Myeon-Gyu Jeong, James R. Morrison and Hyowon Suh ISysE, KAIST Recent Directions Toward Automated Life Cycle Assessment

2 Presentation Overview  1. Introduction  2. LCA via CBR  3. Case study  4. Concepts for LCA at arbitrary levels of detail  5. Concluding remarks 2

1. Introduction 1.Problem definition 2.Related work 3.Motivation 4.Research purpose and scope 5.Comparison to related work 3

4 1.1 Problem definition Planning Concept development System-level design Detail design Testing and refinement Production ramp-up Mission Approval Concept review Production approval Generic Product Development Process System spec. review Critical design review [Design] - known Define part geometry Choose materials Assign tolerances Complete industrial design control doc. [Manufacturing] - yet unknown Piece part production processes Design tooling Define quality assurance processes Begin procurement of long-lead tooling Many Iteration Cycles for Design Improvement Initial DesignImproved Design Input Environmental Impact Evaluation Life Cycle Assessment (LCA) Preceding conditions for eco improvement Standardized by ISO 14040~3 series

5 1.1 Problem definition Concept and General Procedure of Life Cycle Assessment Goal Definition : which products or service are assessed? How to use the result of LCA? Scope definition Life cycle stage Unit process MaterialsResources Parts Ass’y ProductUseDisposal IncinerationLandfill Recycle & Reuse Recycle Inventory analysis Measure envir. burden CO 2 SO x NO x Emission to air T-N T-P metals Emission to water Impact analysis Impact to nature and human Global warming Ozone layer depletion AcidificationWater pollution Detection of important issue Check the reliability of data Interpretation Review and reporting Make reportCritical review Considerable time and money to collect relevant data

6 1.1 Problem definition [Inventory Analysis] Resources Materials Parts Assembly Product Use Disposal Input 1, 2, …,noutput 1, 2, …,n Input 1, 2, …,noutput 1, 2, …,n Input 1, 2, …,noutput 1, 2, …,n Input 1, 2, …,noutput 1, 2, …,n Input 1, 2, …,noutput 1, 2, …,n Input 1, 2, …,noutput 1, 2, …,n Input 1, 2, …,noutput 1, 2, …,n  Collecting all relevant data and information throughout the entire life cycle at the detail design stage is impossible  No matter what data is available, it requires considerable time and money  In case of the product have short development cycle such as cellular phone, LCA put a burden on whole PDP Limitations of LCA is techniques that purposely adopt some sort of [simplified approach] to life cycle assessment Streamlined LCA

7 1.2 Related work Material (Acquisition) Manufacture Use Disposal Material, Energy Air, Water, Waste Material, Energy Air, Water, Waste Material, Energy Air, Water, Waste Material, Energy Air, Water, Waste Environmental Impact BLACK BOX (Numerical &Statistical) Results Product Attribute + Existing Full LCA results Environmental category Life cycle stage Scoring based on checklist Product Attributes Matrix Operation (Weighting, Sum.) Results LCA scopingNumerical LCA Matrix LCA Compare results Learning/Fitting [Output] (Omit indifferent process or in/output) Ines Sousa(2000), Seo, K.K(2006)Graedel & Allenby(1995), Pommer(2001)Christiansen, K.(1997), Hur, T.(2003) No systematic procedure to select life-stage and part/module of product Hard to learning or fitting the black box and applicable to only specific product category & environmental stressor Only qualitative assessment and low accuracy of result Suggested by Limitations [Input] Three basic levels of LCA (Wenzel 1998)

8 1.3 Motivation Design output LCA result In general, most enterprises develop new products by revising or reusing the similar previous product, If we collect the LCA result of previous product, then we can estimate the LCA of new product from previous cases Power trainElectricsInterior Power trainElectricsInterior LCA result for regulation, certification Battery Engine Brake module Seat Case based reasoning for LCA Case retrievalAdaptation Complicated LCA process

9 1.4 Research purpose and scope Planning Concept development System-level design Detail design Testing and refinement Production ramp-up Mission Approval Concept review System spec. review Critical design review Production approval Many Iteration Cycles Generic Product Development Process Design for Environment CBR for LCA Case indexing - Case clustering by k-medoids Case adaptation - Geometry attribute based linear modeling algorithm - Multi-regression analysis Case representation - FBSE expressions - Relations Case retrieval & selection - Similarity measurement and computation Support

Comparison to related work Standard LCALCA scopingMatrix based Substitution of LCI DB Proposed method PrecisionHighMediumLowMediumMedium-High Key technique Allocation, mass balance models Allocation, mass balance, empirical models Checklist based quality assessment ANN models CBR, FBSE, Regression Data typeQuantitative Qualitative/ Quantitative Quantitative Utilization of LCI DB Yes NoneYesNone Modeling effort required HighMediumLowHighMedium Required Resource HighestHighLowMedium Intended user Environmental expert Product designer Environmental expert Product designer Applicable to enterprise LowMediumHighMediumHigh

2. LCA via CBR 1.Overview of the method 2.An FBSe representation 3.Similarity measure 4.Case indexing 5.Case retrieval and selection 6.Case adaptation 11

Overview of the method 12 Find similar cluster Retrieve close case set to P Case adaptation Estimated LCA result Case formulation Clustering (k-medoid) Case building Flow Consider Legend Save as new case Old case w/ LCA Product/part specification Function decomposition FBSE modeling LCA result Product/part specification Function decomposition FBS modeling New problem w/o LCA Behavior similarity Structure (Non-numeric) Function similarity Structure (Numeric) Similarity sum Weighting Similarity measure Construct new cluster set Select Adaptation attribute Regression modeling Find optimal solution set Preprocessing area Apply solution set to N

An FBSE representation 13 [Function-Behavior-State(structure) model] by Umeda Function: The purpose of the design (e.g. the purpose of a fan is to move the air) Behavior: The principle used to achieve the function (e.g. propeller fan is a kind of fan to move the air) Structure: The physical characteristics of the component (e.g. geometry size, material, color) Environmental impact: The component effect in the eleven eco-indicator 99 categories (e.g. climate change, ozone layer) Problem space Solution space [New FBSE model] Fan FunctionTo move the air BehaviorCross flow type StructureNumber of blade: 20 Diameter: 50mm Length: 230mm … Environmental impact Climate change: Pt Radiation: 0.324mPt … [Example of FBSE expression] Two shafts have same geometry and material To support load To transfer torque Require lubricant at “Use” stageNo input at “Use” stage Different LCA result T1T1 T2T2 B F A

An FBSE representation 14 Function Behavior Structure Environmental effect FunctionFvFv FoFo fAfA (move)(air) ……… Behaviorw1w1 w2w2 w3w3 w 32 (cross)(flow)(fan)… (single)(type)-- … ………… Structural 1 (material)(galvanized),(sheet),(steel) (color)(silver) (surface),(treatment)(powder),(coating) …… Structural 2 (mass)220 (wheel), (diameter)80 (length)230 (number),(of),(blades)26 (number),(of),(plates)4 …… Structural 3 (revolution),(speed) ……… Env. effectLPR 1 (carcinogens) … R 11 (fossil fuels) eAeA 1 (raw material acquisition)………… 2 (part manufacturing process) (cutting),(by),(milling) … (assembly),(by),(rolling) … …………… 6 (Disposal)…………

Similarity measure Function Attribute Behavior Attribute Structure Attribute Environmental effect Functional basis by Hirtz et al., at NIST Standard or general engineering terminology Eco-indicator 99 method Nonnumerical value type Numerical value type Product specifications or BOM terminology Function (f) consist of pairs of words: function verb (F v ) and function object (F o ) Ex) ((move), (air)) Behavior (b) consists of up to 32 phrases Ex) ((cross, flow, fan)) The environmental effect e  E := L × P 32 × R 11 L is the the set of life cycle stage Structures consist of three parts S=S 1 ×S 2 ×S 3 S 1 is a set of two phrase vectors used for nonnumeric descriptions Ex) ((material), ((galvanized), (sheet), (steel))) S 2 is a set of vectors, each consisting of a phrase and a real number Ex) ((mass), 220) S 3 is a set of vectors, each consisting of a phrase and two real numbers Ex) (((revolution), (speed)), 500.0, ) Tracing the degree of kinship from hierarchical function structure Cosine similarity Point matching function Interval matching function Attribute type LayerModeling languageSimilarity Measure function

Similarity measure 1. Function verb similarity 2. Function object similarity 3. Behavior similarity 4. Structure similarity a) Two phrase vectors used for nonnumeric descriptions Where, the indicator function I(x, y) = 1, if x=y, and 0, otherwise. b) Structural descriptions with real number values c) The set of vectors, each consisting of a phrase and two real numbers Where, I 2 (x, y) = 1, if x < y, and 0, otherwise 5. Overall similarity measure

Case indexing c1c1 c2c2 c3c3 c4c4 c5c5 c6c6 c7c7 c8c8 C1C1 C2C2 c1c1 c3c3 c5c5 c8c8 c2c2 c4c4 c6c6 r1r1 r2r2 c7c7 k-medoids clustering*

18 c2c2 c4c4 c6c6 r2r2 (v 2 ) c1c1 c5c5 C2C2 c7c7 c3c3 c2c2 c4c4 c6c6 r3r3 (v 3 ) c1c1 c5c5 C3C3 c7c7 c3c3 Case memory c2c2 c4c4 c6c6 c7c7 r1r1 (v 1 ) c1c1 c3c3 c5c5 N r C1C1 2.5 Case retrieval and selection c3c3 c6c6 r c7c7 N Selected case set C N := {c i  C R : U(c i,N) ≤ r}

Case adaptation For case t(j) (mass)220 (wheel), (diameter)80 (length)230 For N (mass)180 (length)145 (number),(of),(blades)26 Basis for linear regression (length) (mass) c t(j)  C N (l,p) c3c3 c6c6 r c7c7 N z t(j) contains 11 real numbers for the ecological effects of that case for life cycle l and unit process p. Where, t(j) is the original case index Each row of E contains the 11 errors for the eco-impact categories for a particular case By least square error minimization, optimal decision variable values will be: The estimated ecological effect row vector z N  R 1×11 for new product module N in the life cycle stage l for unit process p is:

3. Case study 1.Outline of case study 2.Case memory organization by k-medoids clustering 3.Case adaptation and results 1.Case scenario 1 2.Case scenario 2 20

Outline of case study Simple Specifications Material Plate Rolled aluminum (0.6T) Blade Rolled aluminum (0.2T) Mass (g)123 Wheel diameter (mm)60 Length (mm)230 No. of blade26 No of plate4 Max. RPM2000 Impeller profileCross flow Flow typeSingle Goal definition Estimate eco impact values of cross flow fan of vehicle air purifier Intended user: design engineer Scope definition Interested area is from raw material acquisition to part assembly (Upstream process) Raw material acquisition Part manufacturingAss’yTransportationUseDisposal Interested Area Target item New problem P: Cross flow fan in vehicle air purifier

Case memory organization by k-medoids clustering Cluster No Medoid No Distance Distance of P of each cluster medoid Backward curved Forward vaned Axial flowCross flow Backward inclined Backward vaned Strip Tablock (Fergas) SingleDoubleSingleDouble Centrifugal PropellerTubeaxial Mixed flow Classification of Impeller(blade) profile Total 100 cases were collected Environmental impact was evaluated by SimaPro 7 (Commercial SW) [Case memory of fan] Cluster 2 is the closest cluster to P

Case adaptation and results – Case scenario 1

Case adaptation and results – Case scenario 1 Avg 2.56%

Case adaptation and results – Case scenario 2

Case adaptation and results – Case scenario 2 Avg 6.88%

4. Concepts for LCA at arbitrary levels of detail 27

Concepts for LCA at arbitrary levels of detail Product A F A Sub-product A1 F A1 Sub-product A2 F A2 Component A3 F A3, B A3, S A3 Component A4 F A4, B A4, S A4 Component A5 F A5, B A5, S A5 Component A6 F A6, B A6, S A6 Electric fan {(make), (wind)} Fan {(move), (air)} Motor {(rotate), (fan)}

29 Model C F C Sub-model C1 F A1 Sub-model C2 F A2 Sub-model C3 F A3, B A3, S A3, E A3 Sub-model C4 F A4, B A4, S A4, E A4 Sub-model C5 F A5 Sub-model C6 F A6, B A6, S A6, E A6 Sub-model C7 F A5, B A5, S A5, E A5 Sub-model C8 F A6, B A6, S A6, E A6 cases Product A F A Sub-product A1 F A1 Sub-product A2 F A2 Component A3 F A3, B A3, S A3, E A3 Component A4 F A4, B A4, S A4, E A4 Component A5 F A5, B A5, S A5, E A5 Component A6 F A6, B A6, S A6, E A6 Product B F B Sub-product B1 F B1, B B1, S B1, E B1 Sub-product B2 F B2 Component B3 F B3, B B3, S B3, E B3 Component B4 F B4, B B4, S B4, E B4 A3 is instance of C3 A4 is instance of C4 B1 is instance of C6 B4 is instance of C8 Old case A Old case B Case memory Model C is the generalized functional hierarchy for specific product family Concepts for LCA at arbitrary levels of detail

30 Model C F C Sub-model C1 F A1 Sub-model C2 F A2 Sub-model C3 F A3, B A3, S A3, E A3 Sub-model C4 F A4, B A4, S A4, E A4 Sub-model C5 F A5 Sub-model C6 F A6, B A6, S A6, E A6 Sub-model C7 F A5, B A5, S A5, E A5 Sub-model C8 F A6, B A6, S A6, E A6 cases Product N F N Sub-product N1 F N1 Sub-product N2 F N2 Component C3 F N3, B N3, S N3, E N3 Component C4 F N4, B N4, S N4, E N4 Sub-product N5 F N5 Component c6 F N6, B N6, S N6, E N6 Component C7 F N5, B N5, S N5, E N5 Component C8 F N6, B N6, S N6, E N6 If the function of product N is not fully decomposed, we cannot estimate the E of N1, N2 and N5. However, if the product N is subset of Model C, C3, C4, C6, C7 and C8 will be anticipated lower function. After confirm the sub functions and associated behavior, structure, finally we can estimate environmental effect with LCA via CBR process Concepts for LCA at arbitrary levels of detail

5. Concluding remarks 31

32 Concluding remarks  1. Introduction  2. LCA via CBR  3. Case study  4. Concepts for LCA at arbitrary levels of detail 32

Appendix 33

34 * Functional basis reconciled function set 34 CorrespondentsTertiarySecondaryPrimary SecondaryTertiaryCorrespondents * Reference: NIST Technical Note 1447 “A Functional Basis for Engineering Design: Reconciling and Evolving Previous Efforts”

35 *k-medoids clustering algorithm The k-medoids algorithm is a clustering algorithm related to the k-means algorithm and the medoid shift algorithm. In contrast to the k-means algorithm, k-medoids chooses data points as centers (medoids or exemplars). 1. Arbitrary select k of the n data points as the medoids 2. Associate each data point to the closest medoid, and calculate total cost of each cluster 3. Swapping medoid and random case, and calculate total cost 4. Finalized cluster set In our research, similarity measurement can be defined as the sum of functional distance, behavioral distance and structural distance. However each indexing layer have different data type, and some of them have nonnumeric value. Therefore, k-medoids clustering algorithm is appropriate to us than k-means.