tool to complete the cross-platform system. The tool required for Google Sites is an image viewer. However, the displayed images are sourced from each image’s dynamic HTML image-embedded code stored on Google Drive. Figure 7 shows the dynamic image HTML embedded code used to display the image, which is generated by a Google Sites image-embedded code generator [40].

The panel in Figure 5 in Section 3.1 contains a text box for retrieval messages sent via a cross-platform website application. This function is supported by the interaction between Google Sheets (data storage from Google Forms) and the Windows Forms app. Moreover, this function can operate because of the Google Sheets API, which is enabled by the Google Cloud platform console. The Google API client library was installed on Windows Forms. This messaging system displays the last message input from Google in a simple manner. Data processing is then performed in Google Sheets using the OFFSET and COUNTA functions. Figure 8 shows the working process of the message-sending function on the website, and its appearance on the website is shown in Figure 9.

Finally, authentication of the use of CPMA must be considered. To create a CPMA with restricted access, websites, sheets, and form settings must be restricted to certain users. This can be easily achieved with publishing settings, as in other Google Workspace platforms. Therefore, the security of this application can be easily managed with only a few clicks. The developer can choose whether the user can only view or be assigned as an editor of the application. Furthermore, the embedded content has the original permission for strict and reliable control [41].

4. Discussion of CPMA Application

As the proposed monitoring system has two main functions, namely data collection and data prediction, CPMA implementation can also be shown in two applications. The first application involves redisplay of the data collected from the sensor, and the second application is to redisplay the model prediction result from the main application. Furthermore, as previously mentioned, the application was applied to various machines to redisplay the monitoring results from two different machines.

4.1. Data Collection Visualization in CPMA

As stated in Section 3, the proposed CPMA was evaluated by applying two types of sensors developed in other studies. The evaluation arrangement is shown in Figure 10. Different physical phenomena provide impulses for each sensor. A modal shaker was used as a vibration behavior maker to read the accelerometer, and arbitrary pressure was applied to the piezo sensor to increase its voltage reading.

During evaluation, the duration of time and latency of data transmission depend on the Internet connection because it relates to the process of uploading images captured from the main UI. The four images had file sizes ranging from 6 kB to 50 kB. With the relatively small size and stable Internet connection, the upload process and its appearance on the website took approximately 4 s. Figure 11 and Figure 12 show the results of reading the data and their appearance on the main UI and the CPMA website accessed from three different types of devices. Subsequently, the synchronization/uploading process can be performed in two ways: only after the end of data processing (the disconnect button was pressed) or continuously at a given time. However, when the process was carried out continuously, the data processing on the UI crashed when synchronization occurred. This disturbance caused the data retrieval process from the serial port to stop for 90–290 ms, as shown by the results of data recording with a synchronization process every 5 s in Figure 13. Therefore, this continuous synchronization feature is optimally applied when the monitoring process is only performed to display the monitoring data and not to record data for further processing (to be exported to the CSV).

Furthermore, the message-sender feature can be applied using the process and results shown in Figure 14. With this feature, it is understood that an operator at the machining site can receive a message from the user/manager of the CPMA at another location.

4.2. Multi-Machine Monitoring Result Visualization in CPMA

Finally, the proposed CPMA was applied to monitor two CNC lathe machines at different locations. Figure 15 shows the arrangement scheme of the application. To support this application, the CPMA website was updated to include a second page to monitor different machines.

For this implementation, an external cylindrical dry turn was performed on two CNC lathes (Vturn-A20). A 60 × 100 mm AISI1045 round bar was used for the machining tests. The machining process comprised a simple roughing operation with a cutting depth of 0.5 Â mm, a feed rate of 0.1 mm/rev, and a cutting velocity of 300 m/min along a horizontal cutting path with a length of 25 mm. This application proves that the proposed CPMA can be used to monitor different machines at different locations. Although we only present the monitoring system of two adjacent machines with the proposed system, it can also be applied to monitor two different machines in two different locations, even in different buildings or regions. This can be achieved because a specific network is not required. Instead, the system only requires Internet data on desktop users and CPMA devices. Figure 16 shows the latest images captured during the machining process in each machine.

A MEMS accelerometer and a piezoelectric sensor were used in the first machine. Only a piezoelectric sensor was installed on the second machine. Although the system shows the latest images captured during machining, which is an idle state of machining, the overall captured data from this system can be accessed again from the drive system in every folder of the local and cloud systems, as shown in Figure 17.

4.3. Data Prediction Result Visualization in CPMA

As this system can process and display raw data from the machining process, it can be applied to monitor and predict the machine conditions or machining quality. A previous study [42] showed that the linear guide preload loss could be identified by feeding the vibration features, which were assessed by sensors attached to the working table of the machine tool and fed into the artificial neural network predictive model. The vibration features of the spindle tool can also be fused using machining parameters to predict the surface roughness of the machined parts [43]. Other potential applications of the monitoring system include failure prognosis systems, such as the incorporation of surface quality or tool wear evolution during the continuous machining process of batch production. The system can issue an alert when a failure is detected in the machine component and even send an alert when the desired quality of a product is not achieved. This alert system was also developed using an API in another application in this study [44].

Moreover, to support the visualization of the monitoring and prediction of the machine condition results, a new tab in the main UI was developed. This tab contains the parameter prediction result value bar, graph, and parameter properties, which depend on the parameters in the machine that are monitored and predicted. These tabs are then captured for another visualization in the CPMA, as shown in Figure 18.

4.4. CPMA Comparison Analysis

A comparative analysis was performed to clarify the advantages and disadvantages of the proposed CPMA. Such an analysis can be used to define the potential of the system for further development and applications. An existing application with the same main function as the proposed affordable CPMA were used as a comparison object. This application is an open-source database analytics and visualization tool. The purpose of this application is to visualize data in an organized and informative manner. It runs on a computer or server, and the interface is accessed through a web browser. The similarity between these two apps is that they only act as a data viewer in the second user interface dashboard, not as the main interface, which is why they require data that have been collected and saved from the main acquisition system. A comparison of the results is presented in

Table 1. Comparison table of CPMA and an existing application.

Compared Aspects	Technology
	CPMA	Existing Application
Data source	Image-upload-based data captured from main UI (uploaded to Google Drive)	A set of data collected from the main data acquisition system and stored on supported data sources (MySQL, InfluxDB, MSSQL, etc.) [45]
Visualization flexibility	Visualization depends on the main UI development	Visualization is arranged directly in the application
Data visualization type	Time-series and frequency domain, which can be achieved in the development of the main UI	Time-series-based [46,47,48]
Dashboard design	Design depends on the main UI development and is limited by the availability of website creation tool	Panels are limited to those made available by the app and its community [48]
Reliability/scalability	Arrangement can be achieved easily and rapidly with a website creation tool. The settings are simple (only need to set the image URL). Stable and reliable (the latency depends on Internet connectivity).	Arrangement can set according to the specific skill required to operate the app (settings are powerful and exceedingly complex); it would be helpful if the data source can be simplified while remaining stable and reliable [49]
Learning curve (tutorial needed)	Data acquisition code in main UI Image capturing and storing code	Data acquisition code in main system ‘Getting started’ app tutorial is needed, as the system must be understand by the user before use [50]
Development time	Allocated time includes development of the main UI and its data acquisition system in the same software	Allocated time includes the development of the main data acquisition system, which can be stored on supported data sources (MySQL, InfluxDB, MSSQL, etc.).
Cost (excluding data acquisition device)	Free	Free (upgraded feature is available with an additional subscription)

.

The proposed CPMA has positive development potential, although it is not on par with the powerful cross-platform data visualization tools, such as the existing application compared in Table 1, owing to the intended audience. As the CPMA is focused on visualization and development simplicity, as previously mentioned, this is an area in which it must be considered. Subsequently, owing to the feasibility of updates and additional applications, the developer only requires the skill set to design the main UI. Thus, developers can focus on obtaining a powerful monitoring system in the main UI/dashboard and do not need to understand other software applications.

5. Conclusions

In this paper, we describe the development of an affordable cross-platform monitoring application. The application comprises Windows Forms as its main user interface and a website application as an affordable cross-platform application. Image uploading-based data visualization is the main concept of CPMA systems. The hardware used in this system includes an Arduino DUE microcontroller, an ADXL1002 sensor, a piezoelectric sensor, and Wi-Fi modules. The hardware functions as the data acquisition device of this system, with a total cost of approximately USD 163. However, no cost associated with the development of the CPMA system, including the main UI, which can be developed in Microsoft Visual Studio and built in Google Sites. Features have been developed in this system, including a feature to redisplay the results of data collection and parameter prediction in the main UI. Therefore, monitoring results can be seen anywhere and at any time by anyone who has access authority. As much of the visualization of this concept is dependent on the main UI, the skill and time required for development include an understanding of the programming language of the main UI and the required monitoring features. However, the development of the CPMA itself requires an understanding how to use the website creation tool. With this concept, the developer learning curve and system development barriers can also be reduced, owing to its simplicity and the fact that intimate website creation tools, especially the Google Workspace tool environment (which is central to the development of this system) are already widely used. This system was also evaluated by applying it to a piezoelectric and accelerometer sensor reading and acquisition system to monitor two CNC lathe machines. As this system is an Internet-based application, latency performance is determined by the quality of the Internet connection. In the future, the proposed system will be implemented for machine parameter evaluation and machine tool health prognosis applications. Moreover, some improvements to the CPMA design need to be made to increase its accessibility and reliability.