Abstract
Sustainability in manufacturing can be evaluated at product, process and system levels. The 6R methodology for sustainability enhancement in manufacturing processes includes: reduced use of materials, energy, water and other resources; reusing of products/components; recovery and recycling of materials/components; remanufacturing of products; and redesigning of products to utilize recovered materials/resources. Although manufacturing processes can be evaluated by their productivity, quality and cost, process sustainability assessment makes it a complete evaluation. This paper presents a 6R-based evaluation method for sustainable manufacturing in terms of specific metrics within six major metrics clusters: environmental impact, energy consumption, waste management, cost, resource utilization and society/personnel health/operational safety. Manufacturing processes such as casting, welding, turning, milling, drilling, grinding, etc., can be evaluated using this methodology. A case study for machining processes is presented as an example based on the proposed metrics.
| Original language | English |
|---|---|
| Pages (from-to) | 546-553 |
| Number of pages | 8 |
| Journal | 48th SME North American Manufacturing Research Conference, NAMRC 48 |
| Volume | 33 |
| DOIs | |
| State | Published - 2019 |
| Event | 16th Global Conference on Sustainable Manufacturing, GCSM 2018 - Lexington, United States Duration: Oct 2 2018 → Oct 4 2018 |
Bibliographical note
Publisher Copyright:© 2019 The Authors. Published by Elsevier B.V.
Funding
The authors gratefully acknowledge the financial support Knowledge Foundation and GKN Aerospace Engine Systems.
| Funders |
|---|
| Stiftelsen för Kunskaps- och Kompetensutveckling |
| GKN Aerospace Services |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy
-
SDG 9 Industry, Innovation, and Infrastructure
Keywords
- 6R
- Machining process
- Manufacturing
- Sustainability
- Triple Bottom Line
ASJC Scopus subject areas
- Artificial Intelligence
- Industrial and Manufacturing Engineering
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