CC BY-NC-ND
0Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 32 (2015) 19 - 24
www.elsevier.com/locate/procedia
The 5th Conference on Learning Factories 2015
Learning Integrated Product and Manufacturing Systems
Hoda ElMaraghy*, Waguih ElMaraghy
Intelligent Manufacturing Systems Center, Industrial Engineering Department, University of Windsor, Windsor, ON N9B 3P4, Canada * Corresponding author. Tel.: 519-253-3000-5034; E-mail address: hae@uwindsor.ca
Abstract
The development of manufacturing systems that are adaptive to the frequent changes in products and market conditions is becoming important with the increase in product variants and decrease in production volume of each variant to meet customers' demands while remaining profitable. Students mostly learn about various aspects ofproduct design, different production technologies and production planning techniques separately. It is important to provide students with hands-on experience in the whole integrated cycle ofproduct and manufacturing systems development in a realistic environment.
This paper discusses the essential knowledge elements spanning the integrated product/system life cycle and the effects of changes in products and their order mix on the manufacturing system synthesis, design, control and operation. Particular emphasis will be placed on adaptable and changeable manufacturing systems which can respond quickly and efficiently to variations in products, product mix and production volume.
There are many types of learning environments and factories that can lend themselves to systems-oriented training. They vary greatly in type, scope, function, size, and location but can offer a rewarding experience when coupled with appropriate learning modules and education pedagogy. A truly reconfigurable and changeable manufacturing assembly system, the iFactory, and an iDesign studio as well as the iPlan modules are used to demonstrate how students can gain valuable learning experience in interactive custom order placement using an iOrder tool which allows product customization and personalization, product design and rapid prototyping, order processing and scheduling, products assembly and inspection, as well as manufacturing systems design and synthesis for changeable requirements. This Learning Factory environment is the first of its kind which integrates products and systems development, operation and control. It offers a unique learning environment for senior undergraduate and graduate students as well as industrial trainees.
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of 5th Conference on Learning Factories (CLF 2015)
Keywords: Learning Factories, Manufacturing Systems, Body of Knowledge
1. Introduction
Manufacturing systems development today presents many challenges; not only to satisfy the production technological and functional requirements but also to integrate machine tools design, fixtures, monitoring, control, automation, logistics and quality functions in addition to building-in physical and logical enablers of flexibility and adaptability. This is particularly important given the proliferation of products variety and customization and frequent changes in technology and market demands. The close integration and co-development of products and their manufacturing systems makes the design, planning and control of manufacturing systems a complex multi-disciplinary application of many fundamentals taught throughout the engineering education curriculum in separate
subjects. Manufacturing systems engineering combines the knowledge required to synthesize increasingly complex manufacturing systems. Therefore, it is important to provide students with opportunities to put what they learned into practice and an environment which allows meaningful hands-on learning experience.
Learning Factories have the potential to provide such an opportunity if they are planned to fulfill these objectives. Many Learning Factories exist which focus on one or more aspects of manufacturing knowledge such as certain processes and technologies, lean manufacturing, six-sigma systems management strategies, or energy consumption and green engineering techniques. They provide depth of knowledge in the specific topics. Learning factories can also be planned to provide systems-oriented and integrated product-system
2212-8271 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of 5th Conference on Learning Factories (CLF 2015) doi : 10.1016/j. procir.2015.02.222
development and operation learning experience and help acquire and implement the necessary breadth ofknowledge.
This paper discusses the body of knowledge required for engineering education and in particular manufacturing systems engineering. It highlights the need for flexible, adaptable and changeable manufacturing systems and how they should be designed and operated. Existing types of Learning Factories are briefly overviewed. A reconfigurable systems-oriented Learning Factory which integrates product and system design considerations is described and the experiential systems learning modules introduced using it are presented followed by discussion and conclusions.
2. The manufacturing/systems engineering body of knowledge
2.1. The engineering body of knowledge concept
A profession's body of knowledge (BoK) is its common intellectual ground that is shared by everyone in the profession. The Engineering BoK, as used in this paper, is defined as the depth and breadth of knowledge, skills, and attitudes appropriate to enter practice as a professional engineer in responsible charge of engineering activities that potentially impact public health, safety, and welfare. Within the BoK: i) Knowledge consists of comprehending theories, principles, and fundamentals; ii) Skills are the abilities to perform tasks and apply knowledge; and iii) Attitudes are the ways in which one thinks and feels in response to a fact or situation.
For the purposes of the Engineering BoK, the knowledge, skills, and attitudes are referred to as capabilities. A capability is defined as what an individual is expected to know and be able to do by the time of entry into professional practice in a responsible role. A given capability typically consists of many diverse and specific abilities. Each capability is usually acquired by a combination of engineering education and experience. The National Society of Professional Engineers (NSPE) [1] lists 30 capabilities comprising the recommended Engineering BoK. They are organized into three categories, namely, "Basic or Foundational, Technical, and Professional Practice: A) Basic or Foundational Capabilities: 1. Mathematics, 2. Natural Sciences, 3. Humanities and Social Sciences; B) Technical Capabilities: 4. Manufacturing / Construction, 5. Design, 6. Engineering Economics, 7. Engineering Science, 8. Engineering Tools, 9. Experiments, 10. Problem, Recognition and Solving, 11. Quality Control and Quality Assurance, 12. Risk, Reliability, and Uncertainty, 13. Safety, 14. Societal Impact, 15. Systems Engineering, 16. Operations and Maintenance, 17. Sustainability and Environmental Impact, 18. Technical Breadth, 19. Technical Depth; and C) Professional Practice Capabilities: 20. Business Aspects of Engineering, 21. Communication, 22. Ethical Responsibility, 23. Global Knowledge and Awareness, 24. Leadership, 25. Legal Aspects of Engineering, 26. Lifelong Learning, 27. Professional Attitudes, 28. Project Management, 29. Public Policy and Engineering, 30. Teamwork".
It should be noted that manufacturing and systems engineering have increased in importance in recent years, in practice as well as in the education curriculum. Systems
engineering is a holistic, product-oriented engineering discipline the objective of which is to create and execute an interdisciplinary process to ensure that customer and stakeholder needs are satisfied in a high quality, trustworthy, cost efficient and schedule compliant manner throughout a system's life. It requires many of the engineering BoK capabilities specifically knowledge of Manufacturing processes and technologies, machine design and control (NC, CNC, Digital,..), fixturing and tooling, inspection and quality control, process planning, production planning and scheduling, layout planning, production and management strategies and manufacturing systems paradigms.. Figure 1 illustrates key technologies used throughout the product and manufacturing systems life-cycle. Accreditation processes require that engineers before graduating must complete a "Capstone" engineering design project, usually with industry, and write a graduating project report that includes the relevant academic body ofknowledge, the experiential learning and the practical engineering implementation.
Manufacturing Systems Holistic Learning Factory: i Order J j Plan t i Factory key process» and technologies
Cone urrent Concept go ne ration.
Planning, Development, Implementation and Operation
PnidutBDn Mf^&yilimi fraHJctlnr inugrauaji
Tl » f "> » -1P|«nn|na Rttlbvp
Product Life Cycle
(''"^ "p ' "J ü u Hilitmn
Manufacturing System Life Cycle ' 6,""-r
Fig. 1 The product and manufacturing system life-cycles and related manufacturing systems learning technologies
2.2. Importance of manufacturing systems engineering
A prosperous economy depends on a well-educated and highly skilled workforce, which creates a remarkable competitive advantage. Manufacturing is a corner stone of the world economy. The "Manufacturing share of global GDP is 16%, it created 62 millionjobs in 2000 and 45 million in 2010, 30-50% of service jobs are in manufacturing, the advanced economies' trade deficit in labor-intensive goods is $342 Billion and $ 726 Billion is their trade surplus in innovative goods" [2].
Manufacturing remains critically important to both the developing and advanced countries in the world. In developing economies, it continues to provide a pathway from reliance on agriculture to industrialization and rising incomes, living standards and prosperity. In developed economies, manufacturing remains a vital source of innovation and competitiveness, making significant contributions to research and development, exports, productivity growth and wealth generation.
Global competition in advanced manufacturing is growing more intense as technology life cycles are becoming shorter. In addition to achieving economies of scale through mass
production, it is increasingly important to achieve economies of scope by rapidly and economically producing alternative variants of products to satisfy diverse global customers and segmented markets. In today's global race for manufacturing success; competition is all about innovation. Innovation in products, in processes and systems, in satisfying customers' needs and finding and growing new markets is essential. Small and medium size companies can manage incremental process improvements, but designing and developing new products and their variants, applying new technologies, optimizing the scope of products offering and targeting new local and global markets are now the new challenges.
Mass production of goods achieved economy of scale by extremely limiting variety, standardizing manufacturing methods and using dedicated production equipment and lines to produce very large number of same or similar products with steady demands. As products variety increased, economy of scope was achievable by capitalizing on similarity between parts and products to increase the efficiency of design, planning, tooling, fixturing, fabrication, assembly and transportation of members of a pre-planned product family using cellular and flexible manufacturing systems [3].
3. Variety and integrated products/systems development
The market volatility and demand fluctuations as well increased products variety and complexity require agility, adaptability, responsiveness and effective use of innovative enablers of change. As concern about global warming and the carbon footprint increases, combined with the world economic crisis, companies will have to produce closer to markets leading to more glocalization. High cost (wages & standard of living) manufacturing countries such as Canada, USA and many European countries cannot just compete on wages and productivity. It is important to design and manufacture products smartly to regain competitiveness. It is no longer sufficient to make things better, we must make better things.
Current international initiatives and programs focused on "Future Manufacture" place great emphasis on recent technological advances and new systems paradigms to strengthen strategic capabilities in key advanced technologies and to bridge the gap between innovation, implementation and commercialization.
Key transformative enablers and strategies for productivity in manufacturing, which can create significant competitive advantage for manufacturing enterprises of any size, include: flexile, reconfigurable and changeable manufacturing paradigms, intelligent manufacturing systems, customization and personalization of products, intelligent sensors and the internet of things, and personalized manufacturing. Therefore, it is essential to introduce engineering students to these concepts and tools and provide them with meaningful hands-on experience with their implementation and integration in manufacturing systems.
4. Knowledge integration via applied experiential learning
The need to bridge the gap between theory and practice in engineering education, in this competitive international
environment and to achieve the intended depth of knowledge in individual subjects in addition to the required breadth of knowledge in manufacturing engineering education, has led to the concept of "Learning Factories". This is akin to clinical training in hospitals for the medical professional education. The term "Learning Factories" was coined, as early as 1994, when the US National Science Foundation (NSF) awarded a consortium led by Penn State University a grant to develop a "Learning Factory". At that time, this term referred to interdisciplinary hands-on senior engineering capstone design projects with strong links and interactions with industry, hence, erasing the traditional boundaries between lecture and laboratory and academia and industrial practice. A college-wide infrastructure and 6,500 sq. ft. facility equipped with machines, materials and tools was established and utilized by more than 10 courses to support hundreds of industry-sponsored design [4]. The program received 2006 National Academy of Engineering's Gordon Prize for Innovation in Engineering Education. This early model of Learning Factories emphasized the hands-on experience gained by applying knowledge learned at the culmination of engineering education to solve real problems in industry and design/re-design products to satisfy identified needs.
The word "Factory" in this early concept of "Learning Factories", and many that followed, refers to the entire supporting educational environment and physical facilities which connect industry with faculty and increase student engagement and provide them with realistic factory like experience. The literal meaning of "Factory" being a collection of hardware and software entities that produce artifact(s) found its way recently to some Learning Factories. The word "Learning", as opposed to "Teaching", emphasizes the importance of experiential learning where research has shown that learning by doing leads to 80% greater retention than other methods such as lecture, reading, group discussion, demonstration, and simulation [5].
5. Recent Learning Factories initiatives and foci
Learning Factories have become more wide spread, particularly in Europe, and have taken many forms of facilities varying in size and sophistication aiming to enhance the learning experience of students in one or more areas of manufacturing engineering knowledge. A survey in 2012 [6] documented and classified these learning factories, and more continue to emerge since. The Institute of Production Management, Technology and Machine Tools (TU, Darmstadt) started one of the early implementations of Learning Factories in 2007. It consists of many machining centers used for training in industrial technologies and particularly lean manufacturing methodologies in collaboration with McKinsey & Company. Some representative examples of Learning Factories with different educational foci and physical implementation include those at the Institute of Production Systems and Logistics, Leibniz University Hanover, for logistics and changeability, Technical University of Chemnitz for assembly and energy and the Institute for Machine Tool and Industrial Management of the Technical University of Munich for energy productivity and consumption optimization, just to mention a few. The
Learning Factory for advanced Industrial Engineering (alE) at the Institute of Industrial Manufacturing and Management (IFF) (University of Stuttgart) is focused on the link between digital production planning and implementation of the physical models in the laboratory [7]. This Transformable Production Platform comprises standardized and mobile Plug and Play modules for assembly, coating, inspection, transportation and storage, and is capable of re-configuration into different layouts. It uses a product with many variants to demonstrate aspects of production planning and control and order processing.
Learning factories are not duplicates of industrial factories; they are designed to best suit and serve the intended experiential learning process. They often manufacture some parts/products but they are not for sale. The design and selection of such products is driven by different requirements and, therefore, must observe certain constraints and fulfill these educational objectives [6]. Learning factories vary in scope (training, research and applications), implementation (physical, virtual, internships and joint projects), size (full-scale, scaled modules, bench-top, Lego and other learning games), functionality (static or operational), and location (in the Lab, remote connection with plant or in industry) [5].
The mode of implementing Learning Factories is affected significantly by the associated investment, operation and maintenance cost. For Example, the Know-Fact European project, led by the University of Patras [8] advocates a two-way communication channel to reduce the cost of setting up learning factories by bringing industrial practices from industry to the Lab and providing new knowledge from the Lab to the factory through real time communication links between them.
Users of Learning Factories are not limited to university or college students. Company employees are often trained in courses offered using Learning Factories located in educational institutes. Furthermore, several companies have established learning factories within the company to offer structured hands-on learning to their employees aimed particularly at the technologies and knowledge most relevant to their business.
Learning factories provide an environment to learn, test and implement new product solutions and system paradigms. Learning factories can provide an environment for engineers and practitioners to become trained and experienced. Learning Factories may consist of a physical learning environment/hardware and a digital environment/software in order to simulate production processes realistically. Both physical and digital environments should be linked to support the adaptability and the improvement of each environment and emulate the production in real factories. Such integrated environment facilitates the co-design and co-development of products, production processes and manufacturing systems synthesis for their whole life cycle and the enabling technologies needed to increase manufacturing competitiveness, agility and flexibility [9]. Changeable Manufacturing Systems (CMS) are important to the variant-oriented discrete consumer's goods industries. They are necessary to implement frequent changes easily and cost effectively.
There are many important considerations when designing and selecting systems-oriented Learning Factories including
choice of processing modules, material handling systems, hardware control system and production planning and control system. One of the challenges of designing and implementing Changeable Learning Factories (CLF) is the selection or design of products to be created using the CLF. These products must support both students experimentation with the system, and allow demonstration of various related learning modules including the concepts of flexibility, adaptability and reconfiguration. Traditional products have to fulfill functions specified by the customers and market which influences their design, shape, structure and features. A manufacturing system in an industrial setting is planned and constructed to produce the required product. In contrast, products for learning factories are developed to support the planned education and research objectives and budgetary constraints. Additionally, it is desirable to be able to disassemble and reuse the products for multiple research and learning cycles to maximize the learning experience and support research in the field by both undergraduate and graduate students [10].
6. The manufacturing systems learning factory: ¡Order / ¡Design / ¡Plan/ ¡Factory
An integrated products and systems-oriented type of Learning Factories was set up in 2011 at the Intelligent Manufacturing Systems Centre at University of Windsor in Canada - the first iFactory in North America [11], illustrated by Figure 2. The equipment in this learning factory is similar to that at the University of Stuttgart, however, it focuses on systems learning which integrates products design, customization and personalization through the iDesign and iOrder modules (Figure 3), with innovative physical and logical enablers of change on the shop floor such as variant-oriented re-configurable process and production plans through the iPlan module, and design, planning and control of changeable manufacturing systems.
Fig. 2 The modular and reconfigurable Learning Factory at the IMS Center, University ofWindsor, Canada.
Capturing Customer's Spécifications
Personal Objector Provide a CAD Model
CMM with a Laser scanner Build a CAD Model
Specific Person a Nied Area
Final Cuitûitilteti and Personalized Product
Col ored Model Material
(temovaUe Support Material
Fig. 3 The product customization and personalization iOrder / ¡Design module
The ¡Factory is an example of a truly modular and reconfigurable assembly factory with the ability to change both its configuration and layout by modules relocation, addition and/or removal. This "Factory-in-a-Lab" contains modular Plug and Produce robotic and manual assembly, computer vision inspection, automated storage and retrieval system (ASRS) and material handling modules, RFIDs communication sensors and Siemens SCADA control system. Its intelligent control and modular standardized interfaces do not require reprogramming or change of set-up after physical reconfiguration which greatly reduces ramp-up efforts and time.
The iOrder (for customized orders) is complemented with the iDesign, iPlan and ¡Factory environment, with rapid prototyping and coordinate measuring machine facilities, which constitute the "Learning Factory" (Figure 4) which provides an exceptional experiential learning, training and research experience for undergraduate and graduate students, researchers and professional trainees. Knowledge elements covered include products design, prototyping, customization and personalization; variant-based process planning, order processing for mixed-model production, dynamic production planning and scheduling, and principles and enablers of flexible, reconfigurable and changeable intelligent manufacturing systems.
Fig. 4 Integrated products and systems design, planning and control demonstrated in the Learning Factory environment at the IMS Center
product variety management [3]: variety management has to consider the product range, the product architectures as well as the manufacturing system and the supply chain in a holistic and integrated manner. Variety management is considered in three dimensions: (1) Scope, to cover different market segments, (2) Scale, to produce in response to fluctuating demand ranging from a one variant unit as easily as a high demand of that variant, and (3) Time, so companies can sustain the evolution of their line of products and its variants; ii) types and metrics of manufacturing systems complexity [12]: to reap the benefits of complexity management, manufacturing companies need to not only adopt flexible technical solutions, but must also effectively innovate and manage complex socio-technical systems; iii) co-evolution and co-development of products and their manufacturing systems inspired by biological evolution and modelled using Cladistics hierarchical classification [13]: the model embodies a three-fold approach; first it identifies evolution courses of products and manufacturing capabilities, then it searches for the best matching courses to ascertain manufacturing co-evolution, and finally informs future planning guided by the established co-evolution scheme. The results reveal the existence of strong symbiotic co-evolution relationships; iv) new applications of Max-Plus Algebra in modelling and simulation of manufacturing systems [14]: maxplus algebra is a mathematical tool that can model discrete event systems in linear equations analogous to traditional state space dynamic equations. Modeling Mixed-Model Assembly Lines (MMALs) with max-plus equations would enable comparing sequences over ranges of values of assembly times thus increasing the robustness and reliability of obtained results; v) design synthesis of manufacturing and assembly systems and optimum system granularity [15]: the optimum granularity level and number of modules are determined, indicating the potential product and process platforms; vi) manufacturing systems layout complexity modelling and metrics [16]: the nature and sources of complexity in these areas are reviewed and complexity modeling and management approaches are discussed; vii) modular product-multi platform configuration [17]: the new model customizes product platforms by either assembly and/or disassembly, to produce different products and families. The optimal product platforms are defined based on product demand in each period. The developed model incorporates the cost of assembly/disassembly in forming product platforms and families; viii) assembly systems synthesis and master assembly sequence generation using knowledge discovery [18]: a novel knowledge-based mixed-integer programming (MIP) model is presented for generating the assembly sequence of a given product based on available assembly sequence data of similar products. The proposed mathematical model finds the optimal consensus assembly sequence tree for an existing product family based on the assembly sequence trees of individual product family members.
The Learning Factory serves as a test bed for demonstrating and assessing research results in several areas of product and manufacturing systems development and integration.
7. Discussion and Conclusions
This rich environment is also conducive of innovative research conducted in the Intelligent Manufacturing Systems Center at the University of Windsor such as: i) strategies for
The development of manufacturing systems that are adaptive to the frequent changes in products and market conditions is becoming important with the increase in products
variety and changes in production volumes to meet customers' demands while remaining profitable. It is important for engineers to achieve the breadth and depth of knowledge in this field at a professional level to be able to practice both locally and in a global competitive environment. Engineering students must learn to integrate their knowledge in various aspects of manufacturing systems engineering.
Establishment and advancement of Learning Factories is an important contributor to preparing students and researchers for the development of a new age of changeable manufacturing systems (CMS) and Intelligent Manufacturing systems such as those envisioned by Industry 4.0 initiatives in Europe and North America.
It is desirable to provide engineering students with hands-on experience in the whole integrated cycle of product and manufacturing systems development in a realistic environment representative ofindustrial practice.
There are many types of learning environments and factories that can lend themselves to systems-oriented training. They vary greatly in type, scope, function, size, and physical location but can offer a rewarding experience when coupled with appropriate learning modules and education pedagogy. The essential knowledge elements spanning the integrated product/system life and the effects of changes in products on the manufacturing systems synthesis and operation are mandatory. Particular emphasis should be placed on adaptable and changeable manufacturing systems which can respond quickly and efficiently to variations in products, product mix and production volume.
The reconfigurable and changeable manufacturing assembly system (iFactory) set up in the Learning Factory environment at the University of Windsor, Canada is one illustration of the beneficial and valuable experiential learning and research into physical and logical enablers of change that can be achieved towards realizing these objectives. This Factory-In-A-Lab environment is the first to focus on systems learning and integration with products development. It is a rich environment conducive to researching a wide range of topics of importance in modern manufacturing systems.
Acknowledgements
The authors are the directors of the Intelligent Manufacturing Systems (IMS) Centre, at the University of Windsor, which carries out advanced research in a wide range of topics relating to the holistic design and manufacturing integration. The contributions to the research publications and graduate dissertations by the researchers in the IMS Centre are acknowledged. The research funding provided by the Natural Sciences and Engineering Research Council (NSERC), and the Canada Research Chairs (CRC) program, as well as the infrastructure grant to establish the Learning Factory provided by the Canadian Foundation for Innovation (CFI) and the Ontario government Ministry of Research Innovation (MRI), and the related industry support are acknowledged.
References
[1] National Society of Professional Engineers (NSPE), 2013, Engineering Body of Knowledge, First Edition, prepared by the Licensure and Qualifications for Practice Committee, 2013.
[2] McKinsey, 2012, "Manufacturing the future: The next era of global growth and innovation", McKinsey Global Institute, McKinsey & Company, Pages: 184, November 2012, www.mckinsey.com/mgi
[3] ElMaraghy, H., Schuh, G., ElMaraghy, W., Piller, F., Schonsleben, P.,Tseng, M., Bernard, A., 2013, "Product Variety Management", CIRP Annals - Manufacturing Technology, 62/2/2013.
[4] Lamancusa, J. S.; Jorgensen, J. E.; Zayas-Castro, J. L.; Ratner, J.;1995, The Learning Factory - A new approach to integrating design and manufacturing into engineering curricula, Session 3225, 1995 ASEE Conference Proceedings June 25-28, 1995 Anaheim, California, pp. 22622269.
[5] ElMaraghy, H., ElMaraghy, W., 2014, Learning Factories for Manufacturing Systems, 4th Conference on Learning Factories, Royal Swedish Academy of Sciences (IVA) and KTH, Stockholm, 27-28 May.
[6] Wagner, U., AlGeddawy, T., ElMaraghy, H., Muller, E., 2012, The state-of-the-art and prospects of learning factories, 45th CIRP Conference on Manufacturing Systems, CMS 2012, May 16, 2012 - May 18, 2012, Athens, Greece: Elsevier, 109-114.
[7] Hummel, V., Westkamper, E., 2007, Learning factory for advanced industrial engineering - integrated approach of the digital learning environment and the physical model factory, in Production Engineering, Oiicyna Wydawnicza Politechniki Wroctawskiej: Krakow, Poland, 21527.
[8] Chryssolouris, G., KNOW-FACT: A Knowledge Partnership for the definition and launch of the European Teaching Factory Paradigm in manufacturing education, Laboratory for Manufacturing Systems and Automation Patras University, accessed 30 January 2015, http://www.knowfact-project.eu/
[9] Leu, M.C., ElMaraghy, H.A., Nee, A.Y.C., Ong, S.K., Lanzetta, M., Putz, M., Zhu, W., Bernard, A., 2013, CAD model based virtual assembly simulation, planning and training, CIRP Annals - Manufacturing Technology, 62/2:799-822.
[10] Wagner, U., AlGeddawy, T., ElMaraghy, H., Muller, E., 2014, Product family design for changeable learning factories, 47th CIRP Conference on manufacturing Systems, CMS 2014, April 28, 2014 - April 30, 2014, Windsor, ON, Canada: Elsevier, 195-200.
[11] ElMaraghy, H., AlGeddawy, T., Azab, A. and ElMaraghy, W., 2011, Change in Manufacturing - Research and Industrial Challenge. Keynote Paper, Proc. of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV2011), Montreal, pp 2-9.
[12] ElMaraghy, W., ElMaraghy, H., Tomiyama, T. and Monostori, L., 2012, "Complexity In Engineering Design and Manufacturing", Keynote Paper, CIRP Annals Manufacturing Technology, Vol. 61/2, pp. 793 - 814.
[13] ElMaraghy, H.A., AlGeddawy, T., 2012 "Co-evolution of Products and Manufacturing Capabilities and Application in Auto-Parts Assembly," Flexible Services and Manufacturing J. (FSMJ), 24(2), pp. 142-170.
[14] Seleim, A., ElMaraghy, H., 2014, "Parametric Analysis of Mixed-Model Assembly Lines Using Max-Plus Algebra", CIRP J. of Manufacturing Science and Technology (JMST), Vol.7, Issue 4, pp. 305-314.
[15] AlGeddawy, T., and ElMaraghy, H., 2013, "Optimum Granularity of Modular Product Design Architecture", CIRP Annals - Manufacturing technology, Vol. 62/1, pp. 151-154, DOI: 10.1016.j.cirp.03.118.
[16] ElMaraghy, H., AlGeddawy, T., Samys, S.N., Espinonza, V., 2013, "A Model for Assessing the Layout Structural Complexity of Manufacturing Systems", Journal ofManufacturing Systems, Vol. 33, Iss. 1, pp. 51-64.
[17] Hanafy, M., ElMaraghy, H., 2014, "A Modular Product - Multi Platform Configuration Model, Int. J. of Computer Integrated Manufacturing (IJCIM) DOI: 10.1080/0951192X.2014.941407.
[18] Kashkoush, M., ElMaraghy, H., 2015, "Knowledge-Based Model for Constructing Master Assembly Sequence". J. ofMfg Systems (JMS), Vol. 34, Issue, pp.43-52.
