This study aimed to establish a complete additive manufacturing (AM) enabled investment casting process using ferrous scraps from forward operating bases (FOBs). Ferrous waste materials were first characterized, and a waste material database was created to store the critical information of scraps for sorting and recycling. A blending model was created to help select scraps for remelting and fabricating target steel alloys in the fields. In the investment casting process, stereolithography (SLA) printed patterns were used to replace the traditional wax patterns, which significantly increased the versatility and flexibility of the manufacturing process. There were two printers (3D system 6000HD and Formlabs Form 2) were studied and compared, both printers were proven feasible in the established procedure. Zirconium silicate-based based primary slurry and Zircon sand were used in ceramic shell making process. A dipping-stucco process was established to fabricate ceramic shells, and a burnout process was designed and optimized for SLA pattern removal. MagmaSoft was utilized to assist the casting tree design. To satisfy the need of the FOBs, a three-container-based mobile foundry was designed which enabled rapid manufacturing of urgently needed parts in the fields. Casting facilities and inspection equipment were configured into three standard-size shipping containers. Thermal simulations were conducted in ANSYS to ensure a safe operating environment. Three demonstrative components were successfully made using a similar compact casting system at WPI foundry. Data-driven tools have been established and studied for post-treatment after casting. An artificial neural network (ANN) model was created and applied to study the relationship between a wide range of steel chemical compositions and multiple mechanical properties. The model used alloying element concentrations in steel and basic heat treatment information as inputs. The model outputs included mechanical properties, including yield strength (YS), ultimate tensile strength (UTS), Brinell hardness number (BHN), and elongation (EL).

Creator

• Wang, Yutao

Contributors

• Zhang, Zhaolong
• Mishra, Brajendra
• Sisson, Richard D.
• Liang, Jianyu

Degree

• PhD

Unit

• Manufacturing Engineering

Publisher

• Worcester Polytechnic Institute

Identifier

• etd-78296

Advisor

• Liang, Jianyu

Committee

• Mishra, Brajendra
• Sisson, Richard D., Jr.
• Zhang, Zhaolong

Defense date

• 2022-09-20

Year

• 2022

Date created

• 2022-09-30

Resource type

• Dissertation

Source

• etd-78296

Rights statement

• In Copyright

Last modified

• 2023-11-06