Product Design for the Environment: A Life Cycle Approach - Chapter 16 (end) docx

435 Chapter 16 Methodological Framework and Analysis Models for Simulation of the Product Life Cycle Product analysis undertaken from the perspective of Life Cycle Design requires the defi nition of procedures and tools to steer the designer towards the best choices in relation to the product’s performance—conventional and environmental—over its whole life cycle. Design choices can be directly linked to product performance using models of product behavior. With these models it is possible to simulate the life cycle in relation to phenomena of deterioration in performance of materials (due to external factors or loading conditions). This chapter presents a methodological framework and the models for such analysis to allow the simulation of product life cycle at the design stage. The case study reported shows how the simulation method, complemented by analytical tools, can be applied to associate each set of design choices under examination with a broad spectrum of information: the durability and criticality of components and the system, as a function of the period of use; possible faults and the consequent servicing costs; the residual life of components, their possible reuse at the end-of-life, and consequent exten- sion of the system’s useful life; and the environmental impact of the whole product life cycle. The fundamental issues in this chapter were previously introduced in 16.1 Simulation and the Life Cycle Approach Life Cycle Design consists of a design intervention that incorporates all the phases of the product’s life cycle (development, production, use, maintenance and repair, retirement, recovery) in the entire design process, from the phase 2722_C016_r02.indd 4352722_C016_r02.indd 435 11/30/2005 1:53:10 PM11/30/2005 1:53:10 PM © 2006 by Taylor & Francis Group, LLC Chapters 3, 9, 10, and 15. Section 3.1.1). The traditional evolutionary approach is based on the feedback of information fl ows used to improve the design intervention; a powerful alternative can consist of already predicting the consequences of design choices on the product’s life cycle during the solution synthesis phase itself. This is equivalent to simulating the life cycle of the product in the early phases Therefore, it could be extremely helpful to defi ne a methodological frame- work and relative analytical tools in support of the design process, allowing the management of design choices (both at the level of product layout and that of the specifi cations of components), in relation to the product’s conven- tional (functionality, reliability, cost) and environmental performance over its life. When variations in the design choices are proposed, such as reorganiza- tion of the architecture or modifi cation of component geometries and materi- als, these tools must allow the simulation of the resulting life cycle so that it is possible to compare the various alternatives and identify the solutions that best realize appropriate performance goals. With particular regard to the simulation of the fi nal phases of the life cycle (i.e., use and retirement), the objective performances to be optimized must be related not only to the design choices but also to factors of deterioration that can alter the behavior of the 16.2 Approach to the Problem and Methodological Framework With these aims, this chapter proposes a method of simulating product behavior over its life cycle and the relative analytical tools to obtain, for every possible set of design choices: • Indications of the duration, safety, and criticality of single compo- nents and of the overall system, on varying the time of use • Indications of possible failures and evaluations of the resulting servicing costs • Indications of the residual life of components, evaluation of their possible reuse, and quantifi cation of the resulting potential exten- sion of the system’s useful life • Evaluation of the environmental impact associated with the main phases of the life cycle The main diffi culty consists of correlating design parameters with product performance in its life cycle after production. This requires not only a model- ing of the life cycle, but also an appropriate modeling of the product as a basis for simulating its behavior in response to different design choices. 436 Product Design for the Environment 2722_C016_r02.indd 4362722_C016_r02.indd 436 11/30/2005 1:53:11 PM11/30/2005 1:53:11 PM © 2006 by Taylor & Francis Group, LLC of concept defi nition to that of detailed design development (Chapter 3, product and its components over time (Chapter 10). of the design process (Chapter 3, Section 3.2.4). Methodological Framework and Analysis Models 437 The importance of life cycle modeling in the process of product develop- ment, in relation to the environmental impact, has already been demon- strated (Zust and Caduff, 1997). Also, some approaches to life cycle simulation have been outlined with particular reference to modular prod- ucts (Tomiyama et al., 1997) and in more general terms (Kato et al., 2001); see cycle models according to elementary activities that aid the inventory phase of Life Cycle Assessment, in accordance with the indications of ISO14040 standards. The second proposes simulation schemes that provide particular information on the product’s environmental impact (quantifying the fl ow of materials discharged or translating the impact of end-of-life into economic terms) without detailing the correlation with the main design parameters. Furthermore, while introducing the temporal variable governing the simu- lation, these schemes rarely study in depth this concept in relation to the phenomena of performance decay and to the consequent effect on the system’s effi ciency (Hata et al., 1997). As summarized in the scheme in Figure 16.1, the development of the method proposed here makes use of certain tools opportunely correlated with indices expressing the decay of performance over time: • A model of the constructional system, based on the behavior of functional subgroups FIGURE 16.1 Methodological framework and tools. 2722_C016_r02.indd 4372722_C016_r02.indd 437 11/30/2005 1:53:11 PM11/30/2005 1:53:11 PM © 2006 by Taylor & Francis Group, LLC also Chapter 3, Section 3.2.4. The fi rst deals with approaches defi ning life • Certain signifi cant functions that express the system’s performance in relation to the possible strategies of improving the life cycle under examination, which are the optimization of the useful life (through servicing operations on the system) and the recovery of resources at end-of-life (through the reuse and recycling of components) With the support of these tools it is possible to outline a simulation proce- dure allowing the evaluation, already in the design phase, of the product’s possible behavior in the intermediate and fi nal phases of the life cycle (use and end-of-life), according to the main design choices and the duration of use. This behavior must be evaluated using objective functions that are rele- vant to the aims of optimal product design. They must quantify the behavior of the product in relation to three main aspects: • Level of functional effi ciency and safety of the constructional system • Costs of the product in relation to the main phases of the life cycle • Environmental impact of the whole life cycle and the recovery poten- tial of the resources used 16.3 Product Model and Analysis Tools Again r ered in more detail. To correlate the set of design choices and product perfor- mance, the methodological approach proposed here is based on advanced Failure Mode and Effect Analysis (FMEA), scheduling the development of a product behavior model based on its function rather than on its structure (Eubanks et al., 1996; Eubanks et al., 1997). This is based on the defi nition of the main elementary functionalities of the constructional system, on the set of determining variables required for the behavior under examination to take place (initial state) and fi nal conditions reached after the function has taken place (fi nal state), and of the performance characteristics regulating the behavior, which can generally be expressed using mathematical models. With regard to the evaluation of variations in the system’s performance over time, in the method proposed here this can be calculated in relation to different typologies of decay phenomena: • Independent from load conditions (e.g., aging of the materials) • Dependent on the load conditions (e.g., fatigue) Taking into consideration a defi ned set of materials characterized by decay curves, it is possible to evaluate the indices that provide information on the 438 Product Design for the Environment 2722_C016_r02.indd 4382722_C016_r02.indd 438 11/30/2005 1:53:12 PM11/30/2005 1:53:12 PM © 2006 by Taylor & Francis Group, LLC eferring to the scheme in Figure 16.1, the main points can be consid- Methodological Framework and Analysis Models 439 duration of components (Index of Duration—DI) and on safety (Dynamic Criticality Factor —DCF) over the life cycle. On these bases, the simulation of the system’s functionalities allows the identifi cation of possible failures and their classifi cation in terms of their effect on components. It is then possible to derive the criticality of the system through analysis of the confi guration of the model (blocks in a series or parallel), the danger of a failure, and the residual performance level. This information directly infl uences possible strategies for improving the environmental performance of the life cycle considered here and also operations on the system) and the recovery of resources at end-of-life (through the reuse and recycling of components). In this regard, reference is made to the calculation models already available for both the evaluation of the economic impact of servicing systems during their useful life (Gershenson and Ishii, 1993) and for the planning of recovery cycles at respectively, the Life Cycle Service Cost (LCSC) and the Extension of Useful Life (EUL). 16.3.1 Model of System Behavior As mentioned above, design choices and product performance are correlated using a model of the system’s behavior based on advanced FMEA (Eubanks et al., 1996; Eubanks et al., 1997). This model defi nes the main elementary functionalities of the constructional system, the determining variables required for the behavior under examination to take place, the fi nal state after the function has taken place, and the performance conditions regulating main functionality, broken down into elementary behaviors in series and in parallel. BHV s is the s-th behavior in series; if it is constituted by np s behavior in parallel, BHV sp is the p-th behavior in parallel constituting the s-th behav- ior in series. Each elementary behavior is defi ned by: • Components directly involved in the behavior; n s is the number of components involved in the s-th behavior • Mathematical models expressing the performance conditions that regulate the behaviors; nv s is the total number of performance condi- tions for s-th behavior, generally consisting of functions linking performance conditions Pf v to operating conditions, to fi xed and variable geometric parameters, and to the properties of the materials (preconditions) • Performance limits Pf* v , which, compared with Pf v , make it possible to establish whether the behavior takes place correctly (postconditions) 2722_C016_r02.indd 4392722_C016_r02.indd 439 11/30/2005 1:53:12 PM11/30/2005 1:53:12 PM © 2006 by Taylor & Francis Group, LLC discussed in Chapter 9: optimization of the useful life (through servicing end-of-life (Chapter 15). The indicators proposed by these models are, the behavior. Figure 16.2 shows the reference scheme for a system based on a 440 Product Design for the Environment 16.3.2 Evaluation of Performance Decay Simulating the system’s behavior requires an evaluation of the variations in its behavior over time. While distinguishing between the two different typologies of decay phenomena (independent from and dependent on load conditions), it is generally possible to ascribe the phenomena to material performance diagrams of the fi rst type, and the real time of use for phenomena of the second type. Once the materials comprising the system have been chosen and each is characterized by its corresponding decay curve, it is possible to evaluate the indices providing information on the duration of the components and on the level of safety over the time of use, and thus over the life cycle. 16.3.2.1 Duration Index With regard to the fi rst aspect, an index of the duration of the component (DI) is introduced. This is defi ned as the ratio between the estimated physical life of the component t r , determined by the performance required Pf r , and the fi xed useful life t u , which is a design requisite: DI = t t r u (16.1) FIGURE 16.2 Model of system behavior: Reference scheme. 2722_C016_r02.indd 4402722_C016_r02.indd 440 11/30/2005 1:53:12 PM11/30/2005 1:53:12 PM © 2006 by Taylor & Francis Group, LLC (Figure 16.3) where the time variable t represents real time for the phenomena Methodological Framework and Analysis Models 441 Having fi xed the duration of the useful life t u , this index DI depends on t r (i.e., both of the material, since the decay curve varies with this, and of the compo- nent geometry, which determines the working conditions and therefore the required performance Pf r ). The value of DI provides indications of the compo- nent’s operating conditions over the arc of its useful life and allows the quan- tifi cation of both the need for servicing operations and the possibility of reusing the component. In the case where DI < 1, the need to substitute the component f times over the entire arc of the useful life can be anticipated with: f = int DI 1 ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ (16.2) If 1 < DI < 2, the component can be used only once. In the case where DI > 2, the component can be reused r times, with: r = int DI ( ) −1 (16.3) 16.3.2.2 Dynamic Criticality Factor Considering the variation in a component’s level of safety over time, and again . DCF Pf Pf Pf Pf 0 0r ϭ Ϫ Ϫ (16.4) where Pf 0 is the component’s initial performance, Pf is that corresponding to the generic time t, and Pf r is again the required performance. This index FIGURE 16.3 Decay of performance: Reference diagram. 2722_C016_r02.indd 4412722_C016_r02.indd 441 11/30/2005 1:53:12 PM11/30/2005 1:53:12 PM © 2006 by Taylor & Francis Group, LLC referring to Figure 16.3, the Dynamic Criticality Factor (DCF) is introduced: 442 Product Design for the Environment quantifi es the increase in the component’s criticality during its use, and for t = t u expresses the criticality corresponding to the end of use (in this case, the notation DCFu will be used). If Pf u indicates the performance level corresponding to the end of useful life, it is seen that: • If Pf u > Pf r then DCFu < 1; therefore, the component is not critical. • If Pf u < Pf r then DCFu > 1; therefore, the component is critical and is the more critical the higher the value assumed by DCFu. 16.3.2.3 Behavior Criticality Index As above, according to the system model introduced here, each of the system’s behaviors is determined by a set of components. Indicating the generic component correlated to the behavior with C i , and the corresponding value of the dynamic criticality factor at end of use with DCFu i , the Behavior Criticality Index (BCI) is defi ned as: BCI c max DCFu i ϭ ⋅ () (16.5) where c ⑀ [0,1] is an additional factor expressing the criticality of the behavior under examination in relation to the functionality of the whole system. If a “nonbehavior” results in the total arrest of the system, this must be classifi ed with a high c factor, given the importance of the failure. The higher the value of BCI, the more critical the behavior under examination, both for the effect of its failure on the system and for the decrease in performance level, quanti- fi ed by the dynamic criticality factor. The distribution of the values assumed by BCI for each behavior on varying the design parameters allows an analy- sis of the criticality of the system under examination. 16.3.3 Analysis of Life Cycle Strategies The values assumed by the indices of performance decay introduced above directly condition the possible strategies for improving the environmental behavior of the life cycle (i.e., the strategies for improvement of resource the useful life (through servicing operations) and recovery of resources at end-of-life (through component reuse and recycling). The following mathe- matical reference models can be used to quantify these behaviors. 16.3.3.1 Life Cycle Service Cost The strategies for optimizing the useful life of products include interventions of diagnosis, maintenance, repair, substitution, and any other operations that 2722_C016_r02.indd 4422722_C016_r02.indd 442 11/30/2005 1:53:13 PM11/30/2005 1:53:13 PM © 2006 by Taylor & Francis Group, LLC exploitation, as were introduced in Chapter 9). These include optimization of Methodological Framework and Analysis Models 443 Section 9.2). For an evaluation of the effect that design choices can have in terms of ease of servicing, mathematical models that express the life cycle service costs can be used (Gershenson and Ishii, 1993). Given a component requiring several service operations, the cost of the w-th service operation on the i-th generic component is given by: Cs tl cl c iw iw iw i ϭϩ⋅ (16.6) where tl iw is the time of intervention, cl iw is the cost of the intervention per unit time, and c i is the cost of the component or of the material required in the intervention. Considering as service interventions the substitution of failed components, for a system consisting of n components C i , the total Life Cycle Service Cost (LCSC) is expressed by: LCSC Cs iw w = 1 f i = 1 n i ϭ ∑∑ (16.7) where f i is the number of substitution interventions required for the i-th component, defi ned by Equation (16.2). 16.3.3.2 Recovery Cycles and Extension of Useful Life The problem of planning recovery cycles in relation to the duration of the components has already been treated by the authors (Giudice et al., 2003), and model was proposed that takes account of the environmental impact of produc- ing the i-th component, EI Prod i . This can be expressed in terms of the eco-indica- tors of the materials and processes, defi ned by Equation (15.9) of Chapter 15: EI = ei W ei Prod Mat i Pr i k=1 h pr i ii k i k ⋅⋅ ∑ ϩ␩ (16.8) Considering that it is possible to anticipate a number of recovery cycles m, the recovery fraction that can be associated with the j-th cycle (in terms of environmental impact) can be expressed by Equation (15.10) of Chapter 15: ⌽ j EI ij i = 1 n Prod Prod i = 1 n = r EI EI i i ∑ ∑ ⋅ (16.9) 2722_C016_r02.indd 4432722_C016_r02.indd 443 11/30/2005 1:53:13 PM11/30/2005 1:53:13 PM © 2006 by Taylor & Francis Group, LLC may be necessary to ensure the correct functioning of the system (Chapter 9, some signifi cant results have been reported in Chapter 15, where a calculation 444 Product Design for the Environment where r ij is a binary coeffi cient that assumes unitary value only in the case where the i-th component is reusable at the j-th cycle. This is, therefore, easily expressed as a function of r, the number of possible component reuses, given by Equation (16.3). Hypothesizing a constant duration of all the reuses, equal to the fi xed dura- tion of the fi rst use t u , the function quantifying the extension of useful life is EUL = t j = 1 m u Φ k EI k j = ∏∑ ⎡ ⎣ ⎢ ⎤ ⎦ ⎥ ⋅ 1 (16.10) This function, which expresses the extension of the life of original compo- nents within the life cycle of the product, can be considered an indicator of savings in resources according to their different environmental impacts. 16.4 Defi nition of Objective Functions As above, the product’s behavior in the life cycle must be evaluated in rela- tion to its functional capacities, to the economic costs, and to the environ- mental performance. With this aim, and with reference to the mathematical models proposed so far, the following objective functions are proposed: • The criticality index of the constructional system, identifi ed in the mean or maximum values assumed by the BCI indices expressing, through Equation (16.5), the criticality of each behavior of the system • The product’s costs over the life cycle, which, ignoring the costs of product retirement, can be quantifi ed by the production and servic- ing costs, the latter expressed by LCSC defi ned in function (16.7) • The potential extension of useful life of the resources used in the system through the recovery of components, expressed by EUL defi ned in function (16.10) • The environmental impact of the life cycle, quantifi ed by the sum of the impacts associated with the phases of production, use (servic- ing), and retirement The last function requires further development of the mathematical models, as discussed below. These objective functions evaluated for each design choice are suit- able for treatment using multiobjective analysis, with the ultimate aim of 2722_C016_r02.indd 4442722_C016_r02.indd 444 11/30/2005 1:53:13 PM11/30/2005 1:53:13 PM © 2006 by Taylor & Francis Group, LLC defi ned as (see Chapter 15, Section 15.3.6): [...]... (transmission of the torque from the motor shaft to the clutch): the components involved, the mathematical models expressing the required performance (in this case the structural strength), and the verification of the final state of the behavior 16. 5.2 Performance Evaluations and Analysis of Criticality Having developed the behavior model, the analysis continues with the simulation of the functional performance... equal to the maximum value assumed by each single component (reported in brackets), and a value of r equal to the minimum On the basis of the values assumed by the factor DCFu for each component, it is then possible to analyze the distribution of the criticality index of the behaviors BCI, evaluating their maximum value (indicating the most critical behavior) and estimating the mean criticality of the. .. whole life cycle In the methodological framework proposed in this chapter, the direct relation between design choices and final performance is obtained through a behavior model of the product The proposed model allows the simulation of the life cycle in terms of phenomena of decay in performance of the materials As shown by the case study reported, the simulation method and analytical tools can be applied... environmental impact of the life cycle 16. 6 Summary In the context of Life Cycle Design, it is necessary to define analytical procedures and tools allowing the management of design choices (both at the level of product layout and at that of the specifications of single components) in relation to the conventional performances (functionality, safety, cost) and environmental performances of the product over... where the values of the functions under examination have been normalized (the reciprocal of EUL is considered to have to minimize all the functions) 16. 5.4 Analysis of the Environmental Impact of the Life Cycle Finally, it is possible to evaluate the environmental impact of the entire life cycle EILC for the more interesting design alternatives IIa and IIb, using the FIGURE 16. 6 Design alternative... reusable components As in other previous chapters, all the eco-indicators considered are evaluated according to the Eco-indicator 99 method (Chapter 4, Table 4.3) 16. 5 Simulation and Analysis of Results This simulation procedure follows the development of the models proposed and is summarized in Figure 16. 1 It is based on a direct relation between the design parameters that constitute the variables... component, the table gives the chosen material; the number of expected loading cycles tu related to the fixed mission; the required performance level Pfr (mechanical strength in MPa); and the number of loading cycles tr guaranteed by the chosen material, in relation to Pfr (tr assumes a value of infinity when Pfr is below the fatigue limit of the material) The same table then shows the corresponding values assumed... obtain, for each design alternative examined, indications on the duration, safety, and criticality of single components and of the © 2006 by Taylor & Francis Group, LLC 2722_C 016_ r02.indd 451 11/30/2005 1:53:15 PM 452 Product Design for the Environment FIGURE 16. 8 Comparison between design alternatives IIa and IIb FIGURE 16. 9 Comparison between design alternatives IIa and IIb: Radar diagram overall... on varying the time of use; indications of possible expected failures and evaluations of the resulting servicing costs associated with the life cycle; indications of the residual life of components, evaluations of their possible reuse, and quantification of the resulting extension of the system’s useful life; and evaluations of the environmental impact associated with the main phases of the life cycle, ... system (for design alternative I, mean BCI is equal to 0.80) 16. 5.3 First Analysis of the Performance in the Life Cycle Again referring to Table 16. 1, from the values assumed by f and r, it is possible to predict the system’s poor performance, both in terms of the use phase © 2006 by Taylor & Francis Group, LLC 2722_C 016_ r02.indd 448 11/30/2005 1:53:14 PM Performance evaluation and analysis of criticality: . proce- dure allowing the evaluation, already in the design phase, of the product s possible behavior in the intermediate and fi nal phases of the life cycle (use and end-of -life) , according to the. by BCI for each behavior on varying the design parameters allows an analy- sis of the criticality of the system under examination. 16. 3.3 Analysis of Life Cycle Strategies The values assumed. external factors or loading conditions). This chapter presents a methodological framework and the models for such analysis to allow the simulation of product life cycle at the design stage. The