Multi-criteria decision-making for the life cycle of sustainable high pressure die casting products

被引：0

作者：

Pagone E. ^{[1
]}

Papanikolaou M. ^{[1
]}

Salonitis K. ^{[1
]}

Jolly M. ^{[1
]}

机构：

[1] Sustainable Manufacturing Systems Centre, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedfordshire

来源：

International Journal of Sustainable Manufacturing | 2020年 / 4卷 / 2-4期

基金：

英国工程与自然科学研究理事会;

关键词：

Automotive products; Foundries; High pressure die casting; HPDC; Key performance indicators; KPIs; Lifecycle analysis; Manufacturing systems; Material selection; MCDM; Multi-criteria decision making; Sustainability metrics; Sustainable development;

D O I：

10.1504/IJSM.2020.107140

中图分类号：

学科分类号：

摘要：

In the scientific literature there is a scarcity of comprehensive and organic studies on performance indicators encompassing sustainability and their influence on decision-making. This work aims at selecting the most suitable material to manufacture an automotive component using a high pressure die casting (HPDC) process according to four classes of metrics: cost, time, quality and sustainability. The performance of three different alloys (aluminium-A380, magnesium-AZ91D and zinc-ZA8) was evaluated considering overall product life cycle aspects and process characteristics through a deterministic technique for order of preference by similarity to ideal solution (TOPSIS). Results show that the zinc alloy should be chosen on a unit mass-basis mainly thanks to its significantly superior quality and sustainability performance. This study demonstrates that the inclusion of the sustainability dimension in a multi-criteria decision analysis context challenges well-established material selection trends in the automotive industry developed during the past decades. © 2020 Inderscience Enterprises Ltd.

引用

页码：101 / 120

页数：19

共 45 条

[1]

Badurdeen F., Shuaib M.A., Lu T., Jawahir I.S., Sustainable value creation in manufacturing at product and process levels: A metrics-based evaluation, Handbook of Manufacturing Engineering and Technology, pp. 1-28, (2013)

[2]

Bastien P., Armbruster J.C., Azov P., Flowability and viscosity, AFS Transactions, 70, pp. 400-409, (1962)

[3]

Bhushan N., Rai K., Strategic Decision Making: Applying the Analytic Hierarchy Process, (2007)

[4]

Bidanda B., Kadidal M., Billo R.E., Development of an intelligent castability and cost estimation system, International Journal of Production Research, 36, 2, pp. 547-568, (1998)

[5]

Black J.T., Kohser R.A., DeGarmo E.P., DeGarmo's Materials and Processes in Manufacturing, (2013)

[6]

Campbell J., Castings, (2003)

[7]

Casarotto F., Franke A.J., Franke R., High-pressure die-cast (HPDC) aluminium alloys for automotive applications, Advanced Materials in Automotive Engineering, pp. 109-149, (2012)

[8]

Cavalieri S., Maccarrone P., Pinto R., Parametric vs. Neural network models for the estimation of production costs: A case study in the automotive industry, International Journal of Production Economics, 91, 2, pp. 165-177, (2004)

[9]

Chan F.T.S., Jiang B., Tang N.K.H., The development of intelligent decision support tools to aid the design of flexible manufacturing systems, International Journal of Production Economics, 65, 1, pp. 73-84, (2000)

[10]

Chougule R.G., Ravi B., Casting cost estimation in an integrated product and process design environment, International Journal of Computer Integrated Manufacturing, 19, 7, pp. 676-688, (2006)

← 1 2 3 4 5 →