Metallic components are used in a wide selection of applications by every major industrial sector - aerospace, automotive, construction, energy, mining and processing just to name some. These components must endure in operation under various and sometimes extreme environmental conditions combined with variable workloads. A plethora of different test methods have been developed, but the most important ones are based on a seemingly simple method of uniaxial loading. With these tests the aim is to experimentally investigate the onset and progression of plastic deformation, and subsequently fracture. (University of Cambridge 2018)
The scope of this thesis clarifies the very basics of a limited set of most widely used mechanical testing methods.
5.1 Tensile strength
Tensile testing is used to determine materials fracture and deformation characteristics under uniaxial tension (ASM International 2004, 13). Tensile properties are measured to ensure quality, compare materials and manufacturing processes and to make predictions of material behavior under different forms of loading. The usual primary goal in tensile testing is to determine either the stress needed to cause plastic deformation or the maximum stress the material can withstand. Another property of interest determined with tensile testing is so called ductility which measures the materials ability to deform before fracture. (ASM International 2004, 1.)
Picture 10. Tensile specimen, as manufactured and as machined.
Typical tensile specimens have enlarged shoulders for gripping and a reduced gauge section where the deformation and eventually failure will happen (Picture 10). Testing machines are either hydraulic or electromechanical universal testers capable of testing materials under compression or tension and plotting so called force-elongation and stress-strain curves (Figure 1). The test itself is conducted by placing the specimen in a testing machine and subjecting it to tension. Tensile force and gauge sections elongation are monitored and recorded as each other’s function during the test. (ASM International 2004, 1–3.)
After the acquisition of force-elongation data the dimension-independent stress-strain curve can be calculated with the help of gauge sections initial cross-section and change in length (ASM International 2004, 3).
Figure 1. Force-elongation and stress-strain curve (ASM International 2004, 4).
It must be made clear that the calculations are only valid if the specimens deformation stays uniform (ASM International 2004, 7). Once the engineering stress reaches its maximum value i.e., so called ultimate tensile strength Rm of the material (Figure 2), the specimen starts to neck: The reduction of cross-section localizes to some part of the gauge section where material is due to some inconsistency weaker than elsewhere. At the neck, the stress increases as the cross-section shrinks until the specimen ruptures.
(Rösler etc. 2007, 70–71.)
Figure 2. Tensile strength, yield strength, young’s modulus (Rösler etc. 2007, 70).
The stress-strain curves initial slope represents the materials young’s modulus E, indicating the materials elasticity and ability to resist plastic deformation. As deformation increases during the test, also the plastic deformation (yielding) gradually starts. Exact limit between elastic and plastic deformation is not possible to determine, but in most engineering applications plastic deformation of 0.2% is assumed tolerable. The stress required for exceeding this deformation called yield strength Rp0.2 is determined by drawing a line parallel to the initial slope of the stress-strain curve at a distance of 0.2%
strain. The lines intersection with the stress-strain curve then determines the yield strength. (Rösler etc. 2007, 69–70.)
5.2 Hardness
Material’s resistance to localized plastic deformation is a measure of hardness (Callister 2000, 134). Rapid indication of materials deformation behavior can be obtained via indentation hardness testing. Depending on the test method used, the procedure is conducted by forcing a small sphere, pyramid, or cone into the test specimen surface with a predetermined force causing plastic deformation. The “hardness value” is then derived from the dimensions of the indentation. Hardness testing produces valuable data especially when information about brittle materials in high temperatures is required.
(Smallman & Bishop 1999, 199.)
5.3 Impact toughness
Impact testing techniques were developed to predict materials fracture behavior and characteristics under different temperatures as certain circumstances could cause normally ductile materials to suddenly fracture with very little plastic deformation. Two test types called Charpy and Izod are used to measure the impact energy. In both test types the conditions were chosen to optimize the potential for fracture: low temperature, high strain rate, triaxial stress state. Also, in both test types the specimen is a square cross-section bar with a V-notch machined to the side. After temperature conditioning the specimen is placed into the testing machine where the test stress is applied by a pendulum hammer released from a fixed height, fracturing the specimen at the notch.
The energy absorption is calculated from the difference between the initial pendulum release height and the height to which the pendulum continues its swing after the impact.
(Callister 2000, 204–205.)
The primary goal of these tests is to determine if the tested material has a clear ductile-to-brittle transition when temperature decreases (Figure 3). Multiple specimens are tested in different temperatures and from each test the measured energy absorption is plotted as a function of temperature. The slope of the curve dictates if there is a clear narrow range in temperature where the material transitions from ductile to brittle.
(Callister 2000, 206–208.)
Figure 3. Absorbed energy-temperature curve (ISO 148-1 2006, 19).
5.4 Creep & Stress rupture
In principle, materials should deform elastically if the stress applied stays below materials yield strength. However, over long periods of time and in high temperatures plastic deformation can occur even if the applied stress stays below this limit. This deformation is known as creep. (University of Cambridge 2019) Creep phenomenon brings multiple serious challenges to engineering and design. The primary concern being whether the manufactured components operating in conditions optimal for creep will endure the required lifespan. Deformation of material or even rupture could render components useless prematurely. (Penny & Marriot 1995, 1–2.)
During creep testing the specimen is placed under constant tensile load, a method that is and has been the most important way of producing creep data since the recognition of the problem in the component design for high temperatures. Data from this seemingly simple and artificial test method will help to predict how varying combinations of temperature and stress will affect the material. From the recorded data properties such as strain over time and time to rupture can be extracted (Figure 4). (Penny & Marriot 1995, 2.) For accuracy of the test results, the temperature maintenance and measurement of dimensional changes during the test must be under special attention as the rise of temperature by few tens of degrees can double the creep strain rate in many materials (Smallman & Bishop 1999, 200).
Figure 4. Strain accumulation during standard creep test (Penny & Marriot 1995, 2).
5.5 Fatigue
The term “fatigue” is used to describe a form of failure that occurs when a material is subjected to fluctuating and dynamic stress levels considerably lower than the materials tensile or yield strength (Callister 2000, 209). The fatigue conditions are usually extremely complex in nature. Failures commonly happen in axles where the loading of a pulley or a wheel produces varying stress levels which peak on the skin of the axle.
Flexure stresses in aircraft wings and undercarriages are another example of the complexity and significance of the phenomenon. (Smallman & Bishop 1999, 253.) Fatigue testing involves subjecting the specimen to constant cycles of stress. Multiple different methods have been developed in which the stress is applied either by bending, torsion, tension or compression. Usually, three main properties dictate the characteristics of the testing method: stress range, mean stress and frequency of the cycles. The standard method of fatigue study is to prepare many identical specimens and to subject them to different ranges of stress. (Smallman & Bishop 1999, 200.) At a relatively large stress range, usually two thirds of the tensile strength, a first series of tests is conducted, and the number of stress cycles each specimen endured before failure is recorded. This process is then repeated on following groups of specimens while progressively decreasing the stress range. The recorded data is then usually plotted as a so-called S-N curve (Figure 5), where the stress level is represented as the function of cycles to failure on logarithmic scale. (Callister 2000, 209.)
Figure 5. S-N curve (ISO 1099 2017, 15).
6 IMPLEMENTATION
Data modeling process began by getting to know the systems currently in development, test types performed and reading through a multitude of test reports to get an idea what the collected data looks like. A huge source of test specific information in conjunction with colleague’s know-how turned out to be the test standards according to which the tests are conducted. Initial idea was to construct the data models only as a Python source code which would be in line with the technical specifications of the internal reporting system in development, but this method quickly turned out to be impractical especially in a situation where input from persons who are not familiar with programming is required.
Alongside of source code, working with Entity-Relationship diagrams provide a much more visual approach to data modeling and enables persons without programming knowledge to comprehend the structures and relations of the data much better. This way input from a bigger audience is much easier to collect and later on the physical code can be based on the entity-relationship diagrams, which simultaneously works as a documentation of the database system.
As data modeling is essentially a documentation process, it must be made clear that every time the application logic utilizing the model changes or some other change in database system takes place, the models evolve accordingly, so the documentation stays up to date. Keeping the documentation up to date requires a lot of manual work prone to errors so the possibility to generate visualization automatically based on the application code should be explored in the future.
There are several commercial and free tools available for data modeling and diagram generation (Lucidcharts, ER/Studio, erwin Data Modeler, Microsoft Visio etc.) but due to its easy utilization and integration with Microsoft Visual Studio Code, a free cross-platform open-source software Diagrams.net was selected to visualize the initial models seen in this work.
6.1 Modeling process
The general workflow in data modeling process is a sequence of tasks that should be performed in an iterative manner. Following three steps make up a rudimentary all-around backbone that can be applied to various data-producing processes.
6.1.1 Conceptual modeling
First step in modeling process visualized by Picture 11 was to figure a conceptual model and define the entity of interest that is wanted to be kept track of and stored in a database. In the scope of this thesis, the entities of interest are the results of mechanical testing of solid test specimens. The conceptual results entity consists of the test results dictated by the test standard used and the test specimen specific identification information.
Picture 11. Modeling process.
6.1.2 Logical modeling
Second step in modeling was to figure the logical models and define the attributes that the test standard entities and the test specimen entity contains. Test standard entities attributes are of course dictated by the test standard according to which the test is conducted, so a logical model from each possible test standard and its key attributes of interests needs to be thought out. As for the test specimen entity, the approach was to build a common model that is applicable to every possible specimen that undergoes mechanical testing. The common model includes information for identification, tracking, and database indexing purposes in conjunction with file specific meta data information if the data is converted from a report file. The standard specific models function as an extension of the common model, meaning that the final results instance stored in the database will be the combination of both models.
The conceptual and logical level of modeling are the levels where input, requirements, and ideas from a wider audience is easiest to gather. At these levels, the modeling takes place on a fairly abstract level and no programming skills or any other database related technical knowledge is necessary.
6.1.3 Physical modeling
The third step in modeling is to build the physical models and define the data types and key names which are largely dictated by the database management system and other technical specifications. At this level the work is in the hands of the developers and system administrators, or other people with the correct kind of technical know-how. At physical level the attributes required by the database management system are added and also the final structure or grouping of certain attributes of the model is defined. For harmonization purposes, it is extremely beneficial to make the models as similar as possible with each other. Similar key naming conventions and structuring makes querying the information from the database much more straightforward.
6.2 Implemented physical data models
The physical models presented in this thesis use JSON-compliant data types, some of which were already seen in picture 3, defined by ECMA-404 2017 standard. (Appendix 1)
6.2.1 Common data model
A diagram of physical common data model can be found in Appendix 2.
As the main objective in mechanical testing is to determine material specific properties, the common data model includes the attributes necessary to identify the specimen unambiguously. This way the specimen and the test results can be linked to a specific material batch of which the specimen was built from in conjunction with manufacturing process parameters and manufacturing equipment information.
For database indexing purposes, the common model contains an _id-attribute field for a database entry specific unique identification number. Additionally, such attributes as any additional notes, reference numbers or identification numbers, date of the test report, name of the test laboratory, or the specifications according to which the test was conducted can be saved under the info-attribute if provided with the test results. As a good example of the flexibility of non-relational database system, only the attribute name under which the possibly provided information gets stored needs to be agreed upon and documented, the format and structure of the data stored under the info-attribute is not restricted in any ways.
Common model also includes information about the purchase order or internal laboratory request made. In the physical model these are grouped under the key-attribute. In conjunction with such attributes as purchase order number, test type and order type information, this group of attributes includes the specimen and specimen packaging specific labeling information dictated by EOS Finland’s internal quality system.
The group of attributes under meta-attribute seen in the physical model are mainly for tracking and debugging purposes. The test report conversion system under development collects the data for these attributes from the original report file.
All the test related data will be distributed under the converted, raw and testParameters attributes, depending on the type of the test conducted. All attributes or variables considered as an input of the test conducted will be stored under testParameters attribute. The numerical result data acquired from each test will be stored under either converted or raw attribute, depending on the unit system in use. The raw attribute is populated only if the data acquired is in imperial units. The test specific models introduced in the next chapter further expand both converted and raw attributes with summary and detailed sub attributes. The summary attribute will contain a summary of test specific key result variables of interest, while the detailed attribute will function as a container for more specific raw data or for example time series data from which the values under summary attribute gets derived from.
As an example, once the system has been properly implemented, the attributes of the models would enable such database queries as gathering every possible test specimen built from a specific powder batch of a specific material for comparison. Further expanding the idea of analytic possibilities, for example generating rudimentary correlation matrices between mechanical properties and chemical composition should be achievable with very little effort once entities such as material batch chemical analysis results are brought into the database system too.
6.2.2 Test specific models
Tensile strength
A diagram of physical tensile strength data model can be found in Appendix 3.
In tensile testing the key test parameters of interest are the temperature at which the test is conducted, the type of the specimen, and the gauge sections geometric dimensions.
On the results side of things, the data of interest includes things such as ultimate tensile strength, yield strength, gauge section elongation, reduction of gauge sections cross-sectional area, maximum force applied, and if available the detailed time series of applied stress vs. strain or applied force vs. elongation. Most of the individual result variables mentioned before could be derived from the stress vs. strain time series. The model for tensile strength test results is based on ISO 6892-1 & ISO 6892-2 standards and acquired test data.
Hardness
A diagram of physical hardness data model can be found in Appendix 4.
As a relatively simplistic testing method, hardness testing parameter of interest is the test standard according to which the test is conducted. Result variables of interest are the standard specified hardness symbol, numerical values of each individual hardness test conducted, and the rounded average of individual tests which is considered as the
“hardness value.” Indentation force application duration is reported only if the duration exceeds 6s, and atmospheric temperature during testing is reported only if it exceeds the range of 10-35°C. The model for hardness test result is based on ISO 6508-1 standard and acquired test data.
Impact toughness
A diagram of physical impact toughness data model can be found in Appendix 5.
As a temperature-sensitive test type, the parameters of interest in impact toughness testing includes both atmospheric temperature and specimen conditioning temperature.
Specimens’ geometric dimensions are largely dictated by the test standard ISO 148-1 according to which the model is built, but exceptions in geometry can occur. Main result variables of interest consist of numerical value of the absorbed energy and a boolean value of whether the specimen disintegrated. The model for impact toughness test result is based on ISO 148-1 standard.
Creep & Stress rupture
A diagram of creep & stress rupture data model can be found in Appendix 6.
Creep testing is a long duration test by nature, where the main result variable of interest is a plastic strain vs. time time-series. Other result variables are numerical values indicating the duration of the test, elongation of the specimen, reduction of the gauge sections cross-sectional area, gauge sections total plastic strain, and time to rupture.
Additional info is collected as boolean value of whether the specimen fractured, and as a string description of the fracture location. Test parameters of interest are specimens’
geometric dimensions, and the stress and temperature at which the test is conducted.
The model for creep & stress rupture test results is based on ASTM E139 standard and acquired test data.
Fatigue
A diagram of fatigue data model can be found in Appendix 7.
As a complicated testing method, fatigue testing includes a multitude of parameters that affects the methodology of the test. A generic collection of fatigue test parameters includes specimens’ geometric dimensions, stress at which the test is conducted,
As a complicated testing method, fatigue testing includes a multitude of parameters that affects the methodology of the test. A generic collection of fatigue test parameters includes specimens’ geometric dimensions, stress at which the test is conducted,