Advanced Engineering Informatics 20 (2006) 147–154 www.elsevier.com/locate/aei
A knowledge-based approximate life cycle assessment system for evaluating environmental impacts of product design alternatives in a collaborative design environment Ji-Hyung Park a,*, Kwang-Kyu Seo b,1 b
a CAD/CAM Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul, South Korea Department of Industrial Information and Systems Engineering, Sangmyung University, San 98-20, Anso-Dong, Chonan, Chungnam 330-720, South Korea
Received 4 January 2005; accepted 30 September 2005
Abstract In a competitive and globalized business environment, the need for the green products becomes stronger. To meet these trends, environmental impact assessment besides delivery, cost and quality of products should be considered as an important factor in new product development stage. In this paper, a knowledge-based approximate life cycle assessment system (KALCAS) is developed to assess the environmental impacts of product design alternatives. It aims at improving the environmental efficiency of a product using artificial neural networks, which consist of high-level product attributes and LCA results. The overall framework of a collaborative design environment involving KALCAS is proposed, using engineering solution COe based on the distributed object-based modeling and evaluation (DOME) system. This framework allows users to access the product data and other related information on a wide variety of application. This paper explores an approximate LCA of product design alternatives represented by solid models in a collaborative design environment. q 2006 Elsevier Ltd. All rights reserved. Keywords: Artificial neural networks; Design alternatives; Collaborative design environment
1. Introduction It becomes increasingly difficult for companies to develop successful new product due to intensive global competition. Under such a competitive environment, new product development cannot rely on traditional criteria such as cost, quality and delivery. As a result, more and more companies begin to realize the importance of the need of green product development. Effective green product design is able to assist a company to gain a prominent competitive edge over its competitors. With the shift in focus, leading companies begin to environmental considerations for product development, and seek new methods to satisfy environmental needs. Life cycle assessment (LCA) is the most sophisticated tool to consider and quantify the consumption of resources and the environmental impacts associated with a product or process [1]. * Corresponding author. Tel.: C82 2 958 5631; fax: C82 2 958 5639. E-mail addresses:
[email protected] (J.-H. Park),
[email protected] (K.-K. Seo). 1 Tel.: C82 41 550 5371; fax: C82 41 550 5185.
1474-0346/$ - see front matter q 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.aei.2005.09.003
By considering the entire life cycle and the associated environmental burdens, LCA identifies opportunities to improve environmental performance. A detailed LCA is an extremely useful method, but it may be rather costly, time-consuming and sometimes difficult to communicate with non-environmental experts. Further, the use of LCA poses some barriers at the conceptual stage of product development, where ideas are diverse and numerous, details are very scarce, and the environmental data for the assessment is short. This is unfortunate because the early phases of the design process are widely believed to be the most influential in defining the LCA of products. The objective of this work is to develop a knowledgebased approximate life cycle assessment system (KALCAS) for product concept development. KALCAS aims at improving design efficiency by managing high-level product information so-called product attributes. To expedite the realization of KALCAS, the product attributes and environmental impact drivers (EIDs) are identified. The products are grouped by environmental characteristics and then the product attributes strongly correlated with EIDs are chosen as learning data in KALCAS. Moreover, an object-oriented approach to retrieve the information stored in an ODBC has been adopted.
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In this paper, the overall framework of a collaborative design environment involving KALCAS is also proposed. This architecture uses the COe environment to allow users on a wide variety of platforms to access the product data and other related information. It enables us to trade-off the evaluation results between the objectives of design alternatives including the approximate environmental impact assessment in collaborative design environment. This paper is organized as follows. Section 2 describes the overview of an approximate life cycle assessment. In Section 3, KALCAS based on artificial neural networks is explained. Section 4 presents the framework for integrating KALCAS into a collaborative design environment. A refrigerator is shown as an example to integrate KALCAS and CAD model into a collaborative design environment and to assess environmental impact of design alternatives. Finally, some concluding remarks are provided in Section 5. 2. Overview of an approximate life cycle assessment An approximate life cycle assessment proposed by Park et al. [2] is the simplified environmental impact assessment methodology by classifying products into groups according to their environmental and product characteristics. The data mining techniques were applied to classify products into groups. The goal was to develop a product grouping method that supports an appropriately specialized LCA model. The method may learn faster and more effectively if the learning space is narrowed into general but coherent product categories. The categorization should be based on properties that potentially create common dominant environmental impacts so that the proposed method is better able to assess impacts of specific products within the group. Product attributes and environmental characteristics were used to develop a product grouping method based on decision tree algorithm as data mining techniques. After applying decision tree algorithms, products were finally classified into some groups and the results of the grouping analysis guided the definition of environmental characteristics of products. The proposed methodology provides useful LCA results of products in terms of product attributes related with Detailed product description data
High-level product attributes
environmental characteristics and corresponding to a detailed LCA [3,4]. The EIDs are defined by the environmental characteristics of classified products in groups [1,5] and identified through analyzing the correlation between EIDs and product attributes. EIDs are derived from impact categories and captured the environmental performance of product designs and statistically tested for its ability to predict impact categories in product groups. They could possibly be used to predict impact categories such as life cycle energy consumption, greenhouse effect, ozone layer depletion, acidification, eutrophication, winter smog, and summer smog levels derived from impact categories in eco-indicator 95. We focus only on estimating life cycle energy (EIDenergy) category as an example in this paper. The product attributes need to be both logically and statistically linked to EIDs, and also be readily available during product concept design. They must be sufficient to discriminate between different designs. Finally, they must be easily understood by designers and span the scope of the product life cycle. These criteria were used to guide the process of systematically developing a product attributes. With these goals in mind, a set of candidate product attributes was introduced in product groups. The approach for the assessment of environmental impacts is suggested, using artificial neural networks (ANNs). The statistical analysis is used to check the correlation between product attributes and EIDs. ANNs are trained on product attributes typically known in the conceptual phase and the LCA data from pre-existing detailed LCA studies. Fig. 1 shows the detailed procedure of an approximate LCA of products. KALCAS developed in this work is based on the approximate LCA methodology. 3. Knowledge-based approximate life cycle assessment system (KALCAS) 3.1. KALCAS in general KALCAS consists of four modules, namely a product information module, a product LCA module, a database Input : New product attributes
In product groups Training data
Product attributes
Input : Product Attributes
Identifying EIDs & product attributes
Existing LCA Study
Classification criteria
Environmental Impact Drivers (EIDS)
Output : EIDs
ANN
Output: Predicting LCA of the new product design
Fig. 1. Procedure of an approximate LCA of products.
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Product Information Module Product information High-Level Product Product Attributes
attributes
Product Attribute DB
DBMS Module
Product LCA Module Product LCA
Product LCA DB
Results Product of Product LCA
attributes Product Grouping according to Environmental Characteristic
Product Knowledge-based Approximate LCA attributes Best Design Concept Knowledge-based Approximate LCA Module
Fig. 2. Architecture of KALCAS.
management system (DBMS) module and a knowledge-based approximate LCA module as shown in Fig. 2. KALCAS has the following characteristics: – It is a methodology for effective product information elicitation and representation. It can be used to ensure better designer’s decision-making in product concept development stage. – It can use high-level product information called product attributes. It enables to facilitate easily product concept modeling, and results in cost reduction, time saving and avoidance of redundant information. – It embodies a design support decision-making engine to decide the level of environmental consciousness of the design alternatives. Using KALCAS, product information is first elicited from diverse resources such as domain experts and existing products. The obtained product information is then used to establish the high-level product attributes. Upon completion, interrelationship mappings between product attributes and product concepts are identified. The results of environmental impact assessment are obtained from pre-exist LCA. The results of environmental impacts assessment are converted to EIDs derived from impact categories. The product attributes and EIDs of products are built through ODBC. Finally, approximate LCA of products are estimated using artificial neural networks. This enables designers to determine product concepts easily. In KALCAS, the product information can be represented as various objects from the viewpoint of an object-oriented database management system (ODBMS) for the minimization
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of redundancy, the implementation of query facilities, the management of user and security, and the communication and co-ordination support. Thus, object-oriented modeling plays an active role in enhancing modeling capability, providing richer expression for concepts, and incorporating semantics about engineering data. In this work, an object-oriented model is adapted to store and manage the basic product information such as high-level design specifications and to provide the facilities for tasks such as data consistency and integrity control, and query management and data redundancy control. 3.2. Development of KALCAS based on artificial neural networks Fig. 3 shows the main interface of KALCAS, which is developed using artificial neural networks with backpropagation algorithm [6]. We can choose the structure such as the number of input/output nodes and hidden layers of the backpropagation algorithm in main interface of KALCAS. In addition, the parameters to train the backpropagation algorithm are determined. The training data such as product attributes and LCA results derived from ODBC are then used to determine the learning weights of artificial neural networks (ANNs). After determining learning weights to minimize learning error, the new product attributes are defined to evaluate the environmental impacts of new product designs as shown in Fig. 4. Using the dialog box in Fig. 4, the designer can evaluate the environmental impacts of new product designs by defining product attributes easily and systematically. After selecting a product attribute in Fig. 4, the authors can change the product attribute for the new product design as shown in Fig. 5. The dialog box for changing the product attribute can help to assign the value of product attribute between maximum and minimum values. 3.3. Experiments Firstly, approximate LCA for the grouping members were performed by using KALCAS. The five identified products attributes such as lifetime, use time, mode of operation, in use energy source, and in use power consumption for life cycle energy category in product group were used to inputs and EIDenergy was used to output of KALCAS. Training data with product attributes and corresponding life cycle energy were gathered for 40 products in the group. The predicted results of group members with 30 training data and 10 test data are shown in Table 1. The approximate LCA results using KALCAS with the identified EIDs and product attributes gives good results except for heater. It is shown that a grouping of products is possible and reasonable for the use of LCA of the group members. The identified EIDs and product attributes can be used to predict the product’s environmental impact of group elements. In order to assess the environmental impacts of group elements as well as others, all defined product attributes were used as inputs for EIDenergy in KALCAS. Especially,
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Fig. 3. Main interface of KALCAS.
Fig. 4. Dialog box for estimating the environmental impacts of a new product design.
the product attributes used in this estimation were checked by correlation test between EIDenergy and all product attributes defined as the variables of the other EIDs. Sampling data with product attributes and corresponding life cycle energy were collected for 150 different products. Finally, 21 product attributes as shown in Table 2 were identified and used to estimate EIDenergy in KALCAS. More than 20 experiments were performed to determine the best combination of the learning rates (a), momentum (h), number of hidden layers, number of neurons in hidden layers, learning rules and transfer functions. The resulting network has a hidden layer with 16 neurons. The most popular learning rules, generalized delta rules and a sigmoid transfer function were used for the output node. The predicted results of LCA
with 140 training data and 10 test data are shown in Table 3. The absolute errors of predicted LCA are ranged from 0.1 to 12% of the levels given by the actual LCA, so the results obtained by KALCAS seem to be satisfactory.
Fig. 5. Dialog box for data change of the new product design.
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Table 1 The predicted LCA results of group members using KALCAS Product*,**
Actual LCA
The results of predicted LCA (one hidden layer with 10 neurons)
Relative error (%)
The results of predicted LCA (one hidden layer with 15 neurons)
Relative error (%)
Vacuum cleaner Mini-vacuum cleaner Radio Heater Coffeemaker Washing machine Refrigerator (small) Refrigerator (large) IV LCD IV Average absolute error Maximum absolute error
5110 176 207 24,800 3980 54,500 2686.19 18,777.79 2,432,037 24,813.73
3910.68 130.70 182.68 35,498.72 4604.86 54,036.75 2431.54 20,165.47 2,432,523 25,324.89
23.47 25.74 11.75 K43.14 K15.7 0.85 9.48 K7.39 K0.02 K2.06 13.96 43.14
3846.30 126.30 185.43 36,014.56 3995.12 53,786.05 2475.06 18,496.12 2,365,399 24,625.15
24.73 28.24 10.42 K45.22 K0.38 1.31 7.86 1.5 2.74 0.76 12.32 45.22
*Training sample size is 30.** Test sample size is 10.
4. Framework for integrating the KALCAS into the collaborative design environment In this section, the framework for integrating KALCAS and Solidworks into a collaborative design environment is described. 4.1. The collaborative modeling The framework for the collaborative design environment is implemented by engineering solution COe [7] based on the distributed object-based modeling and evaluation (DOME) system [8–10], developed at MIT’s Computer Aided Design Laboratory. In this environment provided by COe, the designer can easily build object-oriented models visualized as entity relationship graphs. As an example for the collaborative design, a collaborative design model for a refrigerator is developed. The model is broken down into four areas, distributed across several computers and integrated as shown in Fig. 6. Cost, efficiency and environmental impacts of the product are of interest to the designer, so specifications are placed upon each one: efficiency is to be maximized, functional requirements must be satisfied, while cost and environmental burdens are to be minimized. Efficiency can be defined as the ratio of a machine’s output of energy to its input. For example, efficiency of a refrigerator can be measured by the performance of compressors or condensers which are the major components. COe is used to model the parts of the system in the product designer’s domain. COe allows the designer to explore the numerous possible design configurations without knowing the details of different domains such as environmental impact assessment, efficiency and cost analysis, or geometric modeling. In this paper, the authors focus on the procedure to integrate solid models into a collaborative design environment and to evaluate environmental impacts of different type of solid models using KALCAS in collaborative design environment. The collaborative framework described in this paper allows product designers to assess the environmental impacts of their
design alternatives easily available to KALCAS over the Internet. Once connected, these models rapidly provide impact assessments to product designers based on a given set of inputs such as product attributes. This assessment can be used in evaluating the attributes of a conceptual design and be incorporated into an environmentally conscious design. The basic workflow is done according to solid models of a refrigerator using Solidworks. The environmental impact is assessed using KALCAS to predict the relative environmental impacts associated with product design alternatives represented by solid models. The designer can easily predict the relative environmental impacts as defining the product attributes of new product design alternatives as shown in Fig. 7. The detailed procedure of building the framework for collaborative design environment is described as follows. The refrigerator designer working on the overall model is interested in producing different models of a refrigerator with various volume and material. The designer is interested in Table 2 The final list of identified product attributes Product attributes
Unit
Level of information
Mass Ceramics Fibers Ferrous metals Non-ferrous metals Plastics Paper/cardboard Chemicals Wood Other materials Assemblability Process Lifetime Use time Mode of operation Additional consumable Energy source Power consumption Modularity Serviceability Disassemblability
kg % mass % mass % mass % mass % mass % mass % mass % mass % mass dimensionless dimensionless hours hours dimensionless dimensionless dimensionless watt dimensionless dimensionless dimensionless
Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, binary Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, specified Quantitative, binary Quantitative, specified Quantitative, specified Quantitative, binary Quantitative, binary Quantitative, binary
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Table 3 The predicted LCA results with 150 sample data and 21 identified product attributes using KALCAS Product*,**
Actual LCA
Predicted LCA
Relative error (%)
Vacuum cleaner Mini-vacuum cleaner Radio Heater Coffeemaker Washing machine Refrigerator (small) Refrigerator (large) TV LCD TV Average absolute error Maximum absolute error
5110 176 207 24,800 3980 54,500 2686.19 18,777.79 24,320.37 24,813.73
4686.84 122.21 164.53 39,471.19 5097.76 49,682.29 3002.98 20,507.22 26,807.69 24,430.42
4.23 5.72 3.75 K12.02 K5.22 K0.11 2.27 1.23 K2.92 0.5 3.79 12.02
*Training sample size is 140.** Test sample size is 10.
environmental performance, so environmental burden is to be minimized. COe is used to model the parts of the system in the product designer’s domain. However, this designer is not an expert in environmental impact assessment, or geometric modeling, so these modeling activities are delegated to experts in those fields who each use their program of choice. The CAD designer builds solid models of different types in Solidworks and provides calculations for material and volumes. The environmental impacts are assessed using KALCAS to predict the relative environmental impact associated with design options. Solidworks and KALCAS can be controlled by external programs. Distributed modeling capabilities of COe are combined with those of the other modeling programs using Common Object Request Broker Architecture (CORBA) as a distributed communication protocol [11]. This distributed
modeling paradigm is very different from centralized data repositories where users conform to a single standard and are required to check in on a regular basis to make sure their models are updated. For example, a geometry change in this model will affect KALCAS, which will in turn affect the product designer’s assessment of the design quality. A change in any part of the system model will propagate through the system, so that the distributed models together form a concurrent system model. This combination of programs allows the product designer to explore the numerous possible design configurations (different refrigerator types and materials, refrigerator dimensions, etc.) without knowing the details of all of the individual models. Fig. 8 shows the final results of environmental impact assessment for various solid models in collaborative design environment. 4.2. Discussion In this paper, the authors focus on the procedure to integrate solid models and KALCAS into a collaborative design environment. As mentioned before, the collaborative modeling is broken down into four areas as shown in Fig. 6. The product designer is interested in cost, efficiency of a product besides environmental performance, so the evaluation models for cost and efficiency have to be considered and integrated into collaborative design environment. COe described in this paper allows the designer to explore the numerous possible design configurations including efficiency and cost analysis, so the authors discuss how to integrate and collaborate with the cost and efficiency models to determine the optimal design alternatives in collaborative environment.
Fig. 6. The framework for collaborative design environment.
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Fig. 7. Integrating KALCAS and solid model into collaborative design environment.
Efficiency analysis is to be done by simulation tool for performance test. Cost analysis will be used to an Excel model that calculates the total production cost based on production time, tool type, material and labor cost, etc. derived from database of ERP system. The designer can easily integrate these applications using CCC API in COe environment and these models are collaborated in order to find the optimal product design for the given application. The product designer as COe users may set goals or requirements and are provided to evaluate all models so that the
Fig. 8. Final results of environmental impact assessment for design alternatives in collaborative design environment.
optimal design alternatives can be calculated. The combination capability of programs in COe allows the designer to explore the numerous possible design alternatives. Trade-offs can be made among the efficiency, cost, and environmental performance of the product. Also, an optimization function can be used to find the most appropriate design solutions. 5. Conclusions All product introduced into the market has some environmental impacts during product life cycle. Recently, companies examine consumer products and attempt to make green products to minimize their environmental impacts. Green product design necessitates the comparison of impacts associated with design alternatives. It is particularly important to estimate environmental impact as early in the design process as possible. Therefore, it is important that product designers have access to relative environmental information so that they can make appropriate decisions and trade-offs with other design requirements. This work has studied KALCAS for the collaborative design environment. KALCAS developed here is used to assess the environmental impacts in context of product concept development. It is also used to improve the environmental efficiency of the product using KALCAS based on artificial neural networks with high-level product attributes and LCA results. The framework of the collaborative environment incorporating KALCAS was also presented. The architecture for the collaborative environment was established by COe. The framework described in this paper suggested collaboration between the product designer and experts of various domains, facilitated by modeling capabilities distributed over the Internet. The information exchange of different domains can be feasible and valuable to the decision-making of design
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alternatives. The framework illustrated enables the designer to trade-off the evaluation results between the other objectives of design alternatives including environmental impacts in collaborative design environment. References [1] Curran MA. Environmental life-cycle assessment. New York: McGrawHill; 1996. [2] Park J-H, Seo K-K, Wallace D. Approximate life cycle assessment of classified products using artificial neural network and statistical analysis in conceptual product design. Proceedings of the second international symposium on environmentally conscious design and inverse manufacturing; 2001. p. 321–6. [3] Hartmut K, Virginia S. An approach to simplified environmental assessment by classification of products. Proceeding of the seventh CIRP international seminar on life cycle engineering; 2000. p. 163–9.
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