IN-SITU CALIBRATED DIGITAL PROCESS TWIN MODELS FOR RESOURCE EFFICIENT MANUFACTURING

The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated for the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of insitu process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing

The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated for the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

A Sheep Model for the Osseointegration of PEO-treated Gamma Titanium Aluminide

A sheep model was used to study the osseointegration of gamma titanium aluminide (γTiAl) screws subjected to plasma electrolytic oxidation (PEO). The degree of osseointegration was determined by measuring the maximum torque for screw removal from bone after 3 and 6 months of implant placement in sheep for PEO-treated γTiAl, untreated γTiAl, and untreated Ti6Al4V cortical screws. The amount of bone growth and mineralization were qualitatively observed by von Kossa staining of the bone tissue in the region surrounding the implants. Inductive Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) was carried out to determine trace amounts of metallic elements in blood serum samples obtained from the animals. Generally Al, Cr and V were present in blood serum in comparable quantities in the control and implanted animals, while neither Ti nor Nb was detected. Results from histological analysis and SEM images indicated that de novo bone growth occurred to a greater extent for the PEO treated γTiAl screws. Furthermore, the torque for screw removal from bone was significantly higher (p<0.05) for the PEO-treated γTiAl implants. Taken together, the data supports that the PEO surface treatment enhanced osseointegration to a considerable degree indicating the potential for favorably utilizing PEO-treated γTiAl for dental and orthopedic implant applications.

Advancement of Plasma Cold-Hearth Melting for Production of Gamma Titanium Aluminide Alloys within Arconic

Utilization of gamma titanium aluminide alloys in aerospace and automotive/industrial applications has placed significant demand on melting sources for products to be used in cast, wrought, and direct-machining applications. There is also an increased demand for input stock used in gas atomization of powders. Current technologies used in ingot manufacturing include plasma arc melting, vacuum arc melting, and induction skull melting + centrifugal casting. Subsequent processing may include forging, re-melting + casting, or machining directly into components. Over the past six years, Arconic Engineered Structures has developed a robust melting method using plasma cold-hearth melting technology, including the design and implementation of a new 3-torch system to produce Ti-48-2-2 cast bars. General discussions concerning plasma cold-hearth melting, manufacturing challenges, and metallurgical attributes associated with cast Ti-48-2-2 bars will be reviewed. Emphasis will be on understanding the impact of hot isostatic pressing on internal voids, residual stress cracking and resulting mechanical properties.

The effect of notch size on fatigue crack threshold and propagation in cast gamma titanium aluminide

Cracks were initiated and grown from notched specimens with notch lengths of 0.03 (FIB), 0.06, 0.1 and 0.5 mm (diamond blade), in cast gamma titanium aluminide at 400 oC. Specimens with longer 0.1 and 0.5 mm notches initiated from the notch and show no effect from initial notch size on either crack growth threshold or propagation. Specimens with shorter 0.03 and 0.06 mm notches initiated naturally, cracking from microstructural features away from the notch. The fracture surfaces of these specimens were characterised to distinguish areas of initiation, fatigue and overload. Multiple initiation sites were observed on these fracture surfaces. It is inferred that, for this alloy and process route, microstructural initiation is dominant where notches are less than ~100 µm in size.