Towards Developing Big Data Analytics for Machining Decision-Making
Abstract
:1. Introduction
- How should the documentation process for manufacturing be carried out?
- What should be the process for digitizing and integrating the prepared documents with process-relevant BD in terms of DMCs?
- What is the proper procedure for utilizing the relevant dataset found in the shared documents?
- What should be the method for meeting the computational challenges underlying the relevant datasets generated from multiple sources?
- What is the recommended method for extracting rules and drawing conclusions?
2. Literature Review
3. Proposed BDA
3.1. Fundamental Concepts
- How should the documentation process for manufacturing be carried out? (Q1)
- What should be the process for integrating the prepared documents with process-relevant BD? (Q2)
- What is the proper procedure for utilizing the relevant dataset (specifically, CV–EV-related datasets) found in the shared documents? (Q3)
- What should be the method for meeting the computational challenges? (Q4)
- What is the recommended method for extracting rules and drawing conclusions? (Q5)
3.2. BDA Framework
- (1)
- Data Preparation System (DPS);
- (2)
- Data Exploration System (DES);
- (3)
- Data Visualization System (DVS);
- (4)
- Data Analysis System (DAS);
- (5)
- Knowledge Extraction System (KES).
4. Developing BDA
4.1. Data Preparation System (DPS)
4.2. Data Exploration System (DES)
4.3. Data Visualization System (DVS)
4.4. Data Analysis System (DAS)
4.5. Knowledge Extraction System (KES)
5. Case Study
6. Conclusions
- Data Preparation System (DPS);
- Data Exploration System (DES);
- Data Visualization System (DVS);
- Data Analysis System (DAS);
- Knowledge Extraction System (KES).
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Source (Si) | Control Variable (CVj=1, …, m) | Evaluation Variable (EVk=1, …, n) | Datasets ) | ||
---|---|---|---|---|---|
j | CVj | k | EVk | ||
S1 [64] | 1 2 3 4 5 | Gap Voltage Current Pulse Off Time Gas Pressure Rotational Speed | 1 2 3 4 | Material Removal Rate Tool Wear Rate Radial Over Cut Depth Achieved | 20 |
S2 [65] | 1 2 3 4 5 6 | Gap Voltage Current Pulse Off Time Gas Pressure Rotational Speed Shielding Clearance | 1 2 3 4 5 | Material Removal Rate Tool Wear Rate Oversize (entry of hole) Oversize (50% of hole depth) Oversize (90% of hole depth) | 30 |
S3 [66] | 1 2 3 4 | Current Pulse On Time Duty Factor Rotational Speed | 1 2 | Material Removal Rate Tool Wear Rate | 8 |
S4 [67] | 1 2 3 4 5 6 | Gap Voltage Current Pulse Off Time Gas Pressure Rotational Speed Shielding Clearance | 1 2 3 4 | Material Removal Rate Tool Wear Rate Oversize Depth Achieved | 24 |
S5 [68] | 1 2 3 4 5 6 | Current Pulse On Time Duty Factor Gas Pressure Rotational Speed Gas Type | 1 2 3 | Material Removal Rate Surface Roughness Radial Over Cut | 18 |
S6 [69] | 1 2 3 4 5 | Current Pulse On Time Duty Factor Gas Pressure Rotational Speed | 1 2 3 | Material Removal Rate Surface Roughness Radial Over Cut | 15 |
Total number of CV–EV-centric datasets | 115 |
CVs | Sources, Si | |||||||||
S1 | S2 | S3 | S5 | S6 | ||||||
Outcomes, Oj | ||||||||||
O1 | O2 | O3 | O5 | O6 | ||||||
CA | UA | CA | UA | CA | UA | CA | UA | CA | UA | |
R-values for Correlation Analysis (CA) and Uncertainty Analysis (UA) | ||||||||||
EV = MRR | ||||||||||
V | −0.904 | −0.75 | −0.852 | −0.957 | ||||||
I | 0.996 | 0.917 | 0.997 | 0.993 | 0.989 | 0.195 | 0.999 | 0.972 | 0.995 | 0.998 |
Toff | −0.653 | −0.69 | −0.661 | −0.754 | ||||||
Ton | 0.989 | 0.071 | 0.978 | 0.993 | 0.523 | 0.593 | ||||
η | 0.222 | 0.181 | 0.986 | 0.99 | 0.944 | 0.987 | ||||
P | 0.93 | 0.926 | 0.369 | 0.965 | 0.902 | 0.863 | 0.996 | 0.798 | ||
S | 0.993 | 0.826 | 1 | 0.957 | 0.707 | −0.009 | −0.419 | −0.691 | 0.265 | 0.452 |
Cb | −0.397 | 0.585 | ||||||||
Gas Type | 0.934 | 0.967 |
Si | Rk | Rules |
---|---|---|
S1 | R1 | |
S2 | R2 | |
S3 | R3 | |
S5 | R5 | |
S6 | R6 |
Si | Rk | MRR′ (mm3/min) | MRR″ (mm3/min) |
---|---|---|---|
S1 | R1 | 1.497 | 1.497 |
S2 | R2 | 0.811 | 0.811 |
S3 | R3 | No conclusion | 9.9425 |
S5 | R5 | No conclusion | 3.77 |
S6 | R6 | 5.31 | 5.31 |
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Ghosh, A.K.; Fattahi, S.; Ura, S. Towards Developing Big Data Analytics for Machining Decision-Making. J. Manuf. Mater. Process. 2023, 7, 159. https://doi.org/10.3390/jmmp7050159
Ghosh AK, Fattahi S, Ura S. Towards Developing Big Data Analytics for Machining Decision-Making. Journal of Manufacturing and Materials Processing. 2023; 7(5):159. https://doi.org/10.3390/jmmp7050159
Chicago/Turabian StyleGhosh, Angkush Kumar, Saman Fattahi, and Sharifu Ura. 2023. "Towards Developing Big Data Analytics for Machining Decision-Making" Journal of Manufacturing and Materials Processing 7, no. 5: 159. https://doi.org/10.3390/jmmp7050159