Optimization of Throughput in Sheet Metal Manufacturing by Tuning the Sheet Metal Nesting Strategy Based on Sheet Utilization and Downstream Part Handling Costs

Sheet metal fabrication has become a fundamental process in modern engineering due to its versatility and is used across a wide range of industries. Nesting a given set of sheet metal blanks onto raw material sheets is a major cost driver as it determines the amount of usable metal and the rest of the sheet is thrown away as scrap. Nesting algorithms are very effective at identifying the most efficient layout of a given set of parts to maximize the sheet utilization. Hence, material utilization of the sheet is mainly defined by the number of parts being nested and their geometries. On one hand, nesting algorithms would prefer having a large number of grouped parts that allow them to make more efficient sheet metal nests due to more possible combinations of parts on a given sheet. On the other hand, the downstream sorting process which sends the parts to their respective further processing stations would prefer having fewer number of grouped parts as the parts get nested randomly which increases the time spent on the non value add activity. Therefore, an effective nesting strategy between the two extremes is necessary to balance the sheet utilization with the intensive sorting requirements to make the process cost effective and meet the required throughput. In this thesis, a sheet metal nesting strategy is identified for a manufacturing operation with a wide variety of products and plant locations across the globe. Cost and throughput models are produced which inform the selection of a globally optimized nesting strategy. Regional differences in cost drivers such as varying labor rates and raw material costs are considered, and an optimized nesting strategy is validated for deployment across global plant locations. This work provides a detailed approach to optimizing sheet utilization in sheet metal manufacturing through selection of an optimized nesting strategy.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Gowra, Vineeth

Advisor dc:contributor.advisor

• Hardt, David E.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/152702

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/152702