Foundry Data Analytics to Identify Critical Parameters Affecting Mechanical Properties of Investment Castings

2017 ◽  
Author(s):  
Amit Sata ◽  
B. Ravi
Keyword(s):  
Mechanical Properties ◽  
Posterior Probability ◽  
Cast Alloy ◽  
Input Parameter ◽  
Real Life ◽  
Critical Parameters ◽  
Real Life Data ◽  
Data Analytic ◽  
Range Of Values ◽  
Cast Parts

Besides shape fidelity and internal soundness, mechanical properties have become critical acceptance criteria for investment cast parts. These properties are mainly driven by the chemical composition of cast alloy as well as process parameters. It is however, difficult to identify the most critical parameters and their specific values influencing the mechanical properties. This is achieved in the present work by employing foundry data analytic based on Bayesian inference to compute the values of posterior probability for each input parameter. This is demonstrated on real-life data collected from an industrial foundry. Controlling the identified parameters within the specific range of values resulted in improved mechanical properties. Unlike computer simulation, artificial neural networks and statistical methods explored by earlier researchers, the proposed approach is easy to implement in industry for controlling and optimizing the parameters to achieve the desired range of mechanical properties. The current work also shows the way forward for building similar systems for other casting and manufacturing processes.

Foundry Data Analytics to Identify Critical Parameters Affecting Quality of Investment Castings

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Amit Sata ◽  
B. Ravi
Keyword(s):  
Mechanical Properties ◽  
Data Analytics ◽  
Posterior Probability ◽  
Input Parameter ◽  
Real Life ◽  
Joint Probability ◽  
Critical Parameters ◽  
Industrial Sectors ◽  
Range Of Values

Investment castings are used in industrial sectors including automobile, aerospace, chemical, biomedical, and other critical applications; they are required to be of significant quality (free of defects and possess the desired range of mechanical properties). In practice, this is a big challenge, since there are large number of parameters related to process and alloy composition are involved in process. Also, their values change for every casting, and their effect on quality is not very well understood. It is, however, difficult to identify the most critical parameters and their specific values influencing the quality of investment castings. This is achieved in the present work by employing foundry data analytics based on Bayesian inference to compute the values of posterior probability for each input parameter. Computation of posterior probability for each parameter in turn involves computation of local probability (LP), prior odd, conditional probability (CP), joint probability (JP), prior odd, likelihood ratio (LR) as well as posterior odd. Computed value of posterior probability helps (parameters are considered to be critical if the value of posterior probability is high) in identifying critical parameter and their specific range of values affecting quality of investment castings. This is demonstrated on real-life data collected from an industrial foundry. Controlling the identified parameters within the specific range of values resulted in improved quality. Unlike computer simulation, artificial neural networks (ANNs), and statistical methods explored by earlier researchers, the proposed approach is easy to implement in industry for controlling and optimizing the parameters to achieve castings that are defect free as well as in desired range of mechanical properties. The current work also shows the way forward for building similar systems for other casting and manufacturing processes.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

The epidemiology of thyroid cancer within a center of excellence in Athens: Real life data

2017 ◽  
Author(s):  
Eleni Pantazi ◽  
Alexios Travlos ◽  
Evaggelia Vogiatzi ◽  
Ifigenia Kostoglou-Athanassiou
Keyword(s):  
Thyroid Cancer ◽  
Real Life ◽  
Life Data ◽  
Center Of Excellence ◽  
Real Life Data

Severe plastic deformation of Al-1%Cu Alloy processed by a Simple Cyclic Extrusion Compression technique

2018 ◽  
Vol 1 (1) ◽  
pp. 77-90
Author(s):  
Walaa Abdelaziem ◽  
Atef Hamada ◽  
Mohsen A. Hassan
Keyword(s):  
Mechanical Properties ◽  
Plastic Deformation ◽  
Severe Plastic Deformation ◽  
Deformation Behavior ◽  
Cast Alloy ◽  
Surface Hardness ◽  
Grain Structure ◽  
Compression Technique ◽  
Cu Alloy ◽  
Cyclic Extrusion Compression

Severe plastic deformation is an effective method for improving the mechanical properties of metallic alloys through promoting the grain structure. In the present work, simple cyclic extrusion compression technique (SCEC) has been developed for producing a fine structure of cast Al-1 wt. % Cu alloy and consequently enhancing the mechanical properties of the studied alloy. It was found that the grain structure was significantly reduced from 1500 µm to 100 µm after two passes of cyclic extrusion. The ultimate tensile strength and elongation to failure of the as-cast alloy were 110 MPa and 12 %, respectively. However, the corresponding mechanical properties of the two pass CEC deformed alloy are 275 MPa and 35%, respectively. These findings ensure that a significant improvement in the grain structure has been achieved. Also, cyclic extrusion deformation increased the surface hardness of the alloy by 49 % after two passes. FE-simulation model was adopted to simulate the deformation behavior of the material during the cyclic extrusion process using DEFORMTM-3D Ver11.0. The FE-results revealed that SCEC technique was able to impose severe plastic strains with the number of passes. The model was able to predict the damage, punch load, back pressure, and deformation behavior.

Sign in / Sign up

Export Citation Format

Share Document