Tool Life Stage Prediction in Micro-Milling From Force Signal Analysis Using Machine Learning Methods

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Alwin Varghese ◽  
Vinay Kulkarni ◽  
Suhas S. Joshi
Keyword(s):  
Machine Learning ◽  
Tool Life ◽  
Life Stage ◽  
Micro Milling ◽  
Life Stages ◽  
Learning Models ◽  
Tool Breakage ◽  
Tool Diameter ◽  
Force Data ◽  
Machine Learning Models

Abstract Tool condition monitoring is difficult in micro-milling due to irregular wear and chipping of the cutting edges, which lead to unexpected tool breakage. This study demonstrates the use of force data to reliably predict different tool life stages until tool breakage, while micro-milling hard materials like stainless steel (SS304) using tungsten carbide tools of 500 μm diameter. Extensive experiments involving machining of 465 slots over 62 min of machining time were performed in this study. The resulting voluminous force data were analyzed to divide the tool life into three stages based on the variation in the forces and other related features. The first stage is the initial 12.5% of the tool life, second stage consists of 12.5–70% of tool life, and the third stage is from 70% to 100% tool life. The analysis of the tool wear and cutting forces shows that the average tool diameter reduces by 32 μm, 67 μm and 108 μm, and the average resultant cutting force were 2.45 N, 4.17 N, and 4.93 N in stage 1, 2, and 3, respectively. To avoid catastrophic breakage of the tool, the tool life stages are predicted from the force data using machine learning models. Among the machine learning models, random forest method gave a better prediction accuracy of 88.5%. The model was further improved by incorporating the initial cutting edge radius as an additional feature, and the variance in the prediction was seen to drop by 48.76%.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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