Introduction

Preliminary engineering design properties that reflect the capability of cast metals are not readily available in a convenient searchable form from a recognized source. In most cast alloys, properties are highly impacted by the chemistry and local cooling rate, which is mainly controlled by the section thickness and the type of mold media or casting process used. Along the same lines, qualified data is vital for design engineers to effectively design more sophisticated parts and components. The need for lightweighting often results in more highly stressed parts due to thinner section thicknesses. Castings are becoming even more complex with the advent of 3D sand printing and its capabilities to cast lower density parts that need to be validated by simulation using accurate data.

When this project started, handbooks contained material properties which are generally inadequate, in a printed format vs. electronic, and/or generated with dated test methods. Moreover, the available handbook data nearly unanimously lacked the requisite pedigree information such as process, chemistry, mold/core media, section thickness, etc. The data also often lacked strain life fatigue data, which is becoming critically important in developing and optimizing casting design(s). Due to a continuously decreasing number of metalcasters, these critical design properties are difficult to find or unavailable. The absence of this data often results in substantial delays in the procurement of castings.

Lack of design allowable data leads to the casting alloy and process selection using an ad-hoc casting knock-down factor attributed to anticipated variability over the minimum mechanical properties as specified by the relevant standards.

Accordingly, castings become less competitive in the design engineer’s selection of alloys and processes over wrought counterparts. This is especially true in aerospace and military applications where the net effect of the knock-down factors detrimentally affects the calculations. Design allowable determination is a statistical method that factors the lot-to-lot and heat-to-heat variability in properties data, meeting the minimum required with a reasonable sample universe. 

The MMPDS organization under the direction of the Federal Aviation Administration (FAA) has developed a statistical computation tool to derive design allowable values factoring sample lot and heat variability. 

Per FAA Regulations (FARS, CFR 14) SECTION 23-613, (a) Material strength properties must be based on enough tests of material meeting specifications to establish design values on a statistical basis; and (b) Design values must be chosen to minimize the probability of structural failure due to material variability. Selecting design values that ensure material strength with the following probability: (1) Where applied loads are eventually distributed through a single member within an assembly, the failure of which would result in loss of structural integrity of the component; 99 percent probability with 95 percent confidence. [T99, also known as A-Basis Design Allowable] and (2) For a redundant structure, in which the failure of individual elements would result in applied loads being safely distributed to other load-carrying members; 90 percent probability with 95 percent confidence. [T95, also known as B-Basis Design Allowable].

The use of design allowable values results in more realistic casting design configurations when compared to ad-hoc casting knockdown factors over the minimum called out by American Society for Testing and Materials (ASTM) specifications, Aerospace Material Specification (AMS), and the U.S. Military Standards (MIL-STDs). 

Best practice data from the quality metalcasters for various alloys using separately cast test bars properties represent the realistic variability and statistically derived design allowable values, and using that data is more valuable and meaningful than minimums provided in applicable standards, specifications, and handbooks published data. Hence, a task is dedicated to generating such data in the last year of the project in place of generating strain life fatigue data for just one alloy.

This project was undertaken to address the above-listed need for creating a repository of the pedigreed data generated in government-funded projects like the American Metalcasting Consortium (AMC) Casting Source Readiness (CSR), its successor Innovative Casting Technologies (ICT), and via industrial partnerships. It is an online database search and retrieval tool to benefit design engineers and foundry process engineers alike, that utilize Finite Element Analysis (FEA) based validations for new product development, design optimization, and process simulation validations.

A dedicated, open-access website (http://www.AFSCADS.com) was designed and launched for the CADS V1.0 (Version 1.0) and Mold Material Data Search (MMDS) V1.0 tools in 2016. Most of the data was leveraged from other author-involved research projects. These projects include a funded project to develop a Fatigue Properties Database (circa 2010), a United States Automotive Materials Partnership/United States Consortium of Automotive Research (USAMP/USCAR) funded light metals materials database (circa 2012), and finally, a society-funded research project on strain life fatigue data for Compacted Graphite Iron (CGI) Grade 400 and a High-alloy Class 40 Gray Iron (circa 2014). 

CADS is a readily searchable database of alloy-centric properties. The recent CADS V3.0 (Version 3.0, circa 2021) greatly enhances the user experience and is the first version to offer a copy/paste citation field and direct link to the AFS online digital library where the pedigreed data reports reside. 

Experimental Procedure

Task 1: CADS Upgrade to V3.0

For better access by the metalcasting industry professionals, CADS V3.0 is currently linked through the main AFS website (www.AFSINC.org), under the sub-menu of “Designers and Buyers” as shown in Figure 1. Also, a separate landing page and dedicated website were designed and launched under the weblink at www.AFSCADS.com, to get additional users, and to facilitate searching/identification via common search engines. Figure 2 shows the landing page with a link to CADS V3.0.

The CADS V3.0 user interface was radically improved from the previous version to enhance the use and adoption by foundry process and design engineers. A screenshot of the main CADS home page appears in Figure 3. On this screen, three main navigation options appear on the left sidebar menu: Select Alloy from Grade List, Strength Property Search, and Global Alloy Search. By selecting the first menu option (Select Alloy from Grade List) on the left sidebar menu, a scrolling list of available grades categorized by alloy (e.g., iron, aluminum, magnesium, steel, copper and other alloys) appears. A screenshot of the 356 Aluminum Alloys grade is provided in Figure 4. 

Similarly, by selecting the second menu option (Strength Property Search) from the left sidebar menu, moveable slide bars can be used to select the desired mechanical properties combination for ultimate tensile strength (UTS) in ksi, yield strength (YS) in ksi, and elongation % (E). 

The resulting screenshot after using the slide bars to select mechanical property value of 31 ksi UTS, 11 ksi YS and 3% E is presented in Figure 5. The result is a list of all combinations of alloys, castings processes, and section thickness maximums that will achieve or exceed the combination of targeted mechanical properties. 

By selecting the third menu choice (Global Alloy Search) from the left sidebar menu, the designer can search for all of the property listings of a known alloy grade. The resulting screenshot after inputting 356 into the “Search For” field from the Global Alloy Search page appears in Figure 6. One might wonder how selecting 356 from the Global Alloy Search page differs from selecting 356 alloy from the scrolling alloy grade list (Select Alloy from Grade List). 

The difference is the scrolling alloy grade list may have multiple listings for information for the 12 steel alloys and Table 2 containing the same dataset information for the various non-steel alloys (four aluminum, six iron, and two copper alloys). These tables were generated using datasets provided by AFS member foundries from separately cast test bars data over multiple heats. Each dataset is reviewed and only acceptable lots meeting the minimum required properties are taken into the calculation for design allowable values using the MMPDS methodology.

A typical output from the MMPDS tool showing T99 and T95 values as highlighted in yellow appears in Table 3. This dataset was from the A206-T4 best practice data with 586 datasets/heats. Furthermore, a Pearson Probability Plot for the same alloy appears in Figure 11.

Results And Discussion

A user-friendly, open-access database tool, Casting Alloy Data Search (CADS), was developed as an online casting material database to assist the Department of Defense (DoD), Original Equipment Manufacturers (OEMs), and metalcasters with easy accessibility to critical design properties on a digital format. This database has been designed so that the material properties can be imported into Computer Aided Engineering (CAE) and Finite Element Analysis (FEA) tools along with casting process simulation programs. CADS assists the casting design engineer with the latest datasets for engineering properties including strain life fatigue values determined using the latest test methods available. This database includes an updated tutorial with detailed instructions along with both a help function and a case study for navigation.

The datasets from CADS V2.0 remain in CADS V3.0. 

Tables 4 and 5 show the T99 (equivalent to A-Basis per MMPDS latest method), T95 (equivalent to B-Basis per MMPDS latest method) and S-basis (minimum allowable per the corresponding standard) for various steel and non-steel (iron, aluminum and copper) alloy grades, respectively. In most alloys, the T99 and T95 strength values either exceed or are very close to the minimum required by the applicable standards. Data from 14,542 heats were reviewed and analyzed and the full report with detailed information on methodology, along with the various probability distribution graphs outputs for each alloy grade by the MMPDS tool, appears in Reference #8 (online). 

The CADS V3.0 database continues to grow with the addition of several alloys each year and now has over 400 datasets including various irons grades (e.g., Austempered Ductile Iron (ADI), Solution Strengthened Ferritic Ductile Iron (SSFDI), and High Silicon Molybdenum Iron (HiSiMo)), various grades of common cast steels (e.g., WCB steel, 4330 steel, 8630 steel, CF8M (316) stainless steel, and 420 (CP40) stainless steel), numerous aluminum and magnesium alloys imported from USCAR/USAMP research projects, and two newly-generated copper alloys (C95800 and C89833). Other datasets added include 17-4PH and 15-5PH steel data, 357-T6 (0.5” to 2” thickness) and A206 (T4 and T7) aluminum MMPDS data from the CHAMPS project.

Photomicrographs at 50x, 100x, and/or 500x (both etched and/or un-etched) were incorporated when available into the CADS V3.0 datasets; including several of the iron and aluminum alloys. The photomicrographs can be retrieved under the “microstructure” selection under the alloy listing. For example, Figure 12 is the search result for SSF-500-14 grade of ductile iron and Figure 13 is an example of the SSF-500-14 microstructure at 100x.

Transition to Industry

One of the primary objectives of AFS is the transfer of technology into industry and there are several ways in which this was accomplished at AFS. There are more than 1,000 corporate members and 7000 individual AFS members. This footprint makes reaching a multitude of industry partners and their OEM customers possible. Many industry foundries supplied data and/or in-kind for this project working through the various AFS Technical Committees on Iron, Aluminum & Light Metals (Aluminum, Magnesium), Steel, Copper, and Molding. These technical committees have a vested interest inthis database and provide invaluable support and oversight. This information has also been distributed to multiple industry OEMs via presentations and live demonstrations at the annual AFS Metalcasting Congress to an average attendance of 25 people each over the past 10 years. A dedicated CADS webinar was organized by AFS as well and is available on demand on the AFS website. Finally, CADS has been incorporated into the AFS Institute’s “Casting Design” course and was promoted on the AFS website and in AFS publications.

Conclusion

The CADS tool is continuously being upgraded with more data and more data fields and is maintained on a secure server. The new CADS V3.0 database contains over 400 datasets. The AFS digital library is available to AFS members including active military email accounts using a .mil or .civ email address.