A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions

As machining processes are complex in nature due to the involvement of large plastic strains occurring at higher strain rates, and simultaneous thermal softening of material, it is necessary for manufacturers to have some manner of determining whether the inputs will achieve the desired outputs within the limitations of available resources. However, finite element simulations—the most common means to analyze and understand the machining of high-performance materials under various cutting conditions and environments—require high amounts of processing power and time in order to output reliable and accurate results which can lead to delays in the initiation of manufacture. The objective of this study is to reduce the time required prior to fabrication to determine how available inputs will affect the desired outputs and machining parameters. This study proposes a hybrid predictive methodology where finite element simulation data and machine learning are combined by feeding the time series output data generated by Finite Element Modeling to an Artificial Neural Network in order to acquire reliable predictions of optimal and/or expected machining inputs (depending on the application of the proposed approach) using what we describe as a backwards training model. The trained network was then fed a test dataset from the simulations, and the results acquired show a high degree of accuracy with regards to cutting force and depth of cut, whereas the predicted/expected feed rate was wildly inaccurate. This is believed to be due to either a limited dataset or the much stronger effect that cutting speed and depth of cut have on power, cutting forces, etc., as opposed to the feed rate. It shows great promise for further research to be performed for implementation in manufacturing facilities for the generation of optimal inputs or the real-time monitoring of input conditions to ensure machining conditions do not vary beyond the norm during the machining process.     

Combined use of Amplified Fragment Length Polymorphism and IS<Emphasis Type="Italic">6110</Emphasis> -RFLP in fingerprinting clinical isolates of <Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis> from Kerala,South India

Background  

DNA fingerprinting by IS6110-RFLP has shown a high incidence of Mycobacterium tuberculosis isolates having no and low copies of the insertion sequence in Kerala, South India. Amplified Fragment Length Polymorphism (AFLP) would scan the entire genome rather than a few repetitive elements, we thought that this technique would help us in differentiating the large reservoir of isolates from an endemic region. Here we evaluate the ability of Amplified Fragment Length Polymorphism (AFLP) to type clinical isolates.     

Non-contact ulcer area calculation system for neuropathic foot ulcer

Background

Around 125,785 new cases in year 2013–14 of leprosy were detected in India as per WHO report on leprosy in September 2015 which accounts to approximately 62% of the total new cases. Anaesthetic foot caused by leprosy leads to uneven loading of foot leading to ulcer in approximately 20% of the cases. Much efforts have gone in identifying newer techniques to efficiently monitor the progress of ulcer healing. Current techniques followed in measuring the size of ulcers, have not been found to be so accurate but are still is followed by clinicians across the globe. Quantification of prognosis of the condition would be required to understand the efficacy of current treatment methods and plan for further treatment. This study aims at developing a non contact technique to precisely measure the size of ulcer in patients affected by leprosy.

Evolution of World Journal of Clinical Cases over the past 5 years

Analysis of the articles published in any journal is necessary to ascertain the performance of the journal in the academia. The author made a scientometric analysis of the articles published in the World Journal of Clinical Cases in the past 5 years and present the data to the readers.