Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator
Lean thinking is interested in identifying and resisting defects that affect business safety, like welding defects of the cooling pipe exposing the chilled foodstuffs parcels to spoilage, posing a danger to the land transportation investment. Four clusters are used to identify welding flaws ...
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IEEE
2025-01-01
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| author | Ahmed M. Abed Tamer S. Gaafar |
| author_facet | Ahmed M. Abed Tamer S. Gaafar |
| author_sort | Ahmed M. Abed |
| collection | DOAJ |
| description | Lean thinking is interested in identifying and resisting defects that affect business safety, like welding defects of the cooling pipe exposing the chilled foodstuffs parcels to spoilage, posing a danger to the land transportation investment. Four clusters are used to identify welding flaws’ severity to keep them from getting worse via mapping their finite element meshes (MFEM) and using the high-frequency current ring (HFCR) lean technique to identify their size, depth, growth direction (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {{R}_{s}}$ </tex-math></inline-formula>), and shape deployment via prediction to assign the flaws as Susceptible (S), and preludes to transfer to a Quarantine cluster (Q) to take one decision whether rescued (R), Isolated (I) or Eliminated (E). We are creating a “digital Jidoka twin system” (SQ(R/I/E)) with a controller segment programmed with machine learning (ML) algorithms that use the MFEM’s huge and uneven data to sort defects and their causes. Hybridising the Random-Forest algorithm with Dingo optimisation and called Regulated Random Forest (RRF) to precisely identify defect clusters and then predict the welding defect growth rate (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {{R}_{s}}$ </tex-math></inline-formula>) using the Cat-boost optimiser, which is enhanced by a beetle search mechanism called CatBAS. The RRF is superior to Apriori, ECLAT, and FP Growth by 23.98%, 7.44%, and 8.38%, respectively, while CatBAS is superior to XG-boost by 94.62% in response time with a 1.04175% error that activates the treating stage-V quickly. The SQ(R/I/E) increased parcel rescues by 38.2% and reduced financial losses. Protecting chilled foodstuffs transport from spoilage serves the SDG <xref ref-type="disp-formula" rid="deqn2-deqn3">(2)</xref>. |
| format | Article |
| id | doaj-art-574c05f3eb2f4fa3b4212694c9c92000 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-574c05f3eb2f4fa3b4212694c9c920002025-01-31T00:00:52ZengIEEEIEEE Access2169-35362025-01-0113192661929410.1109/ACCESS.2025.353337710851261Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka SimulatorAhmed M. Abed0https://orcid.org/0000-0001-5315-3850Tamer S. Gaafar1Department of Industrial Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer and Systems, Zagazig University, Zagazig, EgyptLean thinking is interested in identifying and resisting defects that affect business safety, like welding defects of the cooling pipe exposing the chilled foodstuffs parcels to spoilage, posing a danger to the land transportation investment. Four clusters are used to identify welding flaws’ severity to keep them from getting worse via mapping their finite element meshes (MFEM) and using the high-frequency current ring (HFCR) lean technique to identify their size, depth, growth direction (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {{R}_{s}}$ </tex-math></inline-formula>), and shape deployment via prediction to assign the flaws as Susceptible (S), and preludes to transfer to a Quarantine cluster (Q) to take one decision whether rescued (R), Isolated (I) or Eliminated (E). We are creating a “digital Jidoka twin system” (SQ(R/I/E)) with a controller segment programmed with machine learning (ML) algorithms that use the MFEM’s huge and uneven data to sort defects and their causes. Hybridising the Random-Forest algorithm with Dingo optimisation and called Regulated Random Forest (RRF) to precisely identify defect clusters and then predict the welding defect growth rate (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {{R}_{s}}$ </tex-math></inline-formula>) using the Cat-boost optimiser, which is enhanced by a beetle search mechanism called CatBAS. The RRF is superior to Apriori, ECLAT, and FP Growth by 23.98%, 7.44%, and 8.38%, respectively, while CatBAS is superior to XG-boost by 94.62% in response time with a 1.04175% error that activates the treating stage-V quickly. The SQ(R/I/E) increased parcel rescues by 38.2% and reduced financial losses. Protecting chilled foodstuffs transport from spoilage serves the SDG <xref ref-type="disp-formula" rid="deqn2-deqn3">(2)</xref>.https://ieeexplore.ieee.org/document/10851261/Logistic regression methodscracks finite-elementoptimizationmagnetic induction |
| spellingShingle | Ahmed M. Abed Tamer S. Gaafar Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator IEEE Access Logistic regression methods cracks finite-element optimization magnetic induction |
| title | Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator |
| title_full | Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator |
| title_fullStr | Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator |
| title_full_unstemmed | Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator |
| title_short | Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator |
| title_sort | hybridize machine learning methods and optimization techniques to analyze and repair welding defects via digital twin of jidoka simulator |
| topic | Logistic regression methods cracks finite-element optimization magnetic induction |
| url | https://ieeexplore.ieee.org/document/10851261/ |
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