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A product carbon footprint model for embodiment design based on macro-micro design features

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Abstract

Greenhouse gas emissions have become one of the most prominent global concerns of sustainable development. To reduce product life cycle carbon footprint, planning should begin at embodiment design phase. The accurate assessment of carbon footprint is the foundation of carbon footprint reduction. However, existing carbon footprint models cannot be applied to embodiment design phase due to incomplete and limited design information. With this in mind, this paper proposes a carbon footprint model for embodiment design based on macro-micro design features. First, a Function-Structure-Feature (FSF) model for embodiment design is established to convey the design information. The concept of design features is introduced (at both macro and micro levels). The macro design feature denotes the different operational states of the product and the constraint relationships between parts. The micro design feature denotes the specific properties of parts. Then, a model of product carbon footprint based on design features is presented through the analysis of the relationships between macro-micro design features and product carbon footprint. The feasibility of the proposed method is demonstrated through a gear hobbing machine. The product carbon footprint model allows quantitative evaluation of product carbon footprint during embodiment design phase, and the amount of carbon footprint from each type of design feature is predicted. Based on evaluation result, the design features can be improved to reduce product carbon footprint. Case study results show that the carbon footprint is decreased by 10.96% after improving design features.

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Data availability

The authors thank Chongqing Machine Tool (Group) Co., Ltd help to provide data support. The datasets used or analyzed during this manuscript are available from the corresponding author on reasonable request.

Code availability

Not applicable.

References

  1. Intergovernmental Panel on Climate Change (2014) Climate Change 2014 Mitigation of Climate Change. Cambridge University Press. https://doi.org/10.1017/cbo9781107415416

  2. EIA (2021) 2018 Manufacturing Energy Consumption Survey. https://www.eia.gov/consumption/manufacturing/index.php. Accessed on 21 Feb 2021

  3. He B, Pan Q, Deng Z (2018) Product carbon footprint for product life cycle under uncertainty. J Clean Prod 187:459–472. https://doi.org/10.1016/j.jclepro.2018.03.246

    Article  Google Scholar 

  4. Kengpol A, Boonkanit P (2011) The decision support framework for developing Ecodesign at conceptual phase based upon ISO/TR 14062. Int J Prod Econ 131:4–14. https://doi.org/10.1016/j.ijpe.2010.10.006

    Article  Google Scholar 

  5. Lacasa E, Santolaya JL, Biedermann A (2016) Obtaining sustainable production from the product design analysis. J Clean Prod 139:706–716. https://doi.org/10.1016/j.jclepro.2016.08.078

    Article  Google Scholar 

  6. Yang Q, Yu S, Sekhari A (2011) A modular eco-design method for life cycle engineering based on redesign risk control. Int J Adv Manuf Technol 56:1215–1233. https://doi.org/10.1007/s00170-011-3246-1

    Article  Google Scholar 

  7. Wang L, Shen W, Xie H et al (2002) Collaborative conceptual design - state of the art and future trends. CAD Comput Aided Des 34:981–996. https://doi.org/10.1016/S0010-4485(01)00157-9

    Article  Google Scholar 

  8. Sakao T (2007) A QFD-centred design methodology for environmentally conscious product design. Int J Prod Res 45:4143–4162. https://doi.org/10.1080/00207540701450179

    Article  MATH  Google Scholar 

  9. Bereketli I, Erol Genevois M (2013) An integrated QFDE approach for identifying improvement strategies in sustainable product development. J Clean Prod 54:188–198. https://doi.org/10.1016/j.jclepro.2013.03.053

    Article  Google Scholar 

  10. Vidal R, Salmeron JL, Mena A, Chulvi V (2015) Fuzzy Cognitive Map-based selection of TRIZ (Theory of Inventive Problem Solving) trends for eco-innovation of ceramic industry products. J Clean Prod 107:202–214. https://doi.org/10.1016/j.jclepro.2015.04.131

    Article  Google Scholar 

  11. Vinodh S, Kamala V, Jayakrishna K (2014) Integration of ECQFD, TRIZ, and AHP for innovative and sustainable product development. Appl Math Model 38:2758–2770. https://doi.org/10.1016/j.apm.2013.10.057

    Article  Google Scholar 

  12. Devanathan S, Ramanujan D, Bernstein WZ et al (2010) Integration of sustainability into early design through the function impact matrix. J Mech Des 132:081004. https://doi.org/10.1115/1.4001890

    Article  Google Scholar 

  13. Langeveld L (2011) Product design with embodiment design as a new perspective. In Industrial design: New frontiers. ed. D.A. Coelho, InTech, pp 121–146. https://doi.org/10.5772/20579

  14. Casner D, Houssin R, Renaud J, Knittel D (2017) An optimization-based embodiment design approach for mechatronic product development. Open Autom Control Syst J 9:27–47. https://doi.org/10.2174/1874444301709010027

    Article  Google Scholar 

  15. Xu ZZ, Wang YS, Teng ZR et al (2015) Low-carbon product multi-objective optimization design for meeting requirements of enterprise, user and government. J Clean Prod 103:747–758. https://doi.org/10.1016/j.jclepro.2014.07.067

    Article  Google Scholar 

  16. Wang Q, Tang D, Li S et al (2019) An optimization approach for the coordinated low-carbon design of product family and remanufactured products. Sustain 11(2):460. https://doi.org/10.3390/su11020460

    Article  Google Scholar 

  17. Kuo TC, Chen HM, Liu CY et al (2014) Applying multi-objective planning in low-carbon product design. Int J Precis Eng Manuf 15:241–249. https://doi.org/10.1007/s12541-014-0331-z

    Article  Google Scholar 

  18. Chiang TA, Che ZH (2015) A decision-making methodology for low-carbon electronic product design. Decis Support Syst 71:1–13. https://doi.org/10.1016/j.dss.2015.01.004

    Article  Google Scholar 

  19. Zeng D, Cao H, Jafar S et al (2018) A life cycle ecological sensitivity analysis method for eco-design decision making of machine tool. Procedia CIRP 69:698–703. https://doi.org/10.1016/j.procir.2017.11.093

    Article  Google Scholar 

  20. ISO 14067 (2018) Greenhouse gases—carbon footprint of products—Requirements and guidelines for quantification. International Organization for Standardization, Geneva

  21. Bsi (2008) Guide to PAS 2050 - How to assess the carbon footprint of goods and services. British Standards, London, UK

  22. Draucker L, Kaufman S, ter Kuile R, Meinrenken C (2011) Moving forward on product carbon footprint standards. J Ind Ecol 15(2):169–171

    Article  Google Scholar 

  23. Hornibrook S, May C, Fearne A (2015) Sustainable development and the consumer: exploring the role of carbon labelling in retail supply chains. Bus Strateg Environ 24(4):266–276. https://doi.org/10.1002/bse.1823

    Article  Google Scholar 

  24. Thøgersen J, Nielsen KS (2016) A better carbon footprint label. J Clean Prod 125:86–94. https://doi.org/10.1016/j.jclepro.2016.03.098

    Article  Google Scholar 

  25. Jeswiet J, Kara S (2008) Carbon emissions and CESTM in manufacturing. CIRP Ann Manuf Technol 57:17–20. https://doi.org/10.1016/j.cirp.2008.03.117

    Article  Google Scholar 

  26. Kitzes J, Galli A, Bagliani M et al (2009) A research agenda for improving national Ecological Footprint accounts. Ecol Econ 68:1991–2007. https://doi.org/10.1016/j.ecolecon.2008.06.022.Corresponding

    Article  Google Scholar 

  27. Beatrice M, Kim A, Martini F et al (2015) Energy consumption and GHG emission of the Mediterranean diet : a systemic assessment using a hybrid LCA-IO method. J Clean Prod 103:507–516. https://doi.org/10.1016/j.jclepro.2013.12.082

    Article  Google Scholar 

  28. He B, Deng Z, Huang S, Wang J (2015) Application of unascertained number for the integration of carbon footprint in conceptual design. Proc Inst Mech Eng B J Eng Manuf 229:2088–2092. https://doi.org/10.1177/0954405414539495

    Article  Google Scholar 

  29. Kuo TC (2013) The construction of a collaborative framework in support of low carbon product design. Robot Comput Integr Manuf 29:174–183. https://doi.org/10.1016/j.rcim.2012.12.001

    Article  Google Scholar 

  30. Zhang L (2016) Carbon Emission Analysis for Product Assembly Process. J Mech Eng 52(3):151–160. https://doi.org/10.3901/jme.2016.03.151

    Article  Google Scholar 

  31. He B, Tang W, Wang J et al (2015) Low-carbon conceptual design based on product life cycle assessment. Int J Adv Manuf Technol 81:863–874. https://doi.org/10.1007/s00170-015-7253-5

    Article  Google Scholar 

  32. He B, Wang J, Huang S, Wang Y (2015) Low-carbon product design for product life cycle. J Eng Des 26:321–339. https://doi.org/10.1080/09544828.2015.1053437

    Article  Google Scholar 

  33. Song JS, Lee KM (2010) Development of a low-carbon product design system based on embedded GHG emissions. Resour Conserv Recycl 54:547–556. https://doi.org/10.1016/j.resconrec.2009.10.012

    Article  Google Scholar 

  34. Zhang XF, Zhang SY, Hu ZY et al (2012) Identification of connection units with high GHG emissions for low-carbon product structure design. J Clean Prod 27:118–125. https://doi.org/10.1016/j.jclepro.2012.01.011

    Article  Google Scholar 

  35. Li C, Tang Y, Cui L, Li P (2015) A quantitative approach to analyze carbon emissions of CNC-based machining systems. J Intell Manuf 26(5):911–922. https://doi.org/10.1007/s10845-013-0812-4

    Article  Google Scholar 

  36. Cao H, Li H, Cheng H et al (2012) A carbon ef fi ciency approach for life-cycle carbon emission characteristics of machine tools. J Clean Prod 37:19–28. https://doi.org/10.1016/j.jclepro.2012.06.004

    Article  Google Scholar 

  37. Sanfilippo EM, Borgo S (2016) What are features? An ontology-based review of the literature. CAD Comput Aided Des 80:9–18. https://doi.org/10.1016/j.cad.2016.07.001

    Article  Google Scholar 

  38. Brunetti G, Golob B (2000) Feature-based approach towards an integrated product model including conceptual design information. CAD Comput Aided Des 32:877–887. https://doi.org/10.1016/S0010-4485(00)00076-2

    Article  Google Scholar 

  39. Gaha R, Benamara A, Yannou B (2013) Ecodesigning with CAD features: Analysis and proposals. Adv Mech Eng 2013:1–11. https://doi.org/10.1155/2013/531714

    Article  Google Scholar 

  40. Schneider M, Behr T (2006) Topological relationships between complex spatial objects. ACM Trans Database Syst 31:39–81. https://doi.org/10.1145/1132863.1132865

    Article  Google Scholar 

  41. Egenhofer MJ, Franzosa RD (1991) Point-set topological spatial relations. Int J Geogr Inf Syst 5:161–174. https://doi.org/10.1080/02693799108927841

    Article  Google Scholar 

  42. Hu SJ, Ko J, Weyand L et al (2011) Assembly system design and operations for product variety. CIRP Ann Manuf Technol 60:715–733. https://doi.org/10.1016/j.cirp.2011.05.004

    Article  Google Scholar 

  43. Bahubalendruni MVAR, Biswal BB (2016) A review on assembly sequence generation and its automation. Proc Inst Mech Eng C J Mech Eng Sci 230:824–838. https://doi.org/10.1177/0954406215584633

    Article  Google Scholar 

  44. The International Standards Organisation (2006) ISO 14044:2006:Environmental management—Life cycle assessment—Requirements and guidelines. https://doi.org/10.1007/s11367-011-0297-3

    Book  Google Scholar 

  45. Zhou L, Li J, Li F et al (2016) Energy consumption model and energy ef fi ciency of machine tools : a comprehensive literature review. J Clean Prod 112:3721–3734. https://doi.org/10.1016/j.jclepro.2015.05.093

    Article  Google Scholar 

  46. Kara S, Li W (2011) Unit process energy consumption models for material removal processes. CIRP Ann Manuf Technol 60:37–40. https://doi.org/10.1016/j.cirp.2011.03.018

    Article  Google Scholar 

  47. Ashby M (2012) Materials and the Environment: Eco-informed Material Choice: Second Edition. Elsevier Inc. https://doi.org/10.1016/C2010-0-66554-0

  48. Intergovernmental Panel on Climate Change (2019) Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html. Accessed 21 Feb 2021

  49. GB/T 2589 (2008) General principles for calculation of total production energy consumption. Standardization Administration of China, Beijing, China (in Chinese)

  50. EPA Center for Corporate Climate Leadership (2018) Emission Factors for Greenhouse Gas Inventories. https://www.epa.gov/sites/production/files/2018-03/documents/emission-factors_mar_2018_0.pdf (Accessed on 21 Feb 2021)

  51. He Y, Liu F, Wu T et al (2012) Analysis and estimation of energy consumption for numerical control machining. Proc Inst Mech Eng B J Eng Manuf 226:255–266. https://doi.org/10.1177/0954405411417673

    Article  Google Scholar 

  52. Zhong Q, Tang R, Lv J et al (2016) Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process. Int J Adv Manuf Technol 82:2087–2099. https://doi.org/10.1007/s00170-015-7477-4

    Article  Google Scholar 

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Funding

This research was supported by National Key R&D Project of China (2018YFB2002104), National Natural Science Foundation of China (No. 51675314), and Guizhou University of Finance and Economics Introduced Talents for Scientific Research (2018YJ67).

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Authors and Affiliations

  1. Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, China

    Geng Wang, Fangyi Li & Liming Wang

  2. School of Mechanical Engineering, Environmental and Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Fu Zhao

  3. College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang, 550000, China

    Lirong Zhou

  4. Environmental and Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Aihua Huang & John W. Sutherland

Authors
  1. Geng Wang
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All authors provided critical feedback and made substantial contributions to the research, analysis, and manuscript.

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Correspondence to Fangyi Li.

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Appendix

Appendix

According to the micro-feature of CN7 in Table 11, the carbon footprint of CN7 could be calculated as follow.

  1. 1)

    Carbon footprint in extraction of raw material stage.

$${CF}_e={C}_{est}\left({F}_{mater}\right)=15\times 1.51=22.65\ \left({kgCO}_2e\right)$$
  1. 2)

    Carbon footprint in manufacturing stage.

The machining process and related information are shown in Table 18. For a component, the carbon footprint in assembly process is not taken into account.

$${\displaystyle \begin{array}{c}{CF}_m={C}_{est}\left({F}_{geom}\right)+{C}_{est}\left({F}_{surf}\right)\\ {}=\left(\begin{array}{l}2453\times 15+8592+11.03\times 97.2+2\times 11.03\times 13.5+8.53\\ {}\times 136+6.41\times 99.3+13.02\times 5.02+4\times 10.33\times 15.83\end{array}\right)\div 3600\times 0.99\\ {}=13.55\ \left({kgCO}_2e\right)\end{array}}$$
Table 18 The machining processing information of CN7
Full size table
  1. 3)

    Carbon footprint in transportation stage.

$${CF}_t={C}_{tran}\left({F}_{mater}\right)=\sum \limits_{p=1}^{N_0}{\left[{C}_{tran}\left({F}_{mater}\right)\right]}_p=0.015\times 2200\times 0.188=6.204\ \left({kgCO}_2e\right)$$
  1. 4)

    Carbon footprint in use stage.

The carbon footprint in use stage is calculated at product level; therefore, the carbon footprint of CN7 in use stage is not taken into account.

  1. 5)

    Carbon footprint in recycle and disposal stage.

The failure mode of bearing seat is abrasion; plating process is selected to remanufacture it.

$${CF}_r={C}_{rem}\left({F}_{micro}\right)=6.72-22.65-13.55=-29.48\ \left({kgCO}_2e\right)$$

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Wang, G., Li, F., Zhao, F. et al. A product carbon footprint model for embodiment design based on macro-micro design features. Int J Adv Manuf Technol 116, 3839–3857 (2021). https://doi.org/10.1007/s00170-021-07557-7

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