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Advanced flexible automation cell

Final Report Summary - FLEXA (Advanced flexible automation cell)

Executive Summary:

The FLEXA (advanced FLEXible Automation cell) project was a collaborative project within the seventh framework program (FP7) that aimed to develop and deliver new technology that supports flexible automation within the European aero-engine industry. The project started on 1st of June 2008 and ended on 30th of November 2012. The project had one main objective:

"To create the tools, methods and technologies needed to define, prepare and validate an automated flexible cell that can manufacture a generic process chain allowing for safe human interaction and deliver quality assured parts for the European aerospace industry."

FLEXA was a case-study driven project. The end-users submitted real industrial problems, whose main features were abstracted to form the basis of the construction of two demonstrator cells. These proof-of-concept demonstrator cells were developed within the project, and the results were fed back to the end-user companies. The results were successfully validated and thus shown to be implementation ready.

The project consortium consisted of 14 partners including some of the major aerospace component manufacturers in Europe, namely Rolls Royce PLC, GKN Aerospace (formerly known as Volvo Aero AB), MTU Aero Engines, Avio Spa. and WSK “PZL-Rzeszów” S.A. The consortium also involved four SME companies. These were BCT and Skytek, which focused on information knowledge and data transfer, MILTECH, which developed virtual manufacturing technology and REPLY, which was involved in data transfer between the automation cell coordination unit and IT systems such as ERP and PLM. The basic research activities of the project were conducted by the six universities involved in the project. These were Chalmers University of Technology and University West in Sweden, Nottingham University, University of Sheffield, and Cranfield University in the UK, and Pisa University in Italy.

Numerous scientific papers based on the results from this project have been published in journals and presented at scientific conferences. The interaction with higher education at the universities was also strengthened within the project. For example, more than 10 project courses were developed, which were based on examples from FLEXA. A number of thesis works at both the BSc and MSc levels were supervised and 11 PhD students engaged within the project. Numerous information activities, apart from the scientific publications, were also undertaken. Examples are joint seminars, on-site courses and open seminars for companies. A video showing the ideas behind the project and results was also produced. The achieved research results as well as the publications together with the close university-industry collaboration have given a solid base for future collaboration.

Project Context and Objectives:

Project context

The aero-engine industry is strongly controlled by regulations regarding emissions, noise, and safety in order to provide both environmentally friendly and safe transport. Compared to other industries, relatively low volumes of products are produced. However, the products are typically in operation for 30 years or more before they are taken out of service. This puts strong demands on flexible manufacturing. State-of-the-art in flexible manufacturing in general can be described as shown in 0. When production of a single component or a very closely related part should be performed, normally a transfer line solution or a dedicated automation cell is preferred. On the other hand, if product flexibility is of prior importance, shop floor solutions that can virtually host any type of product are the most optimal solution. Aero-engine manufacturing mainly operates in the latter regime, shown in lower right corner in Figure 1, because of relatively low volume production and the necessity to use existing machineries. The Flexible Manufacturing System (FMS) was developed with the goal to compile the best characteristics of the two extremes. The idea behind the FLEXA project was to adapt and further develop the FMS concept by introducing new tools and technologies. Examples of such tools were virtual manufacturing and knowledge engineering. Other examples were automatic restart of production cells and automated code generation for PLC systems. The strategy was that these technologies should push the FMS concept towards the vision of a cell as defined in the upper right corner of Figure 1.

Figure 1. State-of-the-art in flexible manufacturing and vision of FLEXA cell

The idea was to utilize state-of-the-art technology developed in the automotive area and to adapt and improve this technology in the aspects of flexibility, low volume multi-product production and quality assurance. The ultimate goal was to create a balanced production unit which can produce a multi-generation, multi-size and multi-product flow of components in one production facility with a given set of physical machines. To meet this goal, a number of challenging issues were addressed:

• New validation methods and technologies for virtual manufacturing supporting definition, preparation and operation of flexible automation cells
• New definitions and verification schemes for quality assurance systems that allow flexible automation cells to conform to aerospace industry regulations
• More efficient data flow within the manufacturing cell as well as in and out of the cell by introducing a Web-based data flow system that supports a truly virtual and distributed working environment
• New tools that support knowledge engineering demands in shop floor activities with experience feedback to product innovation activity
• Building physical validation platform for new technology
• Development of education and safety instructions for personnel that will operate next generation flexible automation cells

Project objectives

The FLEXA project was set up to meet one common main objective which was defined as:

To create the tools, methods and technologies needed to define, prepare and validate an automated flexible cell that can manufacture a generic process chain allowing for safe human interaction and deliver quality assured parts for the European aerospace industry.

This main objective was broken down into seven specific Science and Technology (S&T) objectives, all of which contributed to fulfill the main objective of the project:

• Development of flexible automation technology for the aero industry requirements
• Integration of key manufacturing processes in automation concept
• Development of virtual tools supporting cell preparation, operation and restart
• Development of knowledge engineering tools supporting automated manufacturing
• Integration of manufacturing knowledge in design activities
• Development of intelligent data communication protocol for manufacturing
• Develop a quality assurance strategy that meets aerospace requirements

The consortium

The consortium consisted of 14 partners with different responsibilities and competences identified as necessary to develop the required methods and technology to be able to validate a next generation flexible automation cell for machining, fabrication and assembly of aerospace components. Partner organizations represented three different groups- aerospace component manufacturers, SME companies and research practitioners.

The representatives from the aerospace component manufacturer in Europe were Rolls Royce plc., GKN Aerospace (formerly known as Volvo Aero AB), MTU Aero Engines, Avio Spa. and WSK “PZL-Rzeszów” S.A. The major role of these partners was to contribute with component manufacturing and design knowledge as well as authority regulations for manufacturing.

The four SME companies involved in the project were responsible for developing state-of-the-art technologies that formed the building blocks of the project. BCT and Skytek focused on information knowledge and data transfer, MILTECH developed technologies in the area of virtual manufacturing along with the machine cell development for grinding of blades, while REPLY was involved in data transfer between the cell coordination unit and existing company information technology systems such as ERP and PLM.

The basic research activities of the project were conducted by the six universities involved in the project. These were Chalmers University of Technology and University West in Sweden, Nottingham University, University of Sheffield, Cranfield University in the UK, and Pisa University in Italy. A list of all beneficiaries is given in Table 1.

Project Results:

Science and Technology (S&T) results

The project was focused on proving new methods and technologies for the introduction of advanced flexible automation in the aero-engine industry. Validation of developed methods and technologies was a central part of the project and two demonstrator cells were constructed for this purpose. Concept validations were performed at three different levels in the two demonstrator cells as shown in Figure 2. The developed methods and technologies were iteratively validated until a final validation at level 3 could be performed and thus the full objectives of the FLEXA project could be delivered.

Figure 2. The three validation levels in the FLEXA project

The major S&T results for each work package are presented beneath. A brief description of each work package is also given for completeness.

Work package structure and summary of S&T results

The project was divided into six different work packages in order to meet the S/T objectives. These six work packages were divided into five technical work packages and one management work package. The interaction between the work packages is schematically outlined in Figure 3.

Figure 3. FLEXA work packages and their interaction

WP1: System architecture and design

WP1 focused on definitions of cell requirements for processes and machines including specifications of data transfer protocols. WP1 also defined the validation objects that were used to physically validate the cell concept. An optimisation strategy for generic cell construction was developed which enabled optimisation of process chains including cell performance characteristics, limitations on physical boundaries, control capability for quality assurance and human interaction capability. One important result from WP1 was the development of a novel and flexible reconfigurable software environment for data management capable of supporting automated processing and assembly of aero engine components, see Figure 4. This software environment proposed that the flexible configuration is achieved through the use of flexible ‘cells’ and using a modular approach for the implementation of functionality. A ‘cell’ from the data management point of view was defined as anything from a machine to a whole shop floor as long as the elements of the ‘cell’ are connected in terms of data and material flow. Furthermore, a methodology for optimal cell layout using neural networks was developed and evaluated in WP1. The methodology consisted of developing a neural network and then training this network to find an optimal manufacturing layout using results obtained through a process simulation package. Once trained, the neural network can be used to optimise different cell layouts. Another topic studied and evaluated in WP1 was “Design For Manufacturing” (DFM). It was concluded from this study that to design the new product such that it is compatible with the existing equipment is vital in product design. Flexibility in this context means if an existing automation cell could be reused or if a new product could be introduced and produced in a cell that is already producing another product. It was concluded that to ensure this compatibility, a good process for applying manufacturing requirements in product design is needed. New manufacturing requirements were thus developed by the end users.

Figure 4. Flowchart illustrating the FLEXA data management system

The most important results from WP1 could be identified as:

• Cell definition
o Specifications for the FLEXA demonstration cells including
 Components and processes within the cells and
 Cell layouts for both demonstrator cells

Within the task of cell definition early identification of components and manufacturing processes to be included within the demonstration cells were agreed upon and work completed to investigate the processes suitability to be automated. Trade studies were carried out to identify and report current best practice. Product flows of the products were assessed using discreet event simulation and optimised layouts were proposed.

• Design rules were developed for automation cells
• Best practice was defined

The understanding and optimisation of specific design features enabled demonstration components to be fully automatically handled and positioned, both accurately and repeatably. Within the design rule task a staged DfM approach was taken where early ideas were developed and tested within initial demonstration set ups. Results from these trials input into the 2nd stage, within the project known as modified design rules. Within this task the developed methodologies were further tested across the VAC Fabrication cell, Avio’s blade machining cell and Miltech’s blade grinding cell.

• Data management system was developed including
o Requirements of the cells data to be managed
o Definition of software system architecture
o Interface with programming DNC systems

Specification of data to be handled within the VAC and AVIO demonstration cells was defined and concepts to be demonstrated provided to WP2 through the syndicate of partners which formed the data management group. The software system architecture required to handle the data was defined and appropriate trials and demonstrations performed. xDNC functional requirements and the system architecture required to manage the programs within the blade machining cell also defined. Demonstration of the xDNC system performed within WP4 and potential future enhancements identified.

• Best Practice was defined concerning
• Communication of Best practice and lessons learnt

Each of the 27 WP1 reports captures best practice/knowledge gained. Beyond this there is the Flexa Dissemination film. A small working group coordinated by the University of Sheffield organised regular planning meetings in which the content of the film was agreed. A number of partners including Rolls-Royce, GKN, MTU, BCT, Cranfield, Chalmers and University West were involved preparing material to be used. The film spans all work packages and unfolds like a story explaining the challenges of automation within the aerospace sector to the viewer. Interviews with consortium members and technical presentations combined capture the challenge facing the aerospace sector, activities completed and demonstrations achieved within the four year FP7 project.

Lessons Learnt

Design rules – The demonstration cells have enabled the manufacture of these fabricated components to be better understood, but has highlighted the importance of knowledge of the manufacturing process capability within the early design stages.

Ideas for the future

Potential enhancements in the user interface for the xDNC system to incorporate portable hardware (Tablets) with special functionality for example Quality tasks.

WP2: Knowledge engineering

WP2 focused on manufacturing knowledge engineering. Focus was mainly placed on the creation of a set of tools that automated knowledge generation from the cell and making this information available to the subscriber of the infor¬¬¬¬mation. The subscriber was defined as either a control routine of the cell surveillance or a person preparing to start producing a new component in the cell. WP2 also defined the requirements and engineered the data for communicating with processes, control equipment, sensors and environment status as well as with the human machine interface. Developing a link between the FLEXA Database, see Figure 4, and the actual production machines using the FLEXA Cell controller was a central result in this work package. The FLEXA Cell Controller (FCC) system, a fully functional control software which was integrated with the FLEXA Database, was developed, see Figure 5. The functionality of the FCC system was demonstrated and successfully validated within WP4.

Figure 5 Schematic of FLEXA Cell Controller (FCC) system

Examples of results from WP2 were:

• A flexible cell controller was realised and demonstrated
• A common interface for communication between automation resources was developed
• An integrated system from planning to production was developed

WP3: Preparation, virtual verification and quality assurance

WP3 developed and validated virtual manufacturing tools that supported the preparation of new manufacturing tasks to be delivered to the cell. Focus was placed on the development of a platform for a complete virtual manufacturing for flexible automation cells. The platform supports flow simulation as well as off-line modelling of a generic automation cell with multiple process resources. It also supports automatic generation of cell control (PLC) programmes. Virtual models were developed to conform to real cells in order to secure robust operation in the cells and to allow for quality assurance. One important example here was an off-line model of the Volvo fabrication cell i.e. demonstrator cell 2 in WP4, see Figure 6. The simulation models were equipped with advanced signal models and logics based on OPC/PLC connections and internal logic blocks. A PLC program was used to control the simulations. The PLC program was designed to match the requirements of the cell which were defined in WP1. The main advantage of this design is that the sequence of operations is controlled by an external PLC, i.e. the same PLC as in the real cell. This enables validation at level 3 as previously described in section 4.5.

Figure 6. Model of the Volvo fabrication cell i.e. demonstrator cell 2 in work package 4.

The overall principle of the developed virtual manufacturing platform is shown in Figure 7. This work package developed tools for geometrical tolerance dependency that needs to be controlled in the automated cell to meet pre-defined requirements. This is of particular importance since the parts to be welded, machined, assembled often show deviations from the nominal shape. To assure the process stability and the manufacturing quality, the developed information management supports adaptive manufacturing technologies.WP3 also developed the code for the validation tests that were performed in WP4.

Figure 7. Principle of the virtual manufacturing platform for preparation of new manufacturing tasks

Examples of results from WP3 were:

Methods and tools for:

• Automatic generation of PLC code. The major characteristics of this system are
o Easy to program
o Process planner knowledge is dramatically reduced (time ~15 min)
o Automatic PLC code generation (IEC 61131-3)
o Deadlock free code
o Code designed for download during production
• Simulation model generation
• Generating operation sequences in early preparation phases

o Simulation-based optimisation of product flow. This enabled a non-hierarchical manufacturing architecture with More intelligent agents
o Alternative routes included
• A new wizard holon was also developed that included
o Guide and help to make more optimal decisions
o Simulation-based optimisation with a time constraint

WP4: Cell integration

WP4 was responsible for the physical equipment needed for the validation. It also delivered validation data to other work packages in order to iteratively improve the other work packages’ capability to deliver a fully validated flexible automation cell at the end of the project. WP4 was also responsible for education of cell operation. Two demonstrator cells were constructed in close integration with WP3, where virtual simulation activities were performed in order to optimize production flow by utilising the discrete event simulation method and to prevent collisions in the demonstrator cells. In order to increase the robustness of current manual processes, tests of automatic deburring operation on vanes and blades were performed, finalizing a new process on vanes. Validation tests were also performed to validate the developed xDNC system in WP2. These tests were performed in the Avio demonstrator (machining cell - see Figure 8). This system implements the functionality of the transferring and storing Part Program, managing the revision traceability and approval process in the final demonstrator. It also provides the instruments to the cell's user for managing of Production Orders in the cell.

Figure 8. Model of a manufacturing cell for complex aero engine structures

Examples of results from WP4 were:

□ One demonstrator manufacturing cell that included grinding, deburring and measuring operations, Figure 9a, Figure 9b.

Figure 9a. 5 AX- GRINDER (Demonstrator cell developed at Avio)

Figure 9b. Robotized cell for deburring operation (Demonstrator cell developed at Avio)

o One demonstrator manufacturing cell that included welding and non-destructive testing, Figure 10.

Figure 10. Volvo demonstrator cell developed at Production Technology Centre, Trollhättan, Sweden

o An extended DNC system, see Figure 11.

Figure 11. Extended DNC system
o A system for training operators by means of an innovative augmented reality methods has been also developed. Figure 12 shows one of the hardware configurations experimented in the project. By this technique images can be generated on the optical path between the eyes of the operator and real objects in the working area. Images can be visualized as 2D pictures or 3D object (by stereoscopic vision). This technique replaces paper-based work instructions or multimedia information systems allowing a context-related visualization of information, reducing search time in assembly or upstream activities.

Figure 12. Augmented reality system for training of operators in an industrial environment

WP5: Human interaction and restart

WP5 developed a strategy for the situation when an anomaly is detected in the operational sequence of the cell. Strategies for restarting the manufacturing process and supporting tools for restart were developed. The main task was to re-synchronize the control system and the physical system. This task is in general further complicated by requirements on minimum loss of material and time. The developed system enables the computer-demanding and time-consuming work to be performed off-line. Another functionality of the developed system is the generality of the system that makes it possible to execute the program in any Programmable Logic Controller (PLC). Graph-based backward recovery strategies were implemented. The method addresses the issue of reducing the complexity of the system by keeping the number of restart states at a minimum. The system is focused on but not limited to errors that are typically handled by maintenance personnel, such as faulty sensors/actuators, mechanical and electrical faults etc. One critical task in this context was the quality assurance handling and data mapping for the inconsistency created in comparison to expected results.

The worker has been provided of a touch screen monitor and a webcam, attached on the back of the monitor. He directs the display toward the work area and sees the video stream captured by the camera, enriched of virtual contents which explain him the task to accomplish. After reading the information, moves away the screen from his view and operates.

As a test case, it was selected the task of vane assembly on the grinding machine fixture performed in Avio plant in Pomigliano. First of all, it was examined the assembly operation and the material collected in the workshop for a better comprehension of the procedure. Then, the procedure itself was divided in elementary operations and a flow chart for the steps was created; assembly instructions and virtual contents such as video, pictures, 3D models were associated to each operation.

Several tests have been carried out, to check the correctness of the training and value its feasibility. A first set of trials were accomplished inside University of Pisa laboratory; then a real operator used the Augmented Reality based system in Avio plant. Figures 13 and 14 show the Avio worker using AR hardware, notice the markers attached to the real fixture.

Figure 13: Avio worker using Augmented Reality system

Figure 14: detail of the 10" touchscreen monitor with the “augmented reality” instructions to the worker

FLEXA project is underlining how important the component of human-machine interaction is in industrial field, and so AR technique could represent a tool necessary for allowing efficient interaction with the advantage of time saving, error reduction and accuracy improvement.

Examples of results from WP5 were:

• Supporting tools for restart by augmented reality (AR) i.e.
• AR tools for operator training and support during preparation and restart that
o enhances human-machine interaction
o gives quick response in failure and
o reduces lead time

• Methods and tools that enable pro-active handling of failure recovery (restart). This includes preparation, human machine interaction, training, and data communication to cell main control for quality assurance and safe operation. Formal methods were used to support decisions, taking restart limitations into account as well as formal evaluation techniques that allowed exhaustive and early assessment of the suitability of the recovery strategy. The information system architecture showing the restart component accessing information via web services is shown in Figure 14.

Figure 14. Information System showing the restart component

Potential Impact:

Potential Impact

The project focus has been on development of manufacturing technologies and virtual methods supporting the next generation production environment from an automation and flexibility perspective. The selected application area of aero engine development and manufacturing and the main topic to improve cost efficiency in the area of production is highly relevant for the long term objectives for the European industry and will thus have an impact on the European aero-engine manufacturing industry.

The long term objectives for European industry are to deliver technology readiness by 2020 in the following three subjects:

1. Reduce aircraft development costs by 50%
2. Create a competitive supply chain able to halve the time-to-market
3. Reduce travel charges

These 2020 objectives were broken down in the FLEXA project to more tangible objectives that were possible to be delivered within the timeframe of the FLEXA project. Figure 15 below illustrates the impacts from FLEXA foreseen in the near term.

Figure 15. FLEXA contribution to objectives for European industry

Since manufacturing cost, to a large extent, depends on the selections done in product development as well as in the production facility set up to deliver the hardware, it is obvious that the results from FLEXA supports the 2020 vision. The aero-engine manufacturers have identified large structural fabrications as a key area where flexible automation will have major impact on productivity and thus development costs. The FLEXA project, for this purpose, has performed a number of activities that have and will produce direct impact on technical, collaborative and educational fields. These activities in the project were divided into three different groups namely (see also Figure 16 beneath):

1. Activities that have a direct impact
2. Activities that have a strategic impact
3. Activities that have an indirect impact

Figure 16. Direct, strategic and indirect impact of FLEXA project

4.7 Impact Examples

Examples of activities with impacts from FLEXA to support the long term objectives for European industry are:

Reduce aircraft development costs by 50%

1. Knowledge based manufacturing and virtual manufacturing is a key area to reduce development cost in aerospace manufacturing. This has been enabled in FLEXA through the ability to reuse of information via an integrated web service data structure between preparation tools, manufacturing equipment, external data sources and human machine interfaces. This web data based system provides methods and support tools to increase flexibility, efficiency and productivity in the design process and will thus reduce development costs. The project focuses on systems aspects within virtual manufacturing where human aspects are included which can safely assumed to be a central part when development cost is to be reduced. Boundary conditions have been explored to achieve truly flexible automation which is very valuable when new products are designed and developed. Boundary conditions of the automated production system and its ability to handle fluctuations in orders, deliver products with high quality, short lead times and high delivery precision are of specific interest.
2. The generic information management system that handles the information exchange within the cells as well as between the cells and the outer environment. This management system will be an effective tool when new products are to be developed. The tool is based on open standards such as XML and existing infrastructure like LAN and Web which makes it easy to implement. It has been shown that most control implementations of flexible manufacturing cells have been developed specifically to a particular system or facility, and no generic format or tools exist for the systematic creation and planning. The system platform developed in FLEXA has a major advantage compared to existing systems which lack portability and require a long implementation time. Operational flexibility is a key issue in this case that can significantly reduce implementation time and development costs. The developed system platform that can reuse information via a common data base structure is shown in Figure 17. The focus on flexibility of the information management will reduce costs since it can handle changes of the work-pieces as well as of the cell components like processes, process equipment, NC controls, PLC, part identification, part handling (including robots), sensors, inspection (dimensional measuring, QA, NDT) and cell status information.

Figure 1. Figure 17. Information management system in FLEXA

3. Automated code generation and reuse of control logic through the developed system for simulation that utilises emulation. This approach has several advantages compared to state-of-the-art today. The emulation approach enables a complete validation before manufacturing of a part. The validation can also be carried out with unmodified control logic which also is highly beneficial when development time is to be reduced.
4. The developed “Flexible cell controller” that was realised within the project with its common interface for communication between automation resources enables a standardized interface for the shop floor. This will reduce development time when new parts are to be introduced in manufacturing.
5. Restart. In aerospace manufacturing downtime due to errors is extremely costly. The developed methodology and software system for restart will here be very beneficial and significantly reduce the downtime cost. The computer demanding work in this system is done off-line and the on-line part of the method can be executed in a standard PLC. The method relies on the use of restart states, i.e. states within the control function from where it is safe to restart normal production after an error. Once a fault has been detected and corrected, the system is reconfigured to return to a restart state, where parts of the work schedule are re-executed before normal production is finally resumed.

The enabling technologies from these five examples have been estimated within the FLEXA consortium to have the following quantitative impacts:

1. 30% lower product cost.
2. 2 times increased production rate with same process equipment.
3. 25% less scrap during component manufacturing.

Create a competitive supply chain able to halve time-to-market

The project has delivered both a direct and indirect impact on the goal of halving the time-to-market. The aero-engine manufactures have continuously implemented results from FLEXA both in on-going production as well as in new development programmes. Virtual tools for prediction and preparation of manufacturing will make it possible to optimise and consider manufacturing aspects in a more intelligent way and thus have an indirect impact to reduce time-to-market. The impact of research publications, reports, and developed teaching materials for engineering education will have an indirect impact on the time-to-market through knowledge implementation over a wider field, which will also improve European industry in general terms. These direct and indirect impact activities within the FLEXA consortium have been estimated to:

1. 20% decrease in manufacturing preparation lead time.
2. 50% fewer prototypes in manufacturing preparation phases.
3. 15% less scrap during component development.

Reduce travel charges

The FLEXA contribution to reduced travel charges is mainly due to the enhancement in manufacturing automation and quality assurance area, where more efficient and optimised production will lead to lower production cost and higher utilization of manufacturing equipment. Reduced travel charges are expected also due to the indirect connection between travel charges and the efficiency in product development and supply chain performance. These direct and indirect impact activities have within the FLEXA consortium been estimated to:

1. Enabling technology for successful introduction of truly flexible automation cells in European aerospace industry.
2. 30% lower product cost as efficiency in production of the hardware for the aero engine industry accounts for a significant part of the total cost projected to the customer
3. Two times increased production rate with same process equipment. Introducing the flexibility aspect and bringing the flexible manufacturing system has been estimated to provide significant cost savings especially in the area of production equipment utilisation and in robustness of this production through an improved information handling and sharing system. The developed technology that enables testing new concepts and analysing consequences of introducing new technologies as well as new products in manufacturing will also increase the production rate. The system that automatically generates code to control the machine operations (NC-, Robot-, Sensor-, NDT- and monitoring-code) can be also safely assumed to influence the production rate.

4. 25% less scrap during component manufacturing. Reducing error in handling of the cell by preparing the cell offline as well as preparing personnel with training, instruction and guidelines before suspending the equipment. This will also make the equipment more productive since stops due to scrap will be reduced

Interaction between impact activities

Strong connections have to be considered between the performed activities corresponding to the three overall 2020 objectives. Successful improvement in product development cost will be highly dependent on a competitive supply chain since a large amount of the final product cost is defined during product development. Improvement in product development time is necessary to reduce the development cost and therefore strongly linked with the goal of halving the time-to-market. Since manufacturing cost, to a large extent, depends on the selections done in product development as well as in the production facility set up to deliver the hardware, it is obvious that the performed activities in FLEXA will support the 2020 vision.

Summary of industrial impact

FLEXA has developed the next generation production environment from an automation and flexibility perspective by introducing flexible automation and intelligent simulation tools that significantly can reduce installation time, time to reconfigure production and productivity.

The simulation platform that has been developed can be used immediately by the case-study partners.

The modular, generic and standardized nature of the developed platforms will allow them to be exploited in in not just aero-engine manufacturing but also in a wide range of other applications such as:

• Machining in general
• Welding in the automotive business
• Assembly in the mechanical industry in general

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