Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling

Noor, Wazed Ibne; Saleh, Tanveer; Rashid, Mir Akmam Noor; Ibrahim, Azhar Mohd; Ali, Mohamed Sultan Mohamed

doi:10.1007/s00170-021-07910-w

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…In addition to conventional machining, machining conditions were predicted for non-conventional processes [ 99 ]. Electrical discharge machining (EDM), one of the unconventional machining processes using electric energy to remove materials, has been widely used in high-performance engineering sectors [ 100 ].…”

Section: Intelligence In State-of-the-art Manufacturing Technology: M...mentioning

confidence: 99%

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

et al. 2023

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Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

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Section: Intelligence In State-of-the-art Manufacturing Technology: M...mentioning

confidence: 99%

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

et al. 2023

Self Cite

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“…EDM separates the tool and workpiece with a dielectric fluid medium and applies discrete discharges. Spark discharges melt and evaporate a minor amount of workpiece material across a gap (spark gap) between the tool and the workpiece [5]. During a machining process, spark discharges always happen at the highest electrical potential point, which floats around the machining gap at random [6,16].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Modeling of Surface Roughness in Powder Mix EDM and Pure EDM Using Central Composite Design

Zain

Rashid²,

Khan³

et al. 2023

ARASET

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Conventional surface modification methods, such as ion insertion and laser surface melting, have recently seen growing importance in the innovative usage of the electrical discharge machining process (EDM) as a competitive substitute. Surface composition and properties are affected by several factors, including erosion of work materials and tool materials during the process, as well as the formation of plasma channels from the vaporized materials. This indicates that, under certain machining conditions, deliberate materials transfer may be possible using either a composite electrode by scattering metallic powders in a dielectric or a combination of the two. However, the traditional electrode has not been very successful in the surface modification process. Although it has been considered, expanding research into the use of powder additives through EDM techniques to enhance the level of surface modification is lacking. In addition, the pulse interval is not included in the existing works that examine the impact of technique parameters on surface modification. Because of this, a thorough optimization of the process has not been possible to characterize the connection between the independent variables under control and the machining parameters. To take full advantage of the benefits offered by EDM, it is necessary to learn more about the missing links between the process and its desired outcomes, such as whether or not surface alloying of metallic materials using EDM is feasible and what controls should be used for the various variables involved. The purpose of this study was to assess the efficiency of EDM-based surface alloying of stainless steel using Tantalum Carbide (TaC) powder as an additive by measuring the resulting surfaces' roughness (SR). It was found that RaPMEDM 11 recorded at current 5A and on-time 6.30 off-time 7.00 with powder concentration 25g/L. The RaPMEDM is easy to predict by controlling factor current (A) and on-time (B). At the higher current of 7.25A, RaPMEDM showed to decrease slightly. This result justifies TaC powder starts to control and modify the surface of stainless steel at a high factor current. In the case of without additives RaEDM, at maximum current 5 A, on-time 6.26 μs, and off-time 7 μs, RaEDM recorded 7 μm. Although it provided the highest Ra compared to RaPMEDM.

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“…PMEDM uses electrically conductive powder and dielectric fluid to increase sparks between the tool and the workpiece and lower the fluid's insulating strength [7][8][9]. This method improves dielectric fluid breakdown.…”

Section: Introductionmentioning

confidence: 99%

MRR and TWR Study of Powder Mix EDM and Pure EDM Based on Response Surface Methodology

Zain

Rashid

Khan

et al. 2023

ARAM

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Powder-mixed electrical discharge machining (PMEDM) is the process of enhancing the output of the machined surface by combining dielectric fluid with various types of powders. This process is quickly gaining acceptance in the electrical discharge machining (EDM) sector. The purpose of this study is to determine whether a dielectric fluid containing tantalum carbide (TaC) powder can improve material removal rate (MRR) also lessen tool wear rate (TWR) during the subsequent EDM machining of stainless steel material. During the machining, the material removal rates, tool wear rate, and mathematical models of two different EDM mediums were examined. For the machining procedure, kerosene dielectric fluid containing TaC powder at a concentration of 25.0 g/L was used. The machining input variables were used peak current, pulse on time, and pulse off time. We determined how these variables affected the MRR and TWR of the copper-based EDMed electrode tools. During electrical discharge machining, the MRR for stainless steel (SUS 304) was increased by MRRPMEDM by 4.3 to 5.3% and TWRpEDM was reduced by 37.9% when TaC powder additive was used. Optimized results also show that TWR and maximum MRR can be achieved at 81.98% and 13.779mm3/min respectively with 83.50% desirability whenever the pulse on-time and pulse off-time are 6.20 µs and 6.50 µs respectively. The models are reliable and can be used to forecast the machining responses within the experimental region, it can be said. The MRR and TWR model for EDM with TaC powder additive (MRRPMEDM) identifies current as the most significant factor, followed by pulse on time and pulse off-time.

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Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling

Cited by 6 publications

References 40 publications

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

Analysis and Modeling of Surface Roughness in Powder Mix EDM and Pure EDM Using Central Composite Design

MRR and TWR Study of Powder Mix EDM and Pure EDM Based on Response Surface Methodology

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