DEVELOPMENT OF VIRTUAL REALITY MACHINES TO SUPPORT TRAINING IN AUTOMATION
TESIS
MAESTRIA EN CIENCIAS CON ESPECIALIDAD EN INGENIERÍA ELECTRÓNICA (SISTEMAS ELECTRÓNICOS)
INSTITUTO TECNOLÓGICO Y DE ESTUDIOS SUPERIORES DE MONTERREY
POR:
IEC ALFREDO RAFAEL IZAGUIRRE ALEGRIA
MAYO 2011
DEVELOPMENT OF VIRTUAL REALITY MACHINES TO SUPPORT TRAINING IN AUTOMATION
TESIS
MAESTRIA EN CIENCIAS CON ESPECIALIDAD EN INGENIERÍA ELECTRÓNICA (SISTEMAS ELECTRÓNICOS)
INSTITUTO TECNOLÓGICO Y DE ESTUDIOS SUPERIORES DE MONTERREY
POR:
IEC ALFREDO RAFAEL IZAGUIRRE ALEGRIA
MAYO 2011
DEVELOPMENT OF VIRTUAL REALITY MACHINES TO SUPPORT TRAINING IN AUTOMATION
Por:
IEC ALFREDO RAFAEL IZAGUIRRE ALEGRIA
TESIS
Presentada a la División de Mecatrónica y Tecnologías de Información
Este trabajo es requisito parcial para obtener el grado académico de Maestro en Ciencias con Especialidad en Ingeniería Electrónica
(Sistemas Electrónicos)
INSTITUTO TECNOLÓGICO Y DE ESTUDIOS SUPERIORES DE MONTERREY
Monterrey, N.L. Mayo de 2011
Dedications.
To my beloved Yessi, my eternal companion, my internal engine, my inspiration and the main reason of all my effort. You have supported, understood, and given me the opportunity to share with you this hard but excellent stage in my life.
To my parents, Ariel y Patricia, from whom I have always received the best advice and have accepted and supported my decisions and their consequences.
To my little sister, Areli, whom unknowingly has encouraged me to try to be an example.
IV
Acknowledgements.
To all my partners, who have been involved in this project: Fernando, Ernesto Guridi, Luis, Aldo, Aldo Ariel, Victor Hugo, Erick and many others, without your effort this work wouldn´t have been possible.
To the Dr. Manuel Eduardo Macias, who with his leadership has made grew up this project and who gave me the opportunity of work in it.
To the MSC Ruben Treviño, whose advice and orientation encouraged me for enrolling me in this Master.
To my partners from CCR Julio and Esteban, who have supported and understood the occasions when I have to study.
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ABSTRACT.
Current market requirements in industrial sector have motivated the development and adoption of digital manufacturing software tools for control systems design, training, and process optimization to validate and ensure the production system´s programming control and automation equipment. This practice, known as Virtual Commissioning, emulates the real process behavior in a computer software environment. This technology represents an opportunity for education where the virtual emulation of real processes can be used to equip Control and Automation laboratories where students can test, validate, and debug their control and automation strategies, contributing to student formation and solving the need of having costly, real industrial machinery to reinforce the understanding of classroom theory, with practice. This is an excellent option for universities without enough resources, mainly in developing countries, where laboratories are commonly equipped with improvised homemade systems that don´t represent what students will face in an industrial environment. The development of a general procedure of 6 stages (3D CAD creation, Conversion process, Assembly process, Animation process and VRM Validation process) for the creation of an automation simulation virtual final application called Virtual Reality Machine (VRM) is proposed. This application is oriented to automation and control training by it usage for automation and control laboratory equipping. Whose features of performance and free licensing make possible that VRM can be applied to education.
Where can contribute to student’s formation and can be adopted even for low resource educational institutions. Finally an equipping solution for automation and control laboratories is proposed. VRM capabilities, scope, usage, and contribution to student’s formation are presented.
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INDEX
Chapter 1 Introduction………... 1
1.1. Automation simulation software tools in the market……..……….. 11
1.2. Objectives………. 15
1.3. Justification………... 15
1.4. Statement of Problem………... 19
1.5. Features for Virtual Commissioning in Education………... 20
1.6. The VRM (Virtual Reality Machine) Concept……..…... 22
1.7. General Procedure of the Development of the VRM..…..………... 23
Chapter 2 Solid Creation……….. 24
2.1. Considerations for solid creation………... 24
2.2. Part and Assembly origin……….. 28
Chapter 3 Conversion Process……….. 30
3.1. 3D Solid rendering in LabVIEW……….. 30
3.2. 3D Solid Creation in LabVIEW………... 43
3.3. VRML Data Processing………...…. 49
3.4. VRML to VME Converter……… 54
3.5. VME Classification……….. 56
Chapter4 Assembly Process………. 61
4.1. LabVIEW Assembly………. 61
4.2. Assembler………. 70
4.3. Builder……….. 85
4.4. Assembler advantages and disadvantages………..……….. 90
Chapter 5 Animation Process………... 92
5.1. Animation Orientation……….. 92
5.2. VMM Example………. 98
5.3. Programming of sending signals…..……….... 100
5.4. PLC and VRM signals communication……….………... 110
Chapter 6 Connection Process……….. 112
6.1. VRM Connection……….. 113
6.2. Identification………. 114
6.3. Addressing……….... 114
Chapter 7 VRM Validation Process………... 120
Chapter 8 Application Usage……… 122
8.1. Communications Protocols handled for the VRM………... 123
8.2. Automation equipping proposal………... 128
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8.3. VRM usage description……….………... 129
8.4. VRM Communication……….. 131
8.5. Pin Out Table……… 133
8.6. VRM Programming……….. 137
8.7. User interaction with the VRM……….... 138
8.8. Impact of VRM in education……… 139
Chapter 9 Conclusions……….. 140
9.1. Benefits………. 142
9.2. Final comments………. 145
9.3. Future Work……….. 147
Bibliography………... 148
Appendix A Automation Simulation software tools in market………. 150
Appendix B 3D Solid Creation in LabVIEW……….. 172
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LIST OF FIGURES
Figure 1.1. General procedure for VRM developing………... 23
Figure 2.1. SolidWorks principal screen………. 24
Figure 2.2. CAD file general tree and solids quantity affected………... 25
Figure 2.3. a) One solid for every rectangle. b) Four rectangles are grouped in one….. 26
Figure 2.4. Color addition in CAD creation……… 26
Figure 2.5. a) Individual CAD parts. b) Final assembly of individual CAD parts…….. 27
Figure 2.6. Correct origin definition in CAD software.……….. 28
Figure 2.7. VRML 97 Exportation……….………. 29
Figure 3.1. 3D solid rendered in LabVIEW front panel.………. 30
Figure 3.2. LabVIEW Scene Window……….……… 31
Figure 3.3. LabVIEW programming to generate Scene Window………... 31
Figure 3.4. LabVIEW pallet for file loading.……….. 32
Figure 3.5. VRML Blue Pentagon load in LabVIEW……….……… 35
Figure 3.6. Blue Pentagon VRML code…….………. 36
Figure 3.7. LabVIEW code for importing VRML file.………... 37
Figure 3.8. VRML file rendered in LabVIEW………….………... 37
Figure 3.9. LabVIEW code for STL load.………... 39
Figure 3.10. STL file rendered in LabVIEW………….……….. 39
Figure 3.11. LabVIEW code for importing and rendering ASE file.……….. 42
Figure 3.12. ASE file rendered in LabVIEW.………. 42
Figure 3.13. 3D Picture Control Pallet Geometries.……… 43
Figure 3.14. a) Pentagon created using Mesh, b) LabVIEW mesh programming…….. 44
Figure 3.15. Draw mode options for mesh……….. 45
Figure 3.16. Triangles option drawing……… 45
Figure 3.17. Mesh gotten with triangles option drawing……… 45
Figure 3.18. X, Y, Z Vertex array cluster.………... 46
Figure 3.19. Index array……….. 46
Figure 3.20. RGBA color array cluster……… 46
Figure 3.21. Color mode……….. 47
Figure 3.22. Color applied with Binding Off option………... 47
Figure 3.23. Normal mode………... 48
Figure 3.24. S & T coordinates Texture Array……… 49
Figure 3.25. One solid saved as VRML file……… 52
Figure 3.26. Complete vertexes in VRML……….. 53
Figure 3.27. Organization of Individual Vertexes………... 53
Figure 3.28. Coordinates indexes……… 54
Figure 3.29. Normal values and normal index in VRML file………. 54
Figure 3.30. VRML to VME converter front Panel……… 55
Figure 3.31. Set of arrays and cluster in LabVIEW gotten from VRML……… 55
Figure 3.32. VRML Converter options description……… 56
Figure 3.33. VME part programming……….. 57
Figure 3.34. VME assembly programming………. 58
Figure 3.35. VME with desired texture effect………. 59
Figure 3.36. VME with no desired texture effect……… 59
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Figure 4.1. LabVIEW Transformation Operations……….. 62
Figure 4.2. Use of rotational and translational transformation operations……….. 62
Figure 4.3. VMEs used without transformation operations………. 63
Figure 4.4. Scene resulted of rendering VME without transformations……….. 64
Figure 4.5. Applying transformations and animations on the same VME……….. 65
Figure 4.6. Using transformations operations………. 65
Figure 4.7. VMEs in correct (X, Y, Z) coordinate……….. 66
Figure 4.8. Inheritance addition……….. 66
Figure 4.9. Description and movement of mechanisms used……….. 67
Figure 4.10. Description of VME used in rendering scene………. 68
Figure 4.11. Structure needed for correct inheritance and mechanism functionality….. 69
Figure 4.12. Example of VME added as children………... 69
Figure 4.13. Main screen of the assembler……….. 72
Figure 4.14. Principal assembler menu………... 72
Figure 4.15. Select Elements option from principal assembler menu………. 73
Figure 4.16. VMEs in Assembler library any one of these can be chosen to be used… 73 Figure 4.17. Selection of some VMEs……… 74
Figure 4.18. Edit Elements option from principal assembler menu……… 74
Figure 4.19. Edit Elements window controls……….. 75
Figure 4.20. Inheritance definition……….. 76
Figure 4.21. Addition of a VME in a new subassembly……….. 77
Figure 4.22. Addition of subassembly to the inheritance……… 77
Figure 4.23. Three assemblies in a inheritance tree……… 78
Figure 4.24. A more elaborated structure of inheritance tree………. 78
Figure 4.25. Assembly edition, and inheritance definition………. 79
Figure 4.26. Inheritance assembly structure for achieve the correct functionality……. 80
Figure 4.27. Build Model option chosen from Assembler principal window ………… 80
Figure 4.28. VMEs are rendered in the scene……….………… 81
Figure 4.29. Process of placing the VMEs in their correct position……… 82
Figure 4.30. Rotation transformations added from assembler front panel……….. 82
Figure 4.31. Usage of Import and export assembler options………... 83
Figure 4.32. Save as VI a option is shown in front panel……… 83
Figure 4.33. Assembly template front panel……… 84
Figure 4.34. Assembly template block diagram...………... 84
Figure 4.35. Constant cluster gotten from assembler……….. 85
Figure 4.36. Builder VI……..………. 85
Figure 4.37. Assembler internal programming “calling VME part.”……….. 86
Figure 4.38. Assembler programming part for placing VME in correct………. 87
Figure 4.39. Assembler programming part used for inheritance definition……… 88
Figure 4.40. VMEs reference gotten and organized in arrays………. 89
Figure 5.1. VRM Elevator front panel and VRM rendering………... 93
Figure 5.2. Name of the Signal Received from the controller………. 94
Figure 5.3. The four axes receive their conditions from the external inputs………….. 95
Figure 5.4. Transformation VI used in Animation process………. 96
Figure 5.5. Internal programming of VMM used in the VRM……… 97
Figure 5.6. VME References indexed from builder.vi……… 98
Figure 5.7. VMMs Identified in Elevator for animation………... 99
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Figure 5.8. VME that moves in the VMM shown………... 100
Figure 5.9. Elevator’s sensors illustration………... 101
Figure 5.10. Elevator’s touching sensors close up……….. 102
Figure 5.11. Description of the touching sensor’s sensing zone………. 103
Figure 5.12. Internal programming of Touching Sensors………... 104
Figure 5.13. LabVIEW Animation programming for touching sensors……….. 104
Figure 5.14. Interaction between VMMs and Virtual Sensors……… 105
Figure 5.15. More kind of sensors used in VRM……… 106
Figure 5.16. Organizations of signals sent for the VRM’s virtual sensors……….. 107
Figure 5.17. Programming for changing color affecting object geometry……….. 108
Figure 5.18. Tire’s rim VME appearing and disappearing……….. 109
Figure 5.19. Usage of culling SceneObject in Figure……….. 109
Figure 5.20. Communication of signals between PLC and VRM………... 111
Figure 6.1. 3 Stories Elevator pin out table………...……….. 113
Figure 6.2. Identification of signals………. 114
Figure 6.3. Names of the connection signals………... 115
Figure 6.4. Identification and addressing of PLC inputs and outputs………. 116
Figure 6.5. Extraction of the individual signals received from the controller…………. 117
Figure 6.6. Organization and structuring of signals that are send to the controller……. 118
Figure 6.7. Communication Library for interconnecting VRM with controller……….. 118
Figure 6.8. As final step a VRM Pin Out table is created………... 119
Figure 8.1. VRM Connection configuration VI……….. 122
Figure 8.2. WinLC interaction in automation network………... 123
Figure 8.3. WinLC Configuration………... 124
Figure 8.4. OSI Model description……….. 125
Figure 8.5. PROFIBUS network……….. 126
Figure 8.6. PLCSIM example ………. 127
Figure 8.7. Automation & Control Laboratory equipping proposed………... 129
Figure 8.8. Real Process Line Machine………... 130
Figure 8.9. VRM Process Line...………. 130
Figure 8.10. VRM Communication diagram………... 131
Figure 8.11. VRM controlled by PLCSIM……….. 132
Figure 8.12. VRM controlled by External PLC………... 132
Figure 8.13. Pin Out table of the VRM “Process Line”……….. 133
Figure 8.14. Description of Inputs & Outputs of the VRM machining center………… 134
Figure 8.15. Description of Inputs & Outputs of the turning table………. 134
Figure 8.16. Description of Inputs & Outputs of the pusher table……….. 135
Figure 8.17. Description of Inputs & Outputs of the conveyor 2……… 135
Figure 8.18. Description of Inputs & Outputs of the conveyor 1……… 136
Figure 8.19. Description of Inputs & Outputs of the conveyor 3……… 136
Figure 8.20. VRM’s Pusher X+ & X- Ladder logic programming………. 137
Figure 8.21. VRM’s Interaction with Students……… 138
Figure 8.22. VRM Automation education impact…...……… 139
Figure A.1. Delmia Automation application………... 153
Figure A.2. eM-PLC Communication diagram………... 154
Figure A.3. SIMIT SCE example, a 2D animation is controlled with PLCSIM………. 156
Figure A.4. Example of RSTestStand by Rockwell……… 157
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Figure A.5. 2D Tank simulation created in Unity Pro……….……… 158
Figure A.6. Tank simulation Unity Pro creation……….……… 158
Figure A.7. EasyPort interface for connecting EasyVeep with PLC.……….. 160
Figure A.8. EasyPort interface for connecting EasyVeep with PLC.……….. 160
Figure A.9. Processing Station available in Cosimir PLC………….………. 161
Figure A.10. Wonderware InControl example………….………... 162
Figure A.11. Transport Band created in InTouch……….………... 163
Figure A.12. Tarakos Software scene representation……….. 164
Figure A.13. Combination of I/O cards for controlling external devices……… 164
Figure A.14. Machines included in EasyPLC demo version built with the machine….. 166
Figure A.15. Virtual Plants built in SPS-VIS….……….……… 167
Figure A.16. Exercise created in ProSIM-II……… 168
Figure B.1. 3D Picture Control Pallet Geometries……….. 173
Figure B.2. Geometry cone rendered in LabVIEW………. 173
Figure B.3. Geometry cylinder rendered in LabVIEW……….. 174
Figure B.4. Geometry box rendered in LabVIEW……….. 174
Figure B.5. Geometry sphere rendered in LabVIEW……….. 175
Figure B.6. Text added to Box Geometry………... 175
Figure B.7. a) Image in 2D, b) 3D high field gotten from 2D image……….. 176
Figure B.8. Blue pentagon created using Mesh………... 177
Figure B.9. Draw mode options for mesh………... 178
Figure B.10. Points option for drawing Scene Mesh……….. 178
Figure B.11. Lines option Drawing. ………...……… 178
Figure B.12. Line strip option drawing………... 178
Figure B.13. Mesh gotten with lines strip option drawing……….. 179
Figure B.14. Lines loop option drawing……….. 179
Figure B.15. Mesh gotten with lines loop option drawing……….. 179
Figure B.16. Triangles option drawing……… 180
Figure B.17. Mesh gotten with triangles option drawing……… 180
Figure B.18. Triangles strip option drawing……… 180
Figure B.19. Mesh gotten with triangles strip option drawing……… 181
Figure B.20. Triangles fan option drawing……….. 181
Figure B.21. Mesh gotten with triangles fan option drawing……….. 181
Figure B.22. Quads option drawing………. 182
Figure B.23. Mesh gotten with Quads option drawing……… 182
Figure B.24. Quads strip option drawing……… 182
Figure B.25. Mesh gotten with Quads strip option drawing………... 182
Figure B.26. Polygon option drawing………. 183
Figure B.27. Mesh gotten with Polygon option drawing……… 183
Figure B.28. X, Y, Z Vertex array cluster………... 183
Figure B.29. Index array……….. 183
Figure B.30. RGBA color array cluster………... 184
Figure B.31. Color mode………. 184
Figure B.32. Color applied with Binding Off option……….………. 185
Figure B.33. Normal Array……….. 185
Figure B.34. Normal mode……….. 186
Figure B.35. S & T coordinates Texture Array………... 186
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LIST OF TABLES
Table 1. Principal Features of Automation Simulation tools in market……….. 14
Table 3.1. VRML Design Constraints………. 33
Table 3.2. VRML file section descriptions……….. 52
Table 4.1. Assembler Advantages………... 90
Table 4.2. Assembler Disadvantages………... 80
Table 9.1. Principal Features of final applications including VRM……… 141
Table 9.2. VRM Advantages and disadvantages………. 142
Table A.1. Principal Features of Automation Simulation tools in market……….. 171
Table A.2. Principal Features of final applications developed in Automation Simulation tools in market……….. 172
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CHAPTER 1
Introduction
The consumption world market is constantly increasing its requirements. Day by day, better quality products are asked by customers that shrink product life cycles, increase product variant and reduce product launch times. Moreover this happens while market prices erode. These requirements by dynamic customer represent challenges that manufacturers have had to face in a highly competitive environment. New requirements force companies, either globally or locally, to implement technologies, processes, and practices that enhance their competiveness, increase their profit, give them arguments to compete, make them more efficient, position their products as the clients’ favorites and differentiate them from the others.
Some of the practices that companies implement daily with the purpose of increasing their competitiveness advantages are product improvement, constant change of product offer, process standardization, price reduction, flexible process, optimization process, quality programs, etc. However, some of these practices appear to be opposing; on one hand, a fresh product offering requires constant changes in the production lines to reduce costs as opposed to the standardization process. To remain successful in the market and even to survive, companies must be able to innovate constantly. Innovation must be oriented to look for the necessary practices and tools that help the company face these challenges and assure that the changes made in any production matter to improve some sector in the company impacts is carried out in a planned way.
On the other hand, the globalized market has forced companies to focus their production on scale economies in order to offer their products around the world increasing its production. Higher production quotes have motivated that companies open new plants around the world, increase their production lines and change or totally replace their processes and the way they manufacture goods. This growth has brought a bigger organization structure inside companies and more processes to control and manage. In addition, companies have adopted practices like lean manufacturing, Six Sigma, QFD, ISO quality certifications and other quality production activities required for markets or added to manufacturers’ standards. These new challenges demand from companies to handle more specifications for products and more information about processes and procedures.
To solve these needs in manufacturing, many software tools have been developed to focus on different sectors and scopes. Examples of these are CAD/CAM (Computer Aided Design / Computer Aided Manufacturing), CAPP (Computer Aided Process Planning), and PPC (Production Planning and Control). These software tools are integrated in a complete software business-manufacturing suite known as PLM (Product Lean Manufacturing). The PLM objective is to enable companies to achieve the business imperative of timely and cost effective product launches. Within the manufacturing environment, these tools are known
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as Digital Manufacturing tools (DM) which merge the virtual and the physical manufacturing worlds.
Digital Manufacturing (DM).
Digital Manufacturing is an integrated suite of software solutions that supports manufacturing process design, tool design, and visualization through powerful 3D virtual simulation tools1. These tools allow the manufacturing engineer to validate and optimize the manufacturing processes through visualization: design, synchronization and validation of production lines, robotic work cells, machine centers, production equipment, control systems functionality and requirements. All these functions are completely carried out before purchasing, installing, and commissioning the physical equipment. In essence, Digital Manufacturing facilitates the complete view of the product and the process design as integral components of the overall product life-cycle and enables product design methods to be not only sensitive to process constraints and capabilities, but, in fact, be completely integrated with the manufacturing processes.
Applications developed in DM tools make it possible for the manufacturing engineer to design and virtually simulate exact models of machines, robots, conveyor lines, work cells, and practically any production equipment. These models are usually required to fabricate, assemble, and install parts, sub-assemblies, and components of the product. The factory environment of the production process can also be modeled including buildings, production lines, transportation, workflow, and other facilities that represent the complete physical production environment.
The immediate effects and benefits for manufacturers that use DM tools are as follows: a substantial reduction of the manufacturing lifecycle in regards to product launch time, assurance of production changes impact, testing of different production scenarios and significant cost savings1. All this is done by the virtual validation and commissioning of the production systems. Virtual validation and commissioning have become more important lately since the demand for larger production and trustworthy processes have transformed the machinery used for production. Machinery transformation is oriented to the automation of complex manufacturing systems, replacing traditional assembly processes. Due to this, DM allows manufacturing engineers to merge virtual models of production equipment with automation and control. DM enables the complete validation of the control logic, automation strategies and HMI functionality in the Automation Simulation process. This DM extended level of manufacturing process design and execution capability helps manufacturers vie in an intensely competitive, global environment1. DM enables companies to execute “flawless” launches, execute production changes by totally validating all matters concerned with the process from the tool to machine design to the final automation strategy.
Automation Simulation.
A highly competitive environment and important market demands require that the manufacturing production processes be fast, reliable, and keep high standards of repeatability and accuracy. Other aspect to consider is that to keep scale economies,
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manufacturers need larger production quotas which translate in an increase of machinery usage time. Nowadays, it is very common that manufacturing cells work 24 hours per day trying to meet production quotas. High competition in the market and higher production quotas have motivated that day by day traditional manufacturing processes be replaced more frequently by complete automated systems. The usage of automated systems reduces human errors, carries more advanced task out, increases processes accuracy and production output and has hardware and software specially designed for working non-stop, long laps.
The largest manufacturing companies that first introduced this trend in production automation systems were those in the automotive sector, such as General Motors Chrysler (GMC), Volkswagen, and Ford. Today their production lines are mainly conformed by automated machinery and sets of robots that work totally synchronized with conveyors, presses, furnaces, CNC machines, etc. This complete automation production system is integrated to other management production systems to receive feedback and production information2. This trend in automation is supported by software and hardware. Constant technology advances in robotics and the automation field have made it possible to have production automation available for medium and small manufacturing companies too.
The present automated systems go from the simplest to the most complex. Some of them can be formed by single-process automated manufacturing cells as a basis. A set of these are grouped in automated production lines which at the same time are grouped in production systems. These systems can be made up only by machines or may include an individual or a set of industrial robots requiring a minimum or no human interaction when working. These are controlled by autonomous programmable controllers that handle electrical process inputs and outputs and implement the necessary logic and calculus to control them. From the variety of automated industrial processes, even single automated manufacturing cells require automation and the implementation of a control strategy. The complexity of process automation depends on the process. For example, automation complexity increases in automated production lines and is even more complex in automated production systems that commonly require complete network architecture of control devices. In these networks, control devices communicate with each other receiving process signals that come in from machine sensors and send out signals to machine actuators that are part of the process. In addition, communication signals with high or low level controllers inside the network are also received and sent out. Then, despite these, automated systems require a minimum of human interaction when working. During initial commissioning, the sending and receiving of communication data, the recognition of sensors signals and the sending of signals to actuators when the process is being automated have to be programmed in control devices. In addition, when failure or process variation – such as product change, addition or replacement of machines, changes in automation hardware, software upgrades, etc. occurs, it is also necessary to change the programmed automation logic. Human interaction is thus needed for these tasks since the programming is done by control and automation engineers or by staff with special training, knowledgeable of the process, the control hardware and the programming software.
The trend in automation systems has turned into a key piece for companies to control the system programming and implement parts. Changes in automation systems must be designed, installed, and deployed in the shortest possible time and must assure certain
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requirements, for instance: that the optimized control program causes no accidents, that the launching time and deadline for implementation are the shortest and do not affect production, and even more important, that the production system works correctly regardless of how easy or difficult the control program handling the production changes is.
Automation is supported by an extended variety of autonomous programmable controllers available in the market. PC-Based controllers, microcontrollers, microprocessors, programmable automation controllers (PAC), programmable logic controllers (PLC) are present. From these, PLCs have the major presence in the industry because of their roughness and wide variety of hardware configurations and brands in the market. PLCs may be used in small and simple or large and complex production systems.
Many of them have the capability of network interconnection. The constant usage of PLC in the manufacturing sector has motivated further advances in the PLC hardware and software communication. For example, the addition of Industrial Ethernet and the adoption of OPC has become a standard of data communication between control devices from different manufacturers. Communication advances have made it possible that PLCs from different brands communicate with each other or with Personal Computers (PC), situation that years ago was impossible due to the way brands handled their communication protocols. These advances have fostered the evolution of PLCs, from those used in a single process as the implementation of a standalone to becoming a part of an industrial network in which information is shared among control devices from different brands.
With the growth of automated production systems, manufacturing companies have the need to assure the changes in the production systems by validating that the control automation hardware and control system strategies implemented on these work correctly and are in accordance to standards. This need, along with communication with PLCs and interconnection capability with PCs and other computing devices motivated the development of a segment inside Digital Manufacturing tools, the automated simulation, which focuses on covering and solving manufacturing needs.
Automation simulation is the process of modeling, evaluating, optimizing, and validating control systems for automation equipment and systems in a virtual environment.
This environment tries to emulate a manufacturing work cell with 2D image or 3D solids elaborated in CAD software. A virtual environment may represent from single processes with primitive graphs to elaborated production systems. It may include multiple robots, complex tooling and fixtures, clamp automation, PLCs, etc. With these environments, it is intended to mimic and simulate the behavior of real processes in automated ones by means of animation sequences. The objective of automation simulation is to virtually simulate, validate, commission and debug total or partial control logic changes or implement the entire manufacturing work cell. This validation can be carried out even weeks or months before the real machinery is present in the shop floor. The control logic strategy is simulated in virtual cells in which interaction and control sequences of tooling, robots, clamps, safety devices and electrical hydraulics and pneumatics components can be tested.
In addition, automation simulation makes it possible to validate actions in case a failure occurs. This practice is known as virtual commissioning and it helps to assure that any change in an automation system will have no impact in the desired production. It also
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provides manufacturing and control engineers an opportunity to ensure the control design before production starts.
Virtual Commissioning.
Virtual commissioning is state-of-the-art technology on digital manufacturing simulation. It is based on advanced simulation methods which truly represent the merging of 3D virtual simulation environments with the physical automation world of control logic and control platforms to accomplish the required level of automation and synchronization.
This is carried out on virtual prototypes of production systems and equipment which are based on the capabilities and appearance of a real model. Virtual Commissioning was originally intended to allow the debugging of the control code on an actual Programmable Logic Controller (PLC) that would be on the shop floor, weeks or months before the integration of all the devices. These devices could be tooling, robots, clamps, safety devices, electrical, hydraulics and pneumatics components. However, the virtual commissioning scope goes beyond; it has turned itself into an important business enabler.
Ideally, it allows the user to optimize and validate costs efficiently and effectively; and validate any implementation or change of strategies in the manufacturing process controls.
Virtual commissioning makes it possible to test different control scenarios, to accelerate the learning curve and to enable control engineers to reduce the occurrence of costly errors, mitigating the risks in a virtual environment well before using real equipment to accomplish commissioning3.
Automation simulation is a DM tool based on virtual commissioning. Its principal objective is to serve manufacturers as a tool to enhance their competitive advantages in the global market. Moreover, in addition to virtual commissioning, the feature of experimenting different control strategies in virtual production environments without the risk of costly errors has caused that automation simulation is also used for training purposes; beginning control engineers who will be responsible for programming control logic process strategy in specific machinery, work cells or production lines can be trained.
When the virtual commissioning occurs, learning can take place on virtual environments without the risk of damaging the equipment physically. Additionally, more experiment control engineers can be trained, especially when new machinery or new automation equipment will be used.
Inside the field of industry training, the applications of automation simulation were created with virtual commissioning and are being used more frequently to give training to workers, since they are the ones operating the machines on a daily basis. For example, some training is needed when a product or the production flow is changed, or even, when new processes requiring new machinery are going to be implemented. The workers can be trained, months or weeks before the real machinery arrives in the shop floor. A virtual process mimics and emulates the functionality of new machinery to which an external control device can be connected. In this way, virtual commissioning shortens the ramp up learning and the starting up of new products. In addition, the capability that a virtual process gives to test different environments on the same virtual model assures that the worker will know what happens when a human mistake is made and avoids the danger of an injury to a worker or damaging the machine hardware.
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Virtual commissioning is intended to validate control strategies on virtual production system environments, and then to move these to the real production system.
However, this movement is not extremely necessary since the validation of control strategy can be done anyway even if the virtual commissioning stops in the virtual environment. In other words, if real machinery will not be used, the automation simulation is useful to validate the programming of the control engineers as the necessary knowledge to control the virtual production system, just as the real one3. This is similar to the training of industry control engineers, but it has a different orientation. Thus, automation simulation can be used with didactic objectives not only to train someone to use specific machinery but also as a teaching tool in automation and control courses. The virtual environments developed on automation simulation tools offer a control process in which students can observe the right or wrong functioning of their control program. This way, automation simulation supported by virtual commissioning is used in the industry and for educational purposes. In the former it has two orientations: the optimization and validation of production systems, and the training of control engineers and workers. In the latter, it is used in control and automation courses in which final applications are used to support the education and practice of engineering school students.
Automation Simulation for Optimization and Validation.
With PLM tools, manufacturing engineers are able to create a virtual manufacturing environment through powerful simulation applications. On this environment, they can develop and design products, plant layouts, production lines, work cells, material work flow, automation designs, and any production process. When the virtual environment that represents the plant is created, engineers are able to generate process plans, work steps, assembly definition and sequencing, and tool design. They can even generate a control code. Then, PLM makes it possible to deliver a product design that allows manufacturability. This is done by simulating the manufacturing processes of virtual products on virtual production environments early in the product design process. This capability allows a better product design while optimizing the production process.
For manufacturing companies, the amount of time it takes to deploy, install, and commission new production lines for general assembly, painting, stamping, body-in-white, and other assembly systems, and to bring all these systems up to the production stage is much extended. Not only do new lines require time for starting up, but also the production lines or product changes need time for planning, execution, and validation. Changes in working production lines are more critical because these impact directly in the production ratio of the factory. Since the goal is to reduce the cost for launching new models, both time and resources have to be controlled while still satisfying the requirements of the production lines and the delivery of the new products on schedule. To assure these critical factors with the PLM tools, manufacturing companies are using automation simulation technology more often.
The virtual 3D world is created by automation simulation technologies of solid model products, digital mockup, and manufacturing process. Virtual commissioning goes beyond only simulating the behavior of production systems. Its objective is to assure a
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control strategy and an automation program that can be loaded to real automation hardware controlling the real production systems. Additionally, it links the actual production work environment through the connection with the machine control systems. One thing is modeling and simulating the machine tool, the conveyor line, the robotic work cell, and the pneumatics and hydraulics systems, but quite another is generating accurate information capable of running the control systems correctly for all the production equipment.
Automation simulation gives production operations engineers the capability to build virtual production systems based on real automation events2. It also makes it feasible for engineers to virtually model conveyors, workstations, and controls as well as the right physical and logical interface and material handling operations that can occur between the components of work cells and production lines. An important feature is that it permits the development of control strategies or the construction of production scenarios for experimentation that would otherwise be expensive and/or time-consuming. This empowers engineers to try ideas in a dynamic, synthetic environment while collecting virtual response data to determine the physical responses of the control system. This feature, in addition to validation, provides a collaborative workspace for mechanical design, manufacturing, and control engineers to share knowledge, exchange system features and attributes, integrate process information, and react to engineering changes and version updates. The collaborative work around a virtual model shortens the ramp-up of production lines during commissioning and product launch, as well as the designing/building process, cost, time, design changes, and risk of errors. All this facility aspects represent critical factors in product delivery and, ultimately, a company’s profit or loss. These capabilities have made of automation simulation a key piece for the manufacturing industry since manufacturers have validated the plant’s control systems before production starts4.
Engineers typically find over 100 mechanical and electrical errors in logic, HMI, and drawings per cell2. Two to three man weeks are saved during startup, saving thousands of dollars in engineering and production labor costs. Problems are normally found and fixed with minimal disruption to operations. Problems found in the field are solved more quickly since engineers can narrow them down to items such as physical connections, confident that the validated control code works. Scenarios not expected during startup can be simulated and corrected beforehand. All faulty conditions of the process can be tested, and all HMI interfaces will perform as expected at startup and workers will be familiarized with these provided they were previously trained on virtual models.
Companies like GM, Airbus, and DaimlerChrysler have introduced new models targeted to niche markets resulting in very limited production runs (20-30K) that require timely and efficient model launches if the car maker is to make any profit. Production lines, work cells, and control systems must be designed, installed, and deployed in the shortest possible time and, even more importantly, work correctly with the least amount of testing and validation. Digital Manufacturing tools are essential in designing and implementing these agile production systems. Initial results show savings between two and three man- weeks during startups, smoother product accelerations, and improved initial quality and productivity. Virtual commissioning thus provides a consistent global process for validating control logic. Nevertheless, automation simulation is not only useful for this kind of
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companies. Manufacturers producing models in large scale can also benefit of this software technology, which will be explained next.
Automation Simulation Advantage for the Industry.
Pre-validation of control and production systems before production starts. It is possible to validate electrical and mechanical features in the engineering design phase, enabling acceleration of production. In addition, since the production system starts out working as it is supposed to, the product’s initial quality is improved.
Debug time minimized at the plant. Almost all electrical and mechanical designs can be validated by virtual commissioning; then most of the validation left to do at the plant is narrowed down to checking physical connections and software interfaces. This leads to significant reduction in production launch work force at the plant, engineering, direct labor, and travel costs of experts.
Validation of production commissioning on a virtual environment. Evaluating PLC program changes on the virtual model instead of taking risks on the real equipment minimizes production risks by simulating several manufacturing scenarios and allows validation of mechanical and electrical components to be integrated in the production processes (PLC and robotics).
Visualization and optimization of the process functionality and behavior. Processes can be run by the PLC code prior to integrating them to the production engineering phase.
This increases the speed, consistency and reliability of design processes, achieving a significant reduction in risk and start-up time since it is possible to detect logic errors well before ramping-up.
Testing of conditions before production starts. Automation simulation enables engineers to iterate quickly through practically any scenario validating as many “what ifs”
as needed and fully debug the control test production and failure conditions. In addition, validation of all diagnostic codes can be done away from the shop floor.
Standardization of validation processes to be used globally. An automation simulation capability helps reinforce common validation processes throughout the company. This allows mechanical design and control departments to work concurrently by sharing manufacturing information and proving the feasibility of the production cell and its time cycle.
Automation simulation in Training.
As previously mentioned, automation simulation is also used in the industry for training purposes. There are two training orientations: for engineers that carry out added value activities in the production systems and for workers who are the final users of the machines. Both orientations are based on virtual commissioning however different the objectives, the scopes, and the ways automation simulation tools could be.
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In training for engineers, usually control or manufacturing ones, automation simulation is used to build virtual environments emulating real production systems that are or will be present on the shop floor. The objective of these training simulators is to serve as a virtual commissioning tool with which engineers gain a proper understanding of the process, test control strategies, observe production systems limitations and scopes, know production times, respond to process changes, learn how the system behaves when a strange condition is present, and know what to do to solve any malfunction. These aspects are principally important for beginning engineers or for new ones in a specific area within the company. Since the virtual environment represents the future production systems in the shop floor or the changes taking place at present, training with automation simulation tools is also useful for experienced engineers unfamiliar with the new production system. Then, once that the virtual environment is built, the training for engineers consists of consecutive virtual commissioning on the same virtual simulation of the production system. This is done for them to get the necessary knowledge from the simulation to understand and control the production systems in the future. Virtual environment built with automation simulation tools can even make it possible that engineers automate more complex systems than those that they have in the shop floor, increasing their expertise and automation knowledge. This can be done because it is not necessary to have the real production system to carry out the automation. Traditionally, this training could have meant a large economic investment of the companies, an expenditure that is not paid with virtual environments developed with automation simulation tools. The availability of these simulators and other virtual commissioning tools are of invaluable help to manufacturing companies throughout the lifecycle of the plant.
Training for workers also uses virtual environments created by automation simulation tools of virtual commissioning. Yet, these emulations are not intended for constant virtual commissioning. This means that control strategies are not validated on these3. The objective is to use a simulation to train workers previously developed and commissioned by control engineers. This is important since workers constantly operate the machines and use the real production systems on the shop floor. The workers in manufacturing companies receive training when they are hired, when they are moved to a different production process, when new machinery will be used or when a product change has been planned.
Manufacturers constantly draw on to some of these situations, situation which requires the worker to be frequently retrained. When virtual automation simulation is available for operators, the virtual model of the real production system runs on a Personal Computer connected to a real PLC or SoftPLC and to a Human Machine Interface (HMI) similar to the one used in the real machine. The virtual emulation sends signals to the controller which sends them to HMI and receives signals sent from the HMI through the controller. Therefore, operators can observe how the machine emulation reacts to control.
This device is the same as the one they will use in the real machine. In addition, the virtual emulation can illustrate possible human errors during real machine operation. This should reduce the risk of costly machinery damage. Above all, virtual environments assure that the workers know what happens when a human mistake occurs in the machine or production line. These mishaps can be illustrated without endangering the worker’s integrity or the machine hardware. Training simulators play a significant role in reducing the time for a
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plant to go on operation and the learning curve of its workers. It minimizes the risks as it enables the plant operators to perform the tasks needed by their manufacturing employers.
The training can be accomplished with a deep understanding of the functioning and operation of the machine the worker will use. In addition, some of the training data are augmented with recorded narrations and videos that explain the key points of the machine.
With the level of practice that can be achieved with virtual environments, workers can more easily absorb the information both visually and aurally.
Automation Simulation in Education.
Automation simulation software tools are used with two orientations in education.
The first one is their usage principally in universities focusing on control, automation, mechanical, manufacturing and electrical programs. In universities, automation simulation tools are taught principally in advanced engineering courses where students learn about the automation tool itself, its features, limitations, scope, etc. Students work with the development environment and not with final emulations. This is because the teaching is oriented to the automation simulation tool as it is used in the industry and it also requires engineers working with these tools. The most popular automation simulation tools in education are Delmia by Dassault Systemes, Tecnomatix by SIEMENS and Festo. These developing tools are usually expensive and unaffordable for many universities, mainly in those of developing countries.
The second automation simulation orientation consists in using a final emulation application to teach engineering students and to support the automation and control theory taught in classrooms and is later practiced in laboratories. On these virtual environments, a student’s control and automation strategies can be tested in the emulation of real production systems, without the risk of damaging costly equipment. This is done taking advantage of the automation simulation objective of validating, optimizing and testing control and automation strategies in virtual environments. Then, these are deployed in the real production systems equipment. It is also possible to carry out all the automation commissioning process, and never deploy the automation control strategies in a real model, just as the training is done with engineers in the industry.
This way of using automation simulation tools offers the capability that with the knowledge of the electrical, mechanical, and dynamic processes practically any production system, machine, work cell, production line, etc. can be emulated by virtual commissioning. This means that automation simulation makes available a great assortment of virtual emulations in which to practice control and automation strategies. Having this variety of emulations enables students to deploy different control strategies for each one, relating them with different sensors, actuators and machinery functionalities. From this point of view, if automation simulation is well used in education, it opens the opportunity to solve the problems that many universities face in regards to equipping control and automation laboratories. The reason to have a process with which students can practice the knowledge acquired in the classroom is that a real work cell used in the industry is very expensive. Universities can seldom afford to get one. Therefore, the possibility of having a variety of virtual emulations processes with which students increase, reaffirm and practice automation and control skills is an opportunity for higher education institutions to
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c o m ple me nt a n d s u p p ort t heir a ut o mati o n a n d c o ntr ol c o urses, a n d t o i ncr e ase t heir st u d e nts’ k n o wle d g e a n d a bilities.
O n t he ot her ha n d, t he mar k et has a li mit e d offer of fi n al virt u al a p plic ati o ns. T her e a r e o nl y a r e d u c e d n u m b er of c o m p a nies s u c h as Fest o b y Cir os Me c hatr o nics, SI E M E N S b y SI MI T S C E a n d E as y P L C t hat offer 3 D fi n al a p plic ati o ns t o c arr y o ut virt u al c o m missi o ni n g a n d le ar ni n g a ut o mati o n. Pr ol o gi x, a n i n d e p e n d e nt t o ol, offers 2 D fi n al virt u al a p plic ati o ns; ot hers, s u c h as D el mia A ut o mati o n b y D assa ult S yst e mes, T e c n o mati x b y SI E M E N S, R S T est St a n d b y R o c k w ell a n d U nit y Pr o b y S c h nei d er ar e orie nt e d t o pr o vi d e t he s oft w ar e t o ol t o b uil d virt u al a p plic ati o ns a n d n ot t o offer fi n al virt u al e n vir o n me nts wit h w hic h st u d e nts c a n t est a n d vali d at e t heir pr o gr a ms. T he n, fr o m t he re d u c e d n u m b er of ve n d ors i n t he mar k et, t her e is e ve n a m or e r e d u c e d gr o u p t hat offers fi n al a p plic ati o ns f or tr ai ni n g ser vi n g e d u c ati o nal i nstit uti o ns w h ose sc o p e varies.
A p plic ati o n p artic ularities ha ve a d va nt a g es a n d dis a d va nt a g es bet w e e n fi n al virt u al a p plic ati o ns t hat i m p a ct dir e ctl y i n t he st u d e nts’ le ar ni n g le vel. Alt h o u g h t he str o n g i m p a ct that t hese fi n al a p plic ati o ns c a n ha v e i n e d u c ati o n, t hese h a ve n ot be e n br o a dl y use d si n c e the y ar e n ot as p o p ular i n e d u c ati o n as t he y c o ul d be. C o nsi d eri n g t heir sc o p e i n tr ai ni n g, the la c k of p o p ularit y has be e n m oti v at e d mai nl y f or t he p artic ular disa d va nt a g es i n t h e to ols a n d t he s mall n u m b er of o pti o ns i n t he mar k et.
1 . 1 A ut o m ati o n Si m ul ati o n S oft w a r e T o ol s i n t h e M a r k et.
A ut o mati o n si m ulati o n is p r a ctic all y ne w a n d h as be e n e x pl o d e d a n d de v el o p e d o nl y b y a r e d u c e d gr o u p of s oft w ar e ve n d ors. T he s oft w ar e s ol uti o ns pr es e nt i n t he mar k et a r e ver y differ e nt; e a c h wit h its o w n p artic ularities a n d sc o p e4. T he differ e nc es f o u n d c o nc er n mai nl y t o matt ers s u c h as ori gi n, t ar g et se ct or orie nt ati o n, c ost, c o u ntr y of ori gi n, vis u aliz ati o n c a p a bilities, s u p p ort e d c o n ne cti vit y, lic e nsi n g , c o m ple xit y i n us a g e, le vel of int e gr ati o n le vel, pr o gr a m mi n g e n vir o n me nt, p erf or ma n c e, fle xi bilit y, q u alit y of gr a p hs, e t c. T he ori gi n of t he t o ols a vaila ble i n t he mar k et varies si n c e differ e nt c o m p a nies o rie nt at e d t o se ct ors r elat e d t o ma n ufa ct uri n g a n d a ut o mati o n ha ve cr e at e d t hese t o ols;
o t her ha ve be e n d e vel o p e d by a s mall g r o u p of i n di vi du al pr o gr a m mers se e ki n g s p e cifi c p ur p oses. T he ori gi n se e ms t o be cl osel y r elat e d t o t o ol p artic ularities a n d sc o p e. S o me a s p e cts of t he s oft w ar e t o ol d e vel o pi n g c o m p a n y – s u c h as e x p ertise, k n o w- h o w, o bje cti v e, a vaila bilit y of me a ns of pr e vi o us s oft w ar e d e vel o p me nts, pr o d u ct p ortf oli o, et c.- dict at e so me of t he mai n fe at ur es of t he t o ol a n d t her ef ore its sc o p e. T he ori gi n of t he t o ols r efers to t he orie nt ati o n of t he c o m p a n y t hat cr e at e d or c o m mer cializ es t he s oft w ar e t o ol. T her e ar e t hr e e mai n ori gi ns of t he t o ols pr ese nt i n t he mar k et: 1) P L M s oft w ar e t o ols; 2) A ut o mati o n h ar d w ar e a n d/ or s oft w ar e ve n d ors; a n d 3) S oft w ar e t o ols fr o m a t hir d p art y.
P L M s oft w ar e t o ols.
C o m p a nies t hat d e vel o p t his ki n d of t o ols ar e pi o n e ers i n t his fiel d a n d ar e t he o nes that ha ve d e vel o p e d a n d e x pl oit e d m or e t he a ut o mati o n si m ulati o n a n d virt u al c o m missi o ni n g c o nc e pt; t he sc o p e of t hese t o ols h as e xt e n d e d. T hes e ar e t he m ost br o a dl y use d i n t he i n d u str y b y lar g e c o m pa nies s u c h as G M, F or d, Air b us,2 et c. si nc e t he y ar e s u p p ort e d b y a r e n o w ne d c o m pa n y. T her e ar e mai nl y t w o P L M s oft w ar e ve n d ors t hat offer
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automation simulation tools within their product portfolio: Delmia from Dassault Systems powered by IBM and Tecnomatix powered by SIEMENS. From these two with previous experience in CAD/CAM and CAP software, Delmia is the pioneer of this technology and is the one to establish and define the automation simulation and virtual commissioning concepts. Delmia Automation is the tool offered by this vendor. Tecnomatix is more recent and arises from the integration of Unigraphics with SIEMENS; it offers a tool called eM- PLC oriented to virtual commissioning with SIEMENS´s PLCs5.
Automation Hardware or Software Vendors.
Automation tools that have been developed and are offered in the market by automation hardware manufacturers are part of this classification. Automation technology software is closely related to and necessary for using automation hardware. Companies that are vendors of automation hardware such as SIEMENS, Rockwell, Schneider, ABB, etc.
offer in their product portfolio different software applications oriented principally to own PLC programming and PLC emulation. Some of these also offer automation simulation software intended to develop virtual emulation software. The resulting applications vary in visualization, scope and complexity depending on the vendor. Visualization of these tools goes from primitive 2D objects to the import of 3D solids created in CAD; the scope of these tools is mainly oriented to validate PLC programming. Despite of the facts that these tools do not offer the features and capabilities that PLM tools offer and that they have a reduced scope, their principal objective is to validate automation and control PLC programming, which are considered automation simulation tools4. The most important feature of this kind is that it is only compatible with the developer’s automation hardware.
SIMIT by SIEMENS, RSTestStand by Allen Bradley and Unity Pro by Schneider are examples of these.
Software Tools from a Third Party.
The origin of tools in this classification is very heterogeneous; some of these have been developed by software companies oriented to automation. Despite of the fact that there are not a part of PLM solutions, some of these are supported by renowned software companies with brand positioning and expertise in automation. These are robust and have a high level of integration with proprietary and external applications. Some examples are EasyVeep, Cosimir PLC & Ciros Mechatronics by Festo, InControl and InTouch from Wonderware suite by InvenSys. Tools developed by software companies without automation orientation but with expertise in software developing are also in this classification, either in an independent way or by joining with automation hardware manufacturers. TaraVRcontrol by Tarakos is an example of this. A third division that is mainly intended for own training and which is the most varied group is integrated by automation simulation tools created by individuals or little groups of programmers principally automation professors or PLC fans, oriented to facilitate the understanding of PLC programming. Some of these tools have an extended scope and offer other possibilities. However, because of their origin, these are not so robust and their capabilities are somewhat poor compared to the tools in the other two classifications. EasyPLC, SPS- VISU, Prologix are examples of this. The Table 1 in Appendix A shows a summary of the main features and capabilities of these 12 automation simulation tools in the market. These
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are the most commonly used, and they cover most of the tools present in the market. In case of any omission, the mentioned tools describe well the status and the simulation capabilities of the tools in the market.
As shown in Table 1, eM-PLC and Delmia Automation are the most used in the industry by large companies for their high integration level and capabilities; however, the licensing cost of the tools is high. InControl and InTouch are used by automation companies, which develop final applications for the industry. These are oriented to developing and are based in 2D visualization; licensing is required. The RSTestStand, Unity Pro, and SIMIT’s industry versions are oriented to middle-sized companies and are used by manufacturers and external automation companies to validate PLC programs. They only work with hardware from Allen Bradley, Schneider Electric, and SIEMENS respectively: licensing is required for their use. SPS-VISU has achieved integration with SIEMENS; however, the visualization capabilities are poor as it only uses 2D graphs.
taraVRControl is also a developing tool for final applications. This uses an OPC server which makes it possible to communicate with the final application developed on this with any PLC; however, licensing and somebody to develop applications are needed. The rest are not intended for use in the industry as they have been created by small developing companies. EasyPLC offers powerful visual process emulations while PLC and HMI have a programming environment and a suite called machine simulator to build virtual applications. The main objective of these tools is to be used for developing software for virtual environments and offer no final applications to practice. Only EasyVeep, CIROS by Mechatronics and ProLogix are final applications. EasyPLC and SIMIT SCE offer some examples, but the rest are developing tools which need additional software or hiring a company that develops final applications. All these tools require licensing and their price varies according with the tool.
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Table 1 Principal Features of Automation Simulation Tools in the Market
