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12 January 2022

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

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Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, 841 04 Bratislava, Slovakia
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Electronics2022, 11(2), 230;https://doi.org/10.3390/electronics11020230
This article belongs to the Special Issue Digitization of Mechatronic Systems Using Modern Information and Communication Technologies
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Abstract

The main purposes of this paper are to offer a low-cost solution that can be used in engineering education and to address the challenges that Industry 4.0 brings with it. In recent years, there has been a great shortage of engineering experts, and therefore it is necessary to educate the next generation of experts, but the hardware and software tools needed for education are often expensive and access to them is sometimes difficult, but most importantly, they change and evolve rapidly. Therefore, the use of cheaper hardware and free software helps to create a reliable and suitable environment for the education of engineering experts. Based on the overview of related works dealing with low-cost teaching solutions, we present in this paper our own low-cost Education Kit, for which the price can be as low as approximately EUR 108 per kit, for teaching the basic skills of deep learning in quality-control tasks in inspection lines. The solution is based on Arduino, TensorFlow and Keras, a smartphone camera, and is assembled using LEGO kit. The results of the work serve as inspiration for educators and educational institutions.

1. Introduction

Experts in a number of academic and practical fields are required in today’s industry. Education institutions and colleges have been requested to integrate Industry 4.0 methods and features into present curricula to ensure that future graduates are not caught unawares by the industry’s changing expectations. Cyber-physical systems are just one of the numerous major change agents in engineering education [1].
Companies and higher education institutions recognize a need to teach employees digital skills and basic programming [2] as a result of new trends and the new capabilities demanded by the labour market [3]. Higher education has become more competitive between institutions and countries, and European universities have modified their teaching techniques [4] in order to create increasingly skilled workers in many fields of knowledge [2].
Nowadays, with the tremendous growth of Industry 4.0, the demands for product quality control and enhancement are continuously increasing. The aim of quality control is to objectively assess the conformity of a product to requirements, identify nonconformities, prevent the further advancement of defective products, and based on the processed inspection results, take steps to prevent errors in the production process. At the same time, Industry 4.0 brings opportunities to achieve these requirements. Among other things, it represents a major advance in process automation and optimization and the digitalization of data collection [5,6].
The transformation of data into digital form, brought about by Industry 4.0, was one of the fundamental factors that enabled the formation of Quality 4.0. Its use reduces errors, removes barriers to interoperability and collaboration, and simply enables traceability and further development. The basic tools of Quality 4.0 include the management and analysis of large volumes of data, the use of modern technologies (Internet of Things (IoT), Cloud computing), as well as machine vision applications with the support of deep learning [2,7].
Machine vision, as a new technology, offers reliable and fast 24/7 inspections and assists manufacturers in improving the efficiency of industrial operations. Machine vision has rapidly replaced human eyesight in many areas of industry, as well as in other sectors [8].
The data made available by vision equipment will be utilized to identify and report defective products, as well as to analyse the reasons for shortcomings and to enable prompt and efficient intervention in smart factories [9]. Artificial Intelligence (AI) and Computer Vision as the smart technologies have received significant attention in recent years, mainly due to their contributions to Intelligent Manufacturing Systems [10]. Research activities have been conducted in recent years in order to introduce intelligent machine vision systems for defective product inspection, based on the exploitation of gathered information by various integrated technologies into modern manufacturing lines, using a variety of machine-learning techniques [11,12].
The application of machine vision with deep-learning support as an aforementioned Quality 4.0 tool represents a major shift in the quality monitoring of products in the production process, and is becoming the new prevailing trend of inspection [13]. Among other things, this approach brings [14]:
  • The possibility of decentralized management and decision making,
  • Increased system autonomy,
  • Fast and stable identification of defective products independent of the user and the time of use,
  • The ability to react flexibly when adjusting the controlled product,
  • The ability to solve complex tasks,
  • An all-in-one solution without the need for additional special HW or SW.
For this direction of quality control, which brings with it many advantages, to be further expanded, it is necessary to educate new generations of young engineers and experts in this field, who will build on the already-known information and further develop this direction. There are commercial systems that students could use during their studies to learn about this field, such as Cognex In-Sight D900 [15], which includes the deep-learning In-Sight ViDi [16] software (SW) from Cognex, but the price is quite high, so their application in the learning process is practically unrealistic. It is for this reason that we decided to prepare our own Education Kit, based on available and cheap hardware (HW) resources such as Arduino [17], LEGO [18] building set, smartphone camera, and open-source software tools such as Python [19], Tensorflow [20] and Keras [21], so that students can acquire basic skills using deep learning in quality control tasks on the model line.
The paper is organized into six sections: Section 2 deals with a deep overview of related work aimed at the need for education on Industry 4.0 technologies, low-cost solutions for modern technology education, Arduino-based low-cost solutions and LEGO-based low-cost solutions. Section 3 describes proposing of our low-cost education kit and its hardware and software parts. In Section 4, the results of experimental evaluations of the proposed education kit are presented. In Section 5, we discuss the evaluation of the results, possible improvements and limitations. In Section 6 we conclude the whole work.

3. Materials and Methods

This part of the article will be devoted to the goals and tasks of our Education Kit, as well as their elaboration. We will present the design of the hardware part of the Kit and the software solution of the tasks that we have decided to solve with our Education Kit in the teaching process to begin with.

3.1. Goal and Tasks of Education Kit

Our goal was to design an Education Kit for the purpose of teaching machine vision with the support of deep learning, that would be simple, inexpensive, illustrative, easy to modify, and, most importantly, that students would enjoy working with it and that it would stimulate their desire for exploration. In proportion to this goal, we also decided to choose different levels of difficulty for the tasks:
  • Simple binary classification task (checking the quality of the OK/NOK state of the product, while the trained convolutional neural network (CNN) knows both states),
  • More complex one-class classification (OCC) task (quality control of the OK/NOK state of a product, where the trained convolutional neural network only knows the OK state and has to correctly distinguish all other products as NOK).

3.2. Preparation of the Hardware Part of the Education Kit

As we wanted to keep our kit simple in every way, the design of the production line made in the Inventor environment, which can be seen in Figure 1, was simple and easy to understand. The design consists of a loading slider (1) which is used to let the product onto the conveyor belt (2), then once the product on the belt reaches the camera tunnel (3), a signal is sent to the smartphone (4) to create an image which is further evaluated by the convolutional neural network in the computer, and based on the prediction of the convolutional neural network, the sorting mechanism (5) at the end of the line classifies the product.
Figure 1. Design of the production line in Inventor: 1—loading slider, 2—conveyor belt, 3—camera stand in connection with the camera tunnel, 4—mobile, 5—sorting mechanism.
To ensure that the components for our Education Kit are simple and easily accessible we have chosen:
  • LEGO building set for building the line,
  • Arduino UNO for controlling sensors and actuators,
  • A smartphone camera to capture images of products on the conveyor line.
Starting from the proposed model of the production line and the selected components that should make up the line, we have created a model of the production line, which can be seen in Figure 2. The production line assembled from the LEGO building set consists of a loading slider (1), a conveyor belt (2), a camera stand in combination with a camera tunnel (3), a sorting mechanism (4), an Arduino UNO (5) for controlling the sensors and actuators, but also for communication with the computer, an H-bridge L298N (6) to control the motors of line, a battery pack (7) to power the line motors, an infrared (IR) obstacle sensor TCRT5000 (8) built into the leg of the camera tunnel to record the presence of the product in the camera tunnel, and servomotors controlling the line movements (9–11)—twice DS04 360 for the loading slider and conveyor belt and once FS90R for sorting mechanism.
Figure 2. LEGO bricks production line—front view: 1—loading slider, 2—conveyor belt, 3—camera stand in connection with camera tunnel, 4—sorting mechanism, 5—Arduino UNO, 6—double H-bridge L298N, 7—battery pack, 8—infrared obstacle sensor TCRT5000, 9, 10—servomotors DS04 360, 11—servomotor FS90R.
The components of our production line, such as Arduino UNO, H-bridge or IR sensor and servomotors, were chosen according to our experience that we have gained while studying and teaching how to work with different types of development boards. The Arduino UNO, as an open-source project created under The Creative Commons licenses [51] gives us the possibility to use any of its many clones at any time when we need to reduce the production costs of our Education Kit, which has been met with a positive response. The remaining components are commonly used during teaching and are thus familiar to students and available in stores selling Arduino accessories. The wiring diagram of the components connected to the Arduino can be seen in Figure 3.
Figure 3. Arduino wiring diagram.

3.3. Preparation of the Software Part of the Education Kit

Before preparing the datasets and designing the convolutional neural network architecture, we designed a flowchart of the processes running on the line during production. The processes of the production line should simulate serial production and therefore they run in an infinite loop. We designed the process of production to stop the production line once no product arrived in front of the IR sensor in last 15 s. The process diagram, which can be seen in Figure 4, includes processes controlled by the Arduino, such as:
Figure 4. Process diagram (dashed line—information flow).
  • Opening and closing the loading slider, where after opening it is possible to insert the product into the production process,
  • Operating the conveyor belt, ensuring the movement of the product through the production line and stopping the product in the camera tunnel after it has been detected by the IR sensor,
  • Reading the value of the IR sensor,
  • Sorting products with the sorting mechanism, used to separate OK and NOK products according to the result of convolutional neural network prediction.
The diagram also includes processes controlled by an application written in Python, running on a computer that provides the user with a simple graphical user interface (GUI), shown in Figure 5 with the choice to save the snapshots, to display the product currently being evaluated, and to display the result of the prediction of the convolutional neural network.
Figure 5. GUI of the application during product classification in the first task.
Since we decided to start with two tasks of different difficulty, it was necessary to specify the process for each task. For binary product classification, i.e., classification where the trained convolutional neural network can distinguish between just two known product types, the procedure is relatively straightforward. First, we needed to create a dataset of product images, which can be seen in Figure 6.
Figure 6. Products for the first task: (a) NOK (wheel without spoke), (b) OK (wheel with spoke).
Both products were photographed in the camera tunnel in different positions on the production line and with different rotation of the products. Subsequently, in order to increase the dataset size and improve the robustness of the neural network in future prediction, we applied brightness adjustment, Figure 7, and noise (Gauss, salt & pepper, speckle), Figure 8, to the images. The final dataset contained a total of 3000 images of both product states—OK and NOK, which were represented in the dataset in the ratio 50:50. Overall, 1920 images of the dataset were used for training, 600 for testing and the rest—480 images—were used for validation.
Figure 7. Adjusting the brightness: (a) darkening, (b) lightening.
Figure 8. Noise application: (a) Gaussian, (b) S&P, (c) speckle.
Next, we designed a convolutional neural network, whose block diagram can be seen in Figure 9, and according to the proposed architecture, we then built and trained a convolutional neural network model in Python containing two convolutional layers with ReLU activation functions, two max pooling layers, a flattening layer, a fully connected layer, and an output layer with a sigmoid-type activation function. The video example of Task 1 can be seen in [52].
Figure 9. Block diagram of CNN architecture design for binary classification.
Before starting the training process, we set compile and fit parameters of our convolutional neural network to:
  • Loss—binary crossentropy [53],
  • Optimizer—adam [54],
  • Batch size—32,
  • Epochs—20,
  • Validation split—0.2.
The summary of our convolutional neural network for the binary classification task can be seen in Table 1.
Table 1. Summary of convolutional neural network for binary classification.
In the second task, product quality control using one-class classification will the trained convolutional neural network needed to be able to classify incoming products into two classes—OK (wheel without a spoke) and NOK (any other product). Figure 10 shows the OK products, marked by the yellow rectangle, and some of the many NOK products, marked by the red rectangle.
Figure 10. Products for the second task: desired state—yellow rectangle, undesired state—red rectangle.
The process of preparing the dataset for training a convolutional neural network as well as designing the architecture of network itself is different from the previous task. In this task, we can only use the images of the products in the desired state from the camera tunnel, supplemented with brightness-adjusted images. The same as can be seen in Figure 7, to create the training dataset that consisted of 1150 images. With noise-added images, it is possible that we would introduce error into the resulting prediction during training. The images of the undesired states, along with a portion of the images of the desired states, are subsequently only used when testing the trained CNN. We also augmented the test dataset, some elements of which can be seen in Figure 11, with various other objects that the convolutional neural network must correctly classify as undesired states during testing. The final test dataset contained 1300 images, of which 200 were in the desired state and the rest in the undesired state.
Figure 11. Different types of images found in the test dataset.
In designing the architecture of the convolutional neural network we adopted a procedure where first a mask is applied to the image to ensure the removal of the influence of the coloured parts of the LEGO building blocks from the background of the image. Then, the pretrained ResNet50, for which the architecture is described in [55], is used to extract features, and finally the output of ResNet50 is used as an input to the one-class support vector machine (OC-SVM) method. This provides a partitioning between the interior (desired) and the exterior (undesired) states based on the support vectors, for which optimal count needs to be found by fine-tuning the nu parameter of the OC-SVM method via grid search. The proposed architecture can be seen in Figure 12. A video example of Task 2 can be seen in [56].
Figure 12. Proposed CNN architecture for one-class classification.

4. Experimental Results

Despite its simplicity, our illustrative and easy-to-modify Education Kit offers the student a variety of options for solving different tasks of machine vision with support of deep learning that can be simulated in the production process, including:
  • Binary classification—convolutional neural network classifies just two types of products on the line,
  • One-class classification—convolutional neural network distinguishes known OK states from NOK states,
  • Multiclass classification—convolutional neural network classifies products into several known classes (the production line can be modified by modifying the sorting mechanism at the end).
When designing our Education Kit, we focused on making it simple, illustrative, and easy to modify, as well as inexpensive in terms of purchasing the components to build it. The estimated cost can be seen in Table 2 (not including the cost of the computer and mobile phone).
Table 2. Cost of creating the Education Kit (not including the cost of the computer and mobile phone).
The results that can be achieved with our Education Kit, despite its simplicity, correspond in both tasks, as can be seen in Table 3—Area Under the ROC Curve (AUC) scores and Figure 13—Receiver Operating Characteristic (ROC) curves and Figure 14—confusion matrices, to a very accurate prediction.
Table 3. Evaluation of CNN prediction accuracy for both tasks.
Figure 13. ROC curves for both tasks (left curve—binary classification, right curve—one-class classification).
Figure 14. Confusion matrices for both tasks (left matrix—binary classification, right matrix—one-class classification).

5. Discussion

The practical evaluation of the proposed education kit in the whole form (hardware and software) in the context of training and adaptability by the end users (students) was not able to performed because of pandemic restrictions (closed laboratories for education). The software part of the educational kit (dataset, proposing of CNN, experiments with dataset), however, is currently included in our education course “Machine vision and computational intelligence”, where students propose their own software quality evaluation solutions based on this kit. The education kit was presented during a lecture, and this topic was enjoyable for the students since they had unusual number of questions and showed interest in diploma theses in similar topics.
Individual types of occurred errors cannot be classified using the current dataset, which was designed for the second task. To do so, the training dataset would need to be augmented with images of specific possible occurred errors, and the task and the neural network architecture would need to be adapted to multiclass classification. Following that, we could improve our solution by adding the ability to mark the location of defects, which would help in detecting production errors. For example, if the error always occurred in the same location, we would know (if it is in the real production process) that these are not random errors and that we must check the previous processes on the line.
As can be seen in the confusion matrix for the second task, it would be possible to further work on CNN’s accuracy in recognizing OK products. To this end, it would be possible to extend the training dataset, where there would be various errors and defects on the inspected products. It could be possible to generate hundreds or even thousands of images of real damaged products. For this task, modern techniques such as real-time dataset generation by 3D modelling software (as Blender [64]) or 3D game engines (as Unity Computer Vision [65]) can be utilized. With such an augmented dataset, it would be possible to use other CNN structures aimed at recognizing the location of objects in the image (in our case, the location of defects and errors on the product) such as the architecture YOLO: You only look once [66], or SSD: Single Shot MultiBox Detector [67].
Python is a great programming language for working with images and creating and training convolutional neural networks, but it did not provide us with as many options when designing GUI. This deficiency could be remedied by using Windows Presentation Foundation (WPF), which gives the user a lot of freedom in designing the GUI window and is also more user-friendly. However, WPF is based on the NET platform, so we need to use one of the libraries that allow us to load a trained convolutional neural network from Python, such as ML.NET. ML.NET supports basic classifications such as binary and multiclass, allows loading pretrained neural networks, but also includes a model builder for creating and training neural networks. A GUI window design created in a WPF environment that could be used with ML.NET library can be seen in Figure 15.
Figure 15. GUI window design in WPF environment.
Testing of the proposed convolutional neural networks in the production process of our production line was carried out in both tasks on 20 pieces of products that passed through the line. Examples of the testing for both tasks can be seen in the videos available on the link in the Supplementary Materials section. A sample of some of the testing results for the second task can be seen in Figure 16.
Figure 16. Results of testing CNN for second task—one-class classification.

6. Conclusions

This article deals with the proposal of a low-cost education kit aimed at selected digital technologies for Industry 4.0, such as convolutional neural networks, deep learning, machine vision and quality control.
Industry 4.0 brings with it many challenges and opportunities for young, enthusiastic engineers. However, in order to meet these challenges, they need to have a solid foundation in the field, and it is this foundation that the school can provide them with, where they have the opportunity to become acquainted with the issues and try solving various tasks in this area. However, solving the problems requires various hardware and software tools, which are often expensive, not always easy to access and need to be constantly improved as technology advances.
Universities emphasize their role as innovation testbeds and educators for future generations in the development of future technologies. Traditional education has contributed significantly to current levels of economic development and technological advancement. As a result, incorporating Industry 4.0 concepts into engineering curricula is one of the top priorities of academic institutions. Based on a review of the available literature and current research projects, it can be concluded that using low-cost equipment and designing low-cost kits is a typical occurrence in academic and university practice. The survey’s findings show that in the context of Industry 4.0, low-cost equipment can be used to educate high-tech technologies. In academics, hardware such as the Arduino or the LEGO kit have shown to be popular. We were motivated and encouraged to design our own low-cost kit as a result of this review. Furthermore, we discovered that the topics we wish to address (deep learning, machine vision, and quality control) are not covered in these works, indicating that our kit could be useful in these fields. The deep overview of the literature can serve readers as an inspiration for their future research or development of their own low-cost educational solutions. Moreover, the need for learning new digital technologies is not just for universities; it is necessary to cover the whole range of education levels from primary, secondary, and higher education to even postgraduate lifelong learning of professionals with an engineering background (e.g., following the Swedish Ingenjör4.0).
In this work, we presented our own design and implementation of a low-cost education kit. Based on the literature review, we have identified that current works and low-cost solutions do not address topics such as deep learning, convolutional neural networks, machine vision and quality control. This was an encouragement to create a new low-cost educational solution that could be an original solution and enrich the current state of the field. The kit is simple, illustrative, easy to modify, and interesting and appealing for students to work with, as it combines elements of electronics (Arduino), mechanics (production line), control (sensors and actuators), computer science (convolutional neural networks, GUI) and communication—the entire mechatronics spectrum. The Education Kit uses inexpensive and readily available components, such as the Arduino, the LEGO kit, and the smartphone camera, to ensure its modifiability and accessibility to schools. With our proposed Kit, various product quality control tasks can be solved using machine vision-supported convolutional neural networks, such as binary classification, multiclass classification, real-time YOLO applications, or one-class classification tasks, to distinguish a desired state from any other, undesired, state. The educational kit’s software component (dataset, CNN proposal, dataset experiments) is presently included in our education course “Machine vision and computational intelligence”, where students propose their own software quality-evaluation solutions based on this kit. The education kit was provided during a lecture, and the students found this topic to be interesting since they asked an uncommon number of questions and expressed interest in diploma thesis themes on similar issues.
The future developments of the educational kit can be achieved especially on the software side of the solution. The training dataset can be augmented with images of specific hypothetical faults, and the task and neural network architecture can be than changed to allow multiclass classification. Modern techniques, such as real-time dataset generation by 3D modelling software or 3D game engines, can be used for such dataset augmentation. Following that, we may expand our educational kit by allowing users to mark the location of faults.
We believe that our kit will become a quality learning tool in educating the next generation of young engineers, and will help them open the door to the world of Industry 4.0 technologies.

Supplementary Materials

Video examples can be found here: https://drive.google.com/drive/folders/1psvumJAJmNU7LAPjgiwPiyzHSMzzeWR-?usp=sharing (accessed on 8 December 2021).

Author Contributions

Conceptualization, M.P. and O.H.; methodology, O.H.; software, M.P.; validation, O.H. and P.D.; resources, M.P., O.H., E.K. and P.D.; writing—original draft preparation, M.P.; writing—review and editing, O.H.; supervision, E.K. and P.D.; project administration, P.D.; funding acquisition, P.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Slovak research and Development Agency under the contract no. APVV-17-0190 and by the Cultural and Educational Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic 016STU-4/2020 and 039STU-4/2021.

Conflicts of Interest

The authors declare no conflict of interest.

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Investigation of AlGaN Channel HEMTs on β-Ga2O3 Substrate for High-Power Electronics
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