1 Introduction

It is a fact that Industry is being transformed by the accelerating evolution to “Industry 4.0,” pushing universities in the same direction with technological tools that provide students the skills and knowledge necessary to satisfy modern industry requirements. Industry 4.0 has nine technical pillars. This research work explores and implements three of them, namely, (i) advanced simulation, (ii) robotics, and (iii) system integration, which are highlighted in Fig. 1. These pillars are supported by concepts such as smart factories, virtual commissioning, simulation, digital twins, and others shown at the bottom of Fig. 1, which are discussed later in this paper.

Fig. 1
figure 1

Impact of pillars of Industry 4.0

Full size image

Smart factory products, resources, and processes can be characterized by cyber-physical systems (CPS) [1]. An essential keyword implicit in these is the digital factory (DF), which plays a significant role in all parts of planning for manufacturing processes and assembly systems [2, 3]. DF is a real factory model used for design, planning, and operations purposes. The digital factory developed during the engineering phase should be integrated into a “smart” factory with real-time data and information [1]. DF is the generic term for a vast network of digital models, methods, and tools; it includes simulation and 3D visualization, which are integrated by continuous data management. Its purpose is the comprehensive planning, evaluation, and constant improvement of all essential structures, processes, and resources of the real factory in connection with the product [4].

New technologies, concepts, and trends require teaching platforms to adapt quickly; therefore, in the educational arena, we should use virtual tools to arrive at solutions because they are affordable and available. Engineering students with an industry profile need to know the available technological tools in manufacturing and automation under the DF concept.

This project used product lifecycle management (PLM) and automated solutions in a teaching platform for industrial engineering and mechatronics courses to link the three pillars of Industry 4.0 previously mentioned to satisfy the academy’s need to develop virtual and hybrid manufacturing scenarios. In this case, we implemented these in the Manufacturing Systems Integration Lab (MSIL) course, where concepts like PLC automation, virtual commissioning, robotics, CAD/CAM, and process-planning are covered. The availability of various software—in terms of licensing, costs, and levels of integration—makes possible the implementation of these types of solutions. In this way, future engineers prepare professionally by learning new technology, concepts, knowledge, and automated software to meet the world’s changing requirements.

Therefore, it is critical to take advantage of production-system-development technology, not only in Industry but also in Academia. Several engineering tools have emerged in recent years whose impact will be more significant in the future for both Industry and Academia. This research project implements mainly the PLM tool, which supports the production processes by reducing the development process and time to market, increasing the efficiency of the operations (time, resources—human, materials, and financial), due to the improvement of product structure management and the reduction of overhead activities [5].

This research paper contributes to interactive engineering development as it provides efficient and proved tools and methods for learning using the available resources. It does not limit the learning process but expands it to even to the learning from home.

2 Teaching platform using virtual/hybrid commissioning for MSIL as a solution

As the competition in the global manufacturing marketplace becomes increasingly intense, Industry has a growing need for graduates who are productive and “get to work” once they leave school. This need has been a challenge to engineering and technology educators: How do they effectively and efficiently prepare students with the most relevant knowledge, skills, and competencies so that they are adequately trained to address the needs of the manufacturing companies [6]?

It is essential to contextualize the PLM concept; therefore, we present the following eight aspects of the technological platform that we had implemented that increase the learning engagement thru the interaction with the system:

  1. 1.

    PLM tools used in the manufacturing process: In our study, we used PLM software applied to digital manufacturing. Although this kind of software is used sparingly in the educational environment, it has great potential in the training of future engineers because the students will apply not only CAD/CAM but also Digital Manufacturing (DM) tools.

  2. 2.

    Virtual commissioning using two leading tools (Delmia and Tecnomatix): For evaluating and implementing the simulation previously modeled by Valenzuela [7] in Delmia Automation and the work developed during this project using the simulation process software, Tecnomatix.

  3. 3.

    The consideration of Computer-Aided technology (CAXs) tools: Besides the use of digital manufacturing tools, the CAXs are also considered. The CAD aspect is used due to the nature of the project, and the CAM module is considered for G-code and the manufacturing of complex pieces.

  4. 4.

    An educational platform based on the use of a virtual/hybrid system: The new classification proposed by Valenzuela [7], where virtual and real components are combined into a fully functional automated manufacturing system. Four scenarios, depending on the user requirements and resources, are implemented to test the educational platform to increase the interactiveness of the student with the system.

  5. 5.

    Computer modeling and virtual/real simulations: Both the teacher and the student receive the benefit of a dynamic hybrid simulator and computer modeling. The teacher can prepare real-time and relevant exercises while explaining theory and concepts, thereby bridging the gap between theory and practice. The teacher can also create various faults and improve the troubleshooting skills of the student.

  6. 6.

    World Wide Web implementation: For the development of Web system architecture that allows the user to access a virtual laboratory via the internet. A web site is designed and developed for virtual interactions. This aspect is not covered in this project; however, it is implemented in other works referenced here.

  7. 7.

    Focus on the framework for manufacturing system integration: Manufacturing nowadays requires engineering at every phase, including production inception, design, and, increasingly, during and after the transition to the manufacturing itself/through this work, we become trained for the increasingly competitive manufacturing field.

  8. 8.

    Interaction between the PLC programming and the virtual/real manufacturing process: Students can develop their skills to program a PLC in a complex environment without the worry of damaging physical equipment because this is a virtual process.

Several teaching platforms developed through time focus on specific areas of manufacturing, such as those presented by Jianping et al. [8] and Shiue et al. [9], where a virtual or simulated teaching platform is implemented. Bengu [10] applied interactive multimedia to develop manufacturing courseware. The online platform of Saygin and Kahraman [11] is used to teach and operate a PLC in an automated manufacturing systems control area for distance education. There are other works like [12, 13], who develop an innovative instructional model to improve manufacturing courses.

Table 1 shows a summary and comparison of nine different authors’ works and the main characteristics they cover on their studies—described above in aspects 1 to 8. In this research paper, we are addressing the eight aspects, whereas the other researches cover different aspects. In terms of interactivity, we consider our research to go beyond the simulation [6] and the communication capabilities [10] of the educational systems.

Table 1 State of the art for this research
Full size table

Next, a Venn diagram in Fig. 2 presents the context in which this work is based, as well as different concepts that are included in this work along with other manufacturing educative platforms. The teaching methodology assumes a set of formative and motivational processes that work well in the field of knowledge to get the best understanding. Each teacher must rework and adapt the methodology as a function of processes and expectations [19]. The method proposed here sets the structure for the complex decisions presented by a changing world; it responds to the continuous challenges and innovative approaches that we must foresee in the manufacturing field.

Fig. 2
figure 2

Venn diagram of the areas considered in designing the teaching platform

Full size image

2.1 Technology software integration and virtual teaching in engineering education

Teachers need to be more creative when designing their class materials. When technology needs to be taught, teachers are usually limited by not being able to access technology because of cost, and students cannot practice with technology, or these practices are limited.

Educational research shows that a virtual teaching platform is a powerful tool for teaching innovation; thus, it is worth generalizing into teaching applications [20]. Software tools and technologies such as Java Applets, LabVIEW, MATLAB, and Working Model are used extensively at universities to supplement traditional online educational content that consists of handouts and multimedia [16]. Nowadays, it is mandatory to implement new DM technologies available in the market, such as Delmia Automation or Process Simulate.

Following up on the different points mentioned before, next is a description of the features implemented in our study, related to the software that we used.

  1. 1.

    The educational platform is based on the use of a virtual/hybrid system. The new classification proposed by Valenzuela [7], where virtual and real components are combined into a fully-functional, automated manufacturing system, is used in our study. Four scenarios, depending on the user requirements and resources, are implemented in the test of the educational platform.

  2. 2.

    Computer modeling and virtual/real simulations: Both the teacher and the student receive the benefit of a dynamic hybrid simulator and computer modeling. The teacher can prepare real-time and relevant exercises while explaining theory and concepts, thereby bridging the gap between theory and practice. This virtual/real simulation allows for better interactive student experience for the understanding of the manufacturing integrated systems.

2.2 Engineering assignment

New education reflects the real needs of Industry, which faces problems of integration across the traditional disciplines, such as:

  • Working with digital tools for communication.

  • Working in interdisciplinary, multi-skill teams.

  • Working in a virtual environment.

Also, future professional engineers will have to think about the context. The choice of materials and the design solution cannot be based on purely technical and economic criteria but must also consider environmental and societal aspects, like recycling, pollution, and disassembly and reuse concerns, for example. All of this must be considered as we develop a new educative platform where technology integration and virtual teaching play an essential role.

Another critical aspect is PLC programming, which is the key to the success of every automation program. PLC programming is like the machine behind all automation programs used in Industry. Without it, the economy will stagnate because it leads to gains in productivity [21].

Next, we present two characteristics of the platform we used in this research, in terms of manufacturing engineering and PLC programming.

  1. 1.

    It is focused on the framework of manufacturing system integration. Current manufacturing processes require engineering at every stage.

  2. 2.

    Interaction between the PLC programming and the virtual/real manufacturing process: Students can develop their skills to program a PLC in a complex environment without the worry of damaging physical equipment because this is a virtual process.

In Fig. 3, we present an overview of the research methodology. We implemented the two cases for analysis and conclusions.

Fig. 3
figure 3

Overview of this research methodology

Full size image

The new methodology for the development and implementation of a teaching platform introduced in this paper is a process having three main stages. The first two are the core of this project, while the last one is the evaluation of the case study.

In this section, we introduce the new teaching platform in three phases, where virtual and real components are combined with technological software in a manufacturing assignment using technological tools at the core. The three stages are shown in Fig. 4. The following methodology is valid regardless of the version and changes of the MD software used. However, it is necessary to be aware of the new capabilities that the software has to bring these advantages to the students’ training. This methodology focuses mainly on educational purposes; however, it is also functional in industrial environments where there are no students, but paid operators. One advantage of virtual commissioning is that it simulates processes very close to real-life; thus, this can be very useful for training workers and teaching students similarly.

Fig. 4
figure 4

The three phases of the teaching platform

Full size image

  • Phase 1

Phase 1 consists of creating the Flexible Manufacturing Cell (FMC) for the different scenarios proposed. Here is where PLM tools play a vital role in the design, control, and integration of the hybrid/virtual environments. It corresponds to the engineering development, where virtual and real components are combined with a high level of integration to create the different scenarios proposed: Virtual Process–Virtual Controller, Virtual Process–Real Controller, and Real Process–Virtual Controller. Concepts like Computer-Aided Design (CAD), Virtual Commissioning, Simulation, Digital Twin, PLC Code Programming, Open Platform Communications (OPC) Server connection, and Offline Programming (OLP) are implemented during this stage, with a methodology followed to its completion.

The virtual/hybrid real case scenarios are described in Table 2, showing the main features for each case. A virtual environment includes a Virtual Process and a Virtual Controller. Using one software to digitalize, simulate, and automate the manufacturing system avoids complexity in the lab [22]. PLM tools play an essential role in the development of this first case by allowing the virtualization of the physical manufacturing system. The scope of this paper focuses only on automated manufacturing systems and PLC programming. PLM tools, specifically DM, allow us to test and validate many different elements of a manufacturing system (such as design, material flow, production, layout, and ergonomics) to check a manufacturing process before it is built, making it more efficient.

Table 2 Description of scenarios
Full size table

A hybrid manufacturing environment combines virtual and real components into a functional automated manufacturing system (virtual-process → real-controller, or vice versa). The virtual process and the PLC can communicate by using the OPC client/server protocol. The computer where the virtual process (the OPC client) resides does not necessarily have to include a specific OPC server because the server can be reached via IP, allowing remote communication of the virtual process with the physical PLC. This communication via IP makes remote education and training possible.

In real scenarios, both the process and the controller are real. There is not a simulation of the manufacturing process in any DM tool, and a virtual environment is not used. The system is simply implemented once the functionality and productivity are designed for manufacturing. Given this, there are not opportunities to validate “what if” scenarios, since both the process and controller are real [7].

The scenarios presented in this study combine virtual and real elements to form different systems where the primary purpose is to have a working automated manufacturing system that is a flexible manufacturing system (FMS). Table 3 presents the five stages that need to be followed to accomplish each one of the scenarios. For a more in-depth description, the reader can look at [7], where we obtained the methodology to develop the scenarios.

Table 3 Methodology for the design of scenarios using DM tools
Full size table

  • Phase 2

This phase is educative-oriented. Here, laboratory teaching experience, knowledge of manufacturing systems integration, and experience using manufacturing cells are required. This phase shows the planning steps to adapt the activities of traditional laboratory teaching to a virtual/hybrid context.

The objective of this phase is to design and plan the lab practice systematically, oriented to the use of DM tools. Usually, universities have a pedagogical department with teaching guidelines for the development of laboratory practices. The support of this department is vital to carry out the exercises. Below are the steps that we adapted from a guide of the Academic Development Department (ADD) in Tecnologico de Monterrey [23] for the planning and design of laboratory practice.

Planning:

  1. 1.

    Define the contents according to the previous steps.

  2. 2.

    Define who designs the practices.

  3. 3.

    Determine the purpose of the practice.

  4. 4.

    Define the general considerations in the planning.

  5. 5.

    Define the structure of the laboratory practice.

  6. 6.

    Define the experimental techniques that students learn.

  7. 7.

    Define the evaluation tools and processes.

Next, it is necessary to design and write the practice to be implemented, considering the points above and the following, adapted from [23]:

Design:

  1. 1.

    Consider the following elements, if applicable: Reference to the contents, Learning objectives, Practice objectives, Introduction, Applications, Theoretical Framework, Materials, Tools and Equipment required, General safety rules, Procedure.

  2. 2.

    Design practice activities.

  3. 3.

    Design activities that support the pre-report (if applicable).

  4. 4.

    Design activities that support the development of the practice.

  5. 5.

    Design the assessment tools.

  6. 6.

    Design the lab manual.

The authors propose the following tips when applying DM tool exercises:

  • Clearly show the title of the practice.

  • Have detailed procedures to avoid students getting lost.

  • Write clear objectives.

  • Have criteria to ensure that the implementation of the practice will be successful.

  • Develop the practices to be informative and lucid, considering that the students have not worked with the software.

  • Show precisely where the different files and programs are located.

  • Include tips and notes.

  • Provide examples of common errors and the way to resolve them; this is valuable information for the students.

  • Practice implementation

Practice implementation has two stages, technical and educational. The technical stage is strongly related to the “Integration” step in Phase 1; here, the instructor must properly install the software, hardware, and all the technical aspects for the students to perform the practice satisfactorily.

On the other hand, the educational component has several steps that must be followed to implement the practice:

  1. 1.

    Laboratory practice explanation: the students must know how they will learn the content.

  2. 2.

    Purpose of the practice and how it will be assessed: Explain the purpose and objectives of the practice and show the instrumentation tools.

  3. 3.

    Organization and development of the practice: Organize the students and assign the materials.

  4. 4.

    Work commitment in the laboratory: Emphasize the security rules, respect for the time, and the care of materials and equipment.

  5. 5.

    Perform the practice: Do the first activity, the second, and the subsequent ones.

In general, for this whole stage, the instructor must:

  • Know the practice very well so he can guide the students.

  • Know the problems that the students may confront.

  • Be able to solve the problems and use the software.

  • Know the technical aspects involved in the practice.

  • Be able to manage and carry out the practical activities efficiently and advise and guide the students so they can achieve the goals.

For more in-depth information about both stages (Design and Implementation), the following references can be consulted: [19, 23, 24].

  • Phase 3

Phase 3 corresponds to the evaluation stage. We have considered qualitative and quantitative assessments for each implemented scenario in the teaching platform. The following are the procedures to qualitatively and quantitatively assess each scenario and the proposed teaching platform. This phase must be performed simultaneously with phase 2.

Definition of performance variables

Here it is essential to compare patterns between the actual teaching platform and the new one implemented using virtual/hybrid scenarios. In this case, performance variables are considered; several variables can be evaluated from different perspectives. Within the context of the scenarios implemented using the DM tool, we divide the performance variables into three main classifications, namely, user-level, academic performance level, and designer/creator level. Among many possible variables, the author proposes the following, as shown in Table 4. These variables are the ones that tell us how the users (students, instructors, teachers, and developers) perceive the scenarios.

Table 4 Performance variables
Full size table

Measurement tools

Research on student perspectives requires different approaches. In our study, for the educative purpose, the research objectives for implementing the different scenarios are:

  • To know how the scenarios affect the students’ performance.

  • To test and assess the characteristics of each scenario to achieve the educational goals of the course.

  • To provide a value judgment for implementing these scenarios in universities that cannot afford a real-real environment.

  • To get information that may contribute to improving the teaching platform.

It is necessary to design several qualitative and quantitative instruments to gather information from the performers. The creation of these tools helps to get information from different perspectives, which can be compared to get valid data. This step requires the support of an educational or pedagogical advisor to design adequate measurement tools; however, we present some tools for consideration, as shown in Table 5.

Table 5 Possible measurement tools
Full size table

Selection and classification of groups

Classification sorts people or things into groups or categories on a single basis of division. We constructed this step because of the structure that this thesis has.

The purpose is to create a control group to carry out the Prof of this teaching platform using the scenarios. First, it is necessary to make the student selection. They must meet specific requirements, such as a particular track or a certain level of knowledge so that they can achieve excellent results. The requirements must relate to issues, topics, software, and techniques to be applied.

After obtaining the control group, it is necessary to subdivide it according to other detailed characteristics such as learning styles (kinesthetic, visual & auditory) and the way of processing information (dependent or independent). The objective of this selection and classification is that all the scenarios have student groups with homogenous characteristics.

Results analysis

Results analysis refers to processing the data collected and obtained during the experimentation process—what is observed, what is discovered, what is found out—and then interpreting the data. Depending on the tool used, it is necessary to validate it through a methodology.

During this stage, we need to process the results according to standard procedures like organizing the data in tables, diagrams, or graphic representations. Then we need to interpret the data, consider the implications, and draw conclusions; here, we need to determine the qualitative and quantitative relationships. We also discuss the assumptions of the experiment and its limitations, explain the research findings, and generate new questions or identify new problems from the results.

The three phases are illustrated in Fig. 4.

3 Case study implementation of the teaching platform in an MSIL

The methodology described is now implemented for the virtual–hybrid–real automation scenarios previously presented. The manufacturing system automation cell of the Mechatronics Laboratory at Tecnologico de Monterrey was chosen. The subject where the learning platform was applied was the laboratory of the Manufacturing System Integration course (MR3018). The technological software used in this implementation was Delmia Automation from Dassault Systèmes and Process Simulate from Siemens PLM.

Next, we describe the implementation. The scenarios created using Delmia Automation were already developed in [7]; we developed scenarios 1 and 2 using Process Simulate and implemented all the scenarios using the proposed teaching platform.

3.1 Phase 1: virtual commissioning of virtual–hybrid scenarios

For the implementation of the proposed scenarios, the authors chose the manufacturing cell, shown in Fig. 5, of the Mechatronics Laboratory of the Tecnologico de Monterrey facilities, which are used to teach MSIL. The manufacturing cell is composed of the following elements:

Fig. 5
figure 5

Real manufacturing cell

Full size image

1 Motoman UP6 robot with an XRC 2001 controller

Magazine tool with the following components:

1 EMPCO PC Mill 155 Vertical

 1 pneumatic gripper

Machine Center

 1 pneumatic screwdriver

AS/RS Storage

 1 pneumatic suction cap

Two-line Conveyor

Assembly station

Inspection system

 Magazine tool

Allen Bradley PLC

 3 Containers

 

 1 dispenser

 

 1 pneumatic press

The tools and software implemented to build the scenarios in this study are Process Simulate/Delmia Automation, OPC Server, RsLogix500, NX Unigraphics/CATIA (CAD/CAM). Figure 6 shows the virtual–hybrid environment developed for scenario 2: Virtual Process developed in Tecnomatix and Real PLC programmed in Allen Bradley PLC.

Fig. 6
figure 6

Software implementation

Full size image

3.2 Phase 2: implementation stage

3.2.1 Topics definition

During this stage, it is necessary to review the study plans of the courses modeled in each of the different scenarios to align the competencies to be taught. It is essential to consider the objectives to know the capabilities and features of the PLM tools that will be used. It is necessary to establish standards for traditional labs and then apply these objectives to virtual/hybrid laboratories. It must be clear that a scenario with a real part will have different topics and goals than those with a virtual part; this is for obvious reasons and should be planned from the beginning. To implement the platform properly, one must not only understand the curriculum and how the academic course is taught but also be empowered to adapt the curriculum to meet the instructional needs of the students and class. The implementer must have experience using an FMS and a comprehensive background in the use, application, and characteristics of the PLM tools.

The course taken as a case study is the Manufacturing System Integration laboratory taught at Tecnologico de Monterrey for the Mechatronics Engineering majors; it is usually taken by junior students. The four selected topics to be covered by this teaching platform using the hybrid scenarios are:

  1. 1.

    Fundamentals and operation of an FMS

  2. 2.

    Industrial robot handling and coordinate systems

  3. 3.

    Programming an industrial robot and application design

  4. 4.

    PLC programming and integration.

3.2.2 Define the support software

As stated before, the leading software tools used were Delmia Automation and Tecnomatix. The PLC software was also essential; in this case, we used Allen Bradley, and the software was RsLogix5000. Although the rest of the topics were not considered, the authors suggest some software for teaching similar topics and objectives not fully covered in this case study in Table 6.

Table 6 Structure of the MSIL course
Full size table

An essential part of a manufacturing course is G-code programming. To do this, CutViewer is a handy virtual tool to implement and to verify machining programs. Another vital part of the Lab course is the machining of complex parts using CAM tools; this topic could be covered extensively with the CAM module of NX, a software used very frequently in the different stages of the product life cycle. Vision-related topics are other subjects taught in the MSIL course.

3.2.3 Laboratory practice design

The laboratory practices were planned and designed to be understandable and easy to follow by the student to accomplish the objectives of each topic. The method employed was deductive, and the professor proposed the experimental procedure, applying simulation techniques to connect the knowledge to a realistic practice situation. Each practice was divided into four sections:

Section 1::

Identify the essential elements of an FMS and operate the manufacturing cell.

Section 2::

Identify the components of an industrial robot and learn about the coordinate system; also, make basic movements with the robot.

Section 3::

Program a basic routine with the industrial robot over six specific points and return it to the home position.

Section 4::

Program the PLC to move a piston forward and backward from the FMS.

The authors designed one practice for each scenario, as shown in Fig. 3 (three for Delmia Automation, two for Process Simulate and one for the Real cell); nonetheless, some sections were shared among the scenarios.

3.2.4 Laboratory practice implementation

Before the laboratory practice implementation, the students were informed about the project research, the tool used to collect data, the characteristics of the scenarios, and the purposes of the practices, among other useful information. Here are all the steps followed to implement the practice (illustrated in Fig. 7):

  1. 1.

    Explanation of the overall structure of the practice.

    1. a.

      Explain the objective of the practice.

    2. b.

      Give an overview of the environment in which the cell is simulated.

    3. c.

      Provide exercise instructions.

    4. d.

      Clarify initial doubts and answer questions.

  2. 2.

    Perform the laboratory practices.

    1. a.

      Carry out Section 1. Fundamentals and operations of an FMS.

    2. b.

      Carry out Section 2. Industrial robot handling and coordinate systems.

    3. c.

      Carry out Section 3. Industrial robot programming.

    4. d.

      Carry out Section 4. PLC Programming.

  3. 3.

    Answer the perception survey of the scenarios.

  4. 4.

    Answer the semi-structured interview.

  5. 5.

    Answer the reflection blog.

Fig. 7
figure 7

Laboratory implementation

Full size image

3.3 Phase 3: evaluation

3.3.1 Definitions of the variables to measure

For educative and research purposes, the performance variables selected to compare the four scenarios were (1) General Understanding, (2) Motivation, (3) Relationship of contents, (4) Difficulty, (5) Debugging, (6) PLC programming environment, (7) Experience Acquired, and (8) Ease of teaching. These variables were the basis for designing the different measuring instruments in this research. These variables are described in Table 7.

Table 7 Variables to evaluate
Full size table

3.3.2 Measurement tools

Four quantitative and qualitative tools were developed. The idea was to have not only numerical data to compare the scenarios but also the opinions and comments of the students for matching, concluding, and showing congruency in the results. The developed tool is described below.

3.3.2.1 Perception survey of the scenarios

The perception survey is the only quantitative tool. Its purpose was to know the perception the students had about the laboratory practices, and the questions were linked directly with the defined variables.

3.3.2.2 Semi-structured interview

This interview was a face-to-face interview where the students answered five questions about their performance during the practice.

3.3.2.3 Observation log of the performance

The instructor used this tool during the implementation of the practice. It was a tool to register observations of the students’ performance, the time required to finish the practice, common problems faced by the students, and the attitude they displayed.

3.3.2.4 Reflection blog

The reflection blog was a homework activity done by the students. The tool was designed to gather information on:

  • The expectations of the students.

  • The issues they faced during the practice.

  • The way they solved the issues.

  • Moments in which they lost motivation.

3.3.3 Group classification

The implementation was done in two different semesters. A total of 47 students participated in the implementation of the hybrid scenarios using the Delmia Automation development, and 24 students in the second stage using Process Simulate. All of the undergraduate students were studying for a bachelor’s in mechatronics. Other majors can take this class, but in this instance, they were all mechatronic engineers. The learning styles were organized into different control groups. These learning styles were (1) kinesthetic, (2) kinesthetic/visual, (3) visual, and (4) auditory.

Table 8 presents the comparison, showing the survey results by semester, the software used for the scenarios, and the number of students in each learning style. The scale used went from one to five, where one represents the best and five the worst.

Table 8 Group division by semester and by learning style and scenario
Full size table

3.3.4 Analysis results

The results of the perception survey are shown in Table 9. Several questions related to each variable were asked. The average of each variable is shown in Table 9. The scale used to measure the variables goes from 1 to 5, where 1 represents the best and 5 the worst.

Table 9 Scenarios evaluation
Full size table

The reliability of the previous survey was measured by the method of split-halves. It calculates the correlation coefficient between the scores obtained in each of the halves of the test. If the proof is reliable, then the scores of both halves must strongly correlate. In this case, the Cronbach’s alpha was used to measure reliability. This coefficient varies between 0 (no reliability) and 1 (perfect reliability). The test was done for the sample as a whole. The Cronbach’s alpha coefficients for each half of the test gives results of 0.865 and 0.696; this means the reliability for each half is high. The correlation between both halves is 0.729; this also represents a high correlation, which means that both halves are not independent, and they are oriented in the same direction. The Spearman–Brown coefficient and the Guttman split-half Coefficient also suggest that the reliability of proof is high. These statistics are presented in Table 10. The authors used SPSS for statistical analysis.

Table 10 Reliability of the proof
Full size table

Based on the previous results as well as the comments obtained from the qualitative tool, we reach the following conclusions:

General understanding: It is better obtained when students are immersed in a real scenario and in contact with the devices. Scenarios with a high level of virtualization complicate the understanding of the topics; however, virtualization is useful to understand the issues related to robotics.

Motivation: On one hand, scenarios 3 and 4 inspire the students more to attend the lessons, but they clearly articulated that they enjoy studying solo better. This is a circumstance that is not feasible in standard lessons because they have to share equipment with others. On the other hand, students like scenarios 1 and 2 better, because they are more personalized.

Relationship of contents: In a non-hybrid case, students tend to understand the content more quickly. Combining reality and virtualization generally makes interpretation of the material more challenging.

Difficulty: Scenarios that have a higher degree of virtualization are easier to manage, helped by software that produces more ease, so scenarios 1 and 2 have this benefit over 3 and 4; however, we explicitly advise the students to pass a programming course before enrolling in this course.

Debugging: Scenarios 3 and 4 allow the students to be comfortable about making mistakes, an unexpected finding; however, this is because the students already had experience working with the equipment.

PLC programming: This is a critical aspect because it defines the scenario. Students prefer situations in a broader PLC setting (like scenarios 2 and 4) rather than scenarios 1 or 3, enjoying a more constrained environment created by the DM software.

The experience acquired: This variable is differentiated between scenarios 3, 4, and 1, 2. The more realistic scenarios tend to provide the students better learning; however, we must point out that, shortly, the use of DM software will be as important as knowing how to manage an actual robot.

Ease of teaching: Scenarios 3 and 4 are easier to teach than the virtual ones.

4 Conclusions

The use of DM and technological tools was explored in-depth, so the virtual commissioning of a flexible manufacturing cell was achieved successfully. The authors demonstrated the benefits, scopes, and applications that these tools can bring into the educative field. Virtual commissioning is beneficial in terms of economy, ease, speed, and availability.

We have developed a new teaching platform using a systematic approach that blends instructional planning with the integration of technology; this seems to be an ideal way to replace the traditional teaching scenario or complement it using these tools to provide students new ways to incorporate knowledge into their curricula.

The discussion suggested that students found these implementations beneficial and useful because it helped them to understand topics easier after the interaction with the system. They could grasp some fundamental teaching issues uniquely through modeling and simulations. Also, they found more significant opportunities to experience the whole practical process of an FMS; this enhanced their perceptual experiences. It allowed the teacher to deal simultaneously with a large number of students and increase practical teaching. The concept presented offers excellent potential for the Industry by providing students’ knowledge of technological tools requested in Industry 4.0 concept.

5 Future research work

Next, we present new lines of research which may improve our work.

  • Developing customized industrial applications to train operators for real situations so they can apply the training in the plant.

  • Developing a more in-depth evaluation, implementing the scenarios in academic courses. Students taking classes in a virtual environment vs. other students receiving the traditional method, so that the impact on the academic performance of the students in the two groups can be compared by their learning outcomes (scores).

  • Complementing the current educational platforms used in the teaching of Manufacturing Processes Automation with DM tools, mainly in scenarios 1 and 2. As we can see from the results, the implementation of DM tools as a complement to the traditional courses taught is an outstanding innovation that students can appreciate very much.

  • Developing teleoperated applications using an IP connection. In this project, the OPC server was hosted on the same computer where the DM tools were installed. However, the OPC server can be hosted on another server reached via an IP connection.

  • Using Collaborative Robots: The openness of the “cobots” to all the parameters and the ease of connectivity can bring a comprehensive solution to the implementation of the scenarios.

  • Improving the case study presented in this research by adding more features. This work presents a virtual environment built at a device/machine level; that is, no ergonomics, collision analyses, or security measures were added to the process. Thus, these conditions could be added to the already created environment to provide a more realistic approach.