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Unformatted text preview: See discussions, stats, and author profiles for this publication at: Computer Aided Manufacturing (CAM) Chapter · January 2012 DOI: 10.1007/978-3-642-20617-7_6550 CITATIONS READS 4 14,631 3 authors: S. Makris Dimitris Mourtzis University of Patras University of Patras Lab. For Manufacturing Systems and Automation (LMS) 101 PUBLICATIONS 2,176 CITATIONS 240 PUBLICATIONS 4,301 CITATIONS SEE PROFILE George Chryssolouris University of Patras 386 PUBLICATIONS 10,094 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: EP-20903: RIDER View project DIVERSITY View project All content following this page was uploaded by Dimitris Mourtzis on 04 May 2016. The user has requested enhancement of the downloaded file. SEE PROFILE C CAD ▶ Computer-Aided Design (i.e., number of shifts). The outcome of capacity planning is an investment strategy and resources utilization plan defined based on optimal policies that try to fulfill demand and its variation while considering various system’s operational objectives and constraints. CAM ▶ Computer-Aided Manufacturing Capacity Planning Hoda ElMaraghy1 and Ahmed M. Deif2 1 Canada Research Chair in Manufacturing Systems, Industrial and Manufacturing Systems Engineering, Intelligent Manufacturing Systems (IMS) Centre, University of Windsor, ON, Canada 2 Industrial & Service Engineering Program, Nile University School of Engineering & Applied Science, Cairo, Egypt Definition Capacity is defined, in the context of manufacturing, as the maximum rate of production and the ability to yield production. Capacity planning is concerned with defining all resources and factors that affect the ability of a manufacturer to produce including equipment, labor, space, and time Theory and Application History Capacity planning is motivated by manufacturers’ desire to meet customers’ demand. Uncertainty of the customer’s demand increases the complexity of capacity planning. Capacity planning is often confused with scheduling since both deal with managing production to meet demand. However, a major distinction between both activities is that capacity planning is focused on meeting the anticipated demand on the strategic and tactical level, while scheduling focuses on how to meet demand on the shop floor operational level. This distinction brings about different scope, strategies, models, and tools used in meeting the capacity and resources scheduling demands. Capacity planning was classically addressed as a problem of capacity expansion. However, modern planning is concerned with both the reduction and expansion of capacity given the turbulence in markets today. Another major difference between classical and modern capacity planning is their enabling technologies. The International Academy for Production Engineering (ed.), CIRP Encyclopedia of Production Engineering, DOI 10.1007/978-3-642-20617-7, # CIRP 2014 C 124 The techniques used for planning capacity expansion are classical techniques such as adding work shifts, manpower, new production facilities, and subcontracting, whereas for modern capacity planning technologies such as modular design, reconfiguration, open control architectures, and changeability strategies are used, in addition to classical approaches, to implement more scalable, flexible, and successful capacity planning policies. Theory About the Capacity Planning Problem The major decisions in any capacity planning activity are: • What is the best magnitude of capacity expansion/reduction? • When is the best time to expand/reduce production capacity? • What is the best type of capacity expansion/ reduction? • What is the best location for expansion/ reduction? The word “best” (sometimes called optimal) in the previous questions means satisfying the market demand at a minimum cost, effort, and/ or time. A capacity planning policy should answer the previous questions. However, in practice it is difficult to devise a capacity planning policy that satisfies all questions at the same time in an optimal manner. The main inputs required to answer the previous questions are (a) the planning horizon, (b) the costs of capacity expansion and/or reduction, (c) the different system constraints and time parameters, (d) the production strategic policies, and (e) the demand forecast. The demand pattern is a very important parameter in developing any capacity planning policy or plan. It describes the demand over a certain time horizon which is usually the capacity planning period indicated by management. The demand patterns can take deterministic or stochastic forms depending on the desired accuracy in capturing uncertainty-associated demand as well as availability of information. Figure 1 illustrates the main dimensions of the capacity planning problem. Capacity Planning Market Demand How much? Capacity Which type? When? Time Capacity Planning, Fig. 1 Capacity planning problem’s questions Capacity Planning in Various Manufacturing Systems Dedicated Mass Production Systems Capacity planning for dedicated manufacturing lines and mass production systems is carefully optimized a priori to define the best size of manufacturing facilities and resources given the expected steady and large production volume. These systems sometimes face the need to expand or shrink production capacity due to changes in market conditions. Such changes beyond the initial design are not easy to implement in dedicated production systems and may require duplication of the lines or of certain machines in the line or even the expansion of the whole facility into multiple facilities if justifiable. It can be said that capacity planning in these dedicated systems is normally done on a macroscale at the system level and without considering potential capacity reduction/ expansion (except in very few cases). This is understandable since these dedicated mass production systems were economically justified and designed for production of a specific part/product at high volume without dealing with variety or mix of products. Typically capacity expansion methods in dedicated systems have the objective of minimizing the discounted costs associated with expansion. These include expansion cost, congestion, idle capacity, shortages, maintenance, and inventory (Examples include Kalotay 1973 and Erlenkotter 1977). Figure 2 shows the capacity planning approach in dedicated production systems. Capacity Planning C 125 Capacity Planning, Fig. 2 Capacity planning approach in dedicated production systems (Asl and Ulsoy 2002) Demand Xi ti Flexible Manufacturing Systems Capacity planning for flexible manufacturing systems (FMS) is considered a complicated task. Functionality planning usually receives much more attention in FMS than capacity planning, and it is mainly accomplished by using multipurpose, multiaxes CNC programmable machines. The problem of capacity planning in FMS arises from the great alternatives of identical and nonidentical machines available in the system with multiple functionalities. Capacity planning is very expensive in flexible manufacturing systems since these systems are planned for producing a product family defined a priori in the mid-volume and mid-variety range of products. Capacity planning in these systems is viewed as optimally satisfying the demand for multiple products within the existing family boundary using existing built-in capacity change alternatives (programmable machines). The capacity planner will aim to find the optimal control of production flow (alternatives) within the FMS systems to balance between investment cost and lost sales cost (Kimemia and Gershwin 1983). Reconfigurable Manufacturing Systems Capacity planning in advanced manufacturing systems such as reconfigurable manufacturing systems (RMS) and changeable manufacturing systems is usually referred to as “capacity scalability.” ElMaraghy, H. (2005) explains the dimensions of capacity scalability in RMS by identifying and classifying the scalability characteristics into “physical scalability” and “logical scalability” attributes. Examples of physical capacity scalability enablers include the adding or removing of C Optimum capacity based on minimal discounted cost Time material handling equipment, machines, machine modules, such as axes of motions or heads, as well as tools or other components. Examples of logical capacity scalability enablers include increasing or decreasing the number of shifts or the number of workers as well as outsourcing. Modular components’ design and interfaces as well as open control architecture are basic enabling technologies required for “Plug’n Play” cost-effective way of achieving physical capacity scalability in RMS (Koren et al 1999). Modeling Capacity Planning Problems The interest in modeling the capacity planning problem goes back to the middle of the nineteenth century. A capacity planning model typically uses deterministic demand that grows linearly with time and balances between the cost of installing capacity before it is needed and the economies of scale savings of capacity expansion/reduction. The model determines the type and sizes of facilities to be added/removed and when so that the present worth of all capacity changes is minimized while meeting forecasted demand. Examples of such classical and static models can be found in Manne (1967) and Freidenfelds (1981), while a good review on classical capacity expansion models can be found in Luss (1982). Various researchers attempted to enhance such basic notion and models by considering stochastic demands and dynamic lot sizes, accounting for various expansion costs, considering inventory along with capacity and finally implementing different classical optimization techniques. C 126 Modeling and formulating capacity planning problems was further considered from a more dynamic perspective due to the increased level of uncertainty as well as the fast advancements in manufacturing systems technologies. Dynamic modeling approaches included the application of control theoretic methods and feedback loops to control capacity under uncertainty with real-time information of both the market and production system. Examples of this approach include the work of Wiendahl and Breithaupt (2000), Duffie and Falu (2002), and Deif and ElMaraghy (2006). System dynamics was used to capture the dynamics associated with the capacity planning or scalability problem and the various parameters influencing it. Example of this approach for capacity planning includes the work of Deif and ElMaraghy (2009). Other approaches to understand the dynamic nature of capacity planning were through the application of nonlinear dynamic analysis, chaos theory, dynamic optimization, simulation, and stochastic analysis (examples include Radons and Neugebauer 2005 and Scholz-Reiter et al. 2002). The common objective of all such methods and models is to determine the best capacity planning policies which advise manufacturers on which, when, where, and how to expand/ reduce capacity in response to varying and often uncertain demand. Importance of Capacity Planning Sound capacity planning models and strategies are essential to maximize the potential of satisfying demands while minimizing cost and remaining profitable. Any unmet demand is a lost opportunity and any unused production capacity is a waste of money and resources. Effective capacity planning is needed more than ever today to match demands to ability to produce and to rationalize outsourcing and subcontracting. CAPP References Asl FM, Ulsoy AG (2002) Capacity management via feedback control in reconfigurable manufacturing systems. In: Proceedings of Japan-USA symposium on flexible manufacturing automation, Hiroshima, Japan Deif A, ElMaraghy W (2006) A control approach to explore the dynamics of capacity scalability in reconfigurable manufacturing systems. J Manuf Syst 25(1):12–24 Deif A, ElMaraghy H (2009) Modeling and analysis of dynamic capacity complexity in multi-stage production. Prod Plann Contr Manag Op 20(8):737–749 Duffie N, Falu I (2002) Control-theoretic analysis of a closed-loop PPC system. Ann CIRP 51(1):379–382 ElMaraghy H (2005) Flexible and reconfigurable manufacturing systems paradigms. Int J Flex Manuf Syst 17(4):261–276 (Special issue on reconfigurable manufacturing systems) Erlenkotter D (1977) Capacity expansion with imports and inventories. Manage Sci 23(7):694–702 Freidenfelds J (1981) Capacity expansion: analysis of simple models with applications. Elsevier North Holland, New York Kalotay AJ (1973) Capacity expansion and specialization. Manage Sci 20(1):56–64 Kimemia J, Gershwin SB (1983) An algorithm for the computer control of a flexible manufacturing system. IIE Transactions 15(4):353–362 Koren Y, Heisel U, Jovane F, Moriwaki T, Pritschow G, Ulsoy G, Van Brussel H (1999) Reconfigurable manufacturing systems (Keynote paper). CIRP Ann Manuf Technol 48(2):527–540 Luss H (1982) Operation research and capacity expansion problems: a survey. Oper Res 30(5):907–947 Manne AS (1967) Investments for capacity expansion, size, location, and time-phasing. MIT, Cambridge Radons G, Neugebauer R (eds) (2005) Nonlinear dynamics of production systems. Wiley-VCH, Weinheim Scholz-Reiter B, Freitag M, Schmieder F (2002) Modelling and control of production systems based on nonlinear dynamics theory. Ann CIRP 51(1):375–378 Wiendahl H, Breithaupt J (2000) Automatic production control applying control theory. Int J Prod Econ 63(1):33–46 CAPP ▶ Computer-Aided Process Planning Cross-References ▶ Manufacturing System ▶ Production Capacity ▶ Production Planning and Control Carbide ▶ Cemented Carbides Cemented Carbides CAT ▶ Computed Tomography CBN ▶ Superabrasives Cemented Carbides Markus Groppe Sandvik Coromant GmbH, D€usseldorf, Germany Synonyms Carbide; Hard metal; Tungsten carbide Definition The cemented carbides are a range of powdermetallurgical composite materials, which consist of hard carbide particles bonded together by a metallic binder (Sandvik Hard Materials 2012). The proportion of carbide phase is generally between 70 % and 97 % of the total weight of the composite and its grain size averages between 0.4 and 10 mm. In cemented carbides, these hard particles are mainly tungsten carbides (WC) compared to cermets (TiC, TaC, NiC) or ceramics where metal oxides or nitrites are used. Theory and Application Cemented carbide is one of the most successful composite engineered materials. Its unique combination of strength, hardness, and toughness satisfies the most demanding applications. 127 C History The development of this highly optimized cutting material is strongly connected to the history of the industrial manufacturing. First important invention which had very practical impact on the cutting technology was the invention of highspeed steel (HSS). With the new cutting material 3, higher cutting speeds could be applied. High-speed steel has even today importance as a material for applications where tools with sharp cutting edges are needed, e.g., in reaming or fine finishing (Fig. 1). The second important step was the invention of the cemented carbides. This material was a by-product of an entirely different technology. William D. Coolidge had invented the so-called ductile tungsten, applied to manufacture the filaments for the incandescent light bulbs. To form these filaments, diamond drawing dies were used which were very expensive. A research institute in Germany “Osram Studiengesellschaft” got the order to find a replacement material for the diamond tools. The idea was to take the material cemented carbide and a binding material and have it sintered. In 1923, a young scientist, Karl Schroeter, was given patent no. 498349A for “the process to manufacture a hard, melting alloy for working tools especially drawing dies.” This material was not a melting alloy; it was sintered. The patent started a new age of tool material (Schro¨ter 1923). It was soon sold to a company who was very interested in cutting materials, namely, to the Krupp Company in Essen in 1926. They introduced it under the trademark WIDIA (wie Diamant, like diamond). One year after, in 1927, the new cutting material was displayed at the Leipzig Exhibition. While this was not primarily a material innovation, it was, however, a great step forward in productivity with 4 higher cutting speeds than HSS, which is connected to a five times higher productivity (Toenshoff and Denkena 2013). Engineering Cemented Carbide To fulfill the requirements of a high productive cutting material, the tool must C 128 Cemented Carbides, Fig. 1 Development of cutting materials (Toenshoff and Denkena 2013) Cemented Carbides 1600 m/min 1000 coated carbides hourly cutting speed vc60 C 400 cemented carbide 160 63 HSS 25 10 tool steel 4 1900 1894 1920 1926 1940 1960 1967 1980 2000 year of development DIAMOND Hardness (Sandvik Coromant and Society of Manufacturing Engineers 1996): • Be hard to resist flank wear and deformation • Have a high toughness to resist fracture • Be chemically inert to workpiece • Be chemically stable to resist oxidation and dissolution • Have good resistance to thermal shocks The most commonly used materials are shown schematically relative to their hardness and toughness properties in Fig. 2. Diamond (PCD) is the hardest, of all, followed by cubic boron nitride (CBN) and ceramics (A1203, SiC, SIALON, etc). The super hard materials all suffer from lower toughness and poor resistance to sudden fracture; the cemented carbides have a unique combination of high hardness and good toughness within a wide range and thus constitute the most versatile hard materials group for engineering and tooling applications. A key feature of the cemented carbide is the potential to vary its composition so that the resulting physical and chemical properties ensure maximum resistance to above-listed wear mechanism. In addition, the wide variety of shapes and sizes that can be produced using modern powder-metallurgical processing offers CBN SiC Al2O3 Si3N4 CEMENTED CARBIDE HIGH SPEED STEEL STELLITE Toughness Cemented Carbides, Fig. 2 Application area of cemented carbides (Source: [Date of access: 16.5.2013]) tremendous scope to design cost-effective cutting solutions (Toenshoff and Denkena 2013). The unique property of cemented carbide is that it offers a safer and more dependable solution than any other known material to one of the toughest problems which engineers have to contend with – reliability. Cemented Carbides 129 C Cemented Carbides, Fig. 3 Microstructure of cemented carbides (Source: . allaboutcementedcarbide. com/02.html [Date of access: 16.5.2013]) C Reliability is often a problem of wear. Wear resistance is the most outstanding feature of cemented carbide. Cemented carbide can also withstand deformation, impact, heavy load, high pressure, corrosion, and high temperature – often the only material that can fulfill these requirements satisfactorily. One restriction is the limited capacity to withstand tensile stresses which has to be considered for the application. Over the years, cemented carbides have also proven their superiority in a great number of other tooling and engineering applications than cutting. Tungsten carbide (WC), the hard phase, together with cobalt (Co), the binder phase, forms the basic cemented carbide structure from which other types of cemented carbide have been developed. In addition to the straight tungsten carbide – cobalt compositions – cemented carbide may contain varying proportions of titanium carbide (TiC), tantalum carbide (TaC), and niobium carbide (NbC). These carbides are mutually soluble and can also dissolve a high proportion of tungsten carbide. Also, cemented carbides are produced which have the cobalt binder phase alloyed with, or completely replaced by, other metals such as iron (Fe), chromium (Cr), nickel (Ni), molybdenum (Mo), or alloys of these elements. There are three individual phases which make up ...
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