Ensemble learning with a genetic algorithm for surface roughness prediction in multi-jet polishing

Wang, Ruoxin; Cheng, Mei Na; Loh, Yee Man; Wang, Chunjin; Cheung, Chi Fai

doi:10.1016/j.eswa.2022.118024

Cited by 22 publications

(8 citation statements)

References 30 publications

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“…In the present work, the surface corresponds to Ti-6Al-4V alloy flat specimens built by Selective Laser Melting (SLM) after being finished by blasting and electropolishing techniques. Input variables to be taken into account were industrial variables considering specimens characteristics (deposition angle and as-built surface roughness), blasting parameters (type of abrasive particles, time and pressure), and the electropolishing ones (time, voltage and agitation frequency), making it very difficult to develop an analytical or a numerical model that relates them with the surface roughness [23]. Therefore, following the example of previous works such as [24][25][26], an Artificial Neural Network (ANN) technology, which is a branch of artificial intelligence that attempts to achieve human brain capability [27], was designed and trained to predict surface roughness (Ra).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness of SLM Built Parts after Finishing Processes Using an Artificial Neural Network

Soler

Telleria

García-Blanco

et al. 2022

JMMP

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A known problem of additive manufactured parts is their poor surface quality, which influences product performance. There are different surface treatments to improve surface quality: blasting is commonly employed to improve mechanical properties and reduce surface roughness, and electropolishing to clean shot peened surfaces and improve the surface roughness. However, the final surface roughness is conditioned by multiple parameters related to these techniques. This paper presents a prediction model of surface roughness (Ra) using an Artificial Neural Network considering two parameters of the SLM manufacturing process and seven blasting and electropolishing processes. This model is proven to be in agreement with 429 experimental results. Moreover, this model is then used to find the optimal conditions to be applied during the blasting and the electropolishing in order to improve the surface roughness by roughly 60%.

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Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness of SLM Built Parts after Finishing Processes Using an Artificial Neural Network

Soler

Telleria

García-Blanco

et al. 2022

JMMP

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show abstract

“…Then, the desired surface finish improvement and material removal are fed to the algorithm, which enforces GA to find the optimum polishing parameters. Therefore, machine learning regression methods were utilized for prediction of surface roughness [ 164 ].…”

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

confidence: 99%

“…Furthermore, electrochemical jet machining was utilized to finish AM parts created by PBF and to subsequently micro-pattern these for increasing part functionality [ 167 ]. In multi-jet polishing (MJP), a surface roughness prediction model was developed based on ensemble learning with a genetic algorithm (ELGA), as illustrated in Figure 16 a–c [ 164 ].…”

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

confidence: 99%

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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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“…Surface quality is a critical indicator during manufacturing due to its relationship with fatigue strength, deformation and other service performance of the component. However, it requires a lot of effort to realize the evolution of surface quality through theoretical modeling or automatic monitoring 1 – 4 . Surface roughness is a significant side of the entirely quality of machined parts, which rest with cutting parameters, tool condition, and machine vibrations 5 , 6 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

Guo,

Zhou,

et al. 2023

Sci Rep

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The roughness of the part surface is one of the most crucial standards for evaluating machining quality due to its relationship with service performance. For a preferable comprehension of the evolution of surface roughness, this study proposes a novel surface roughness prediction model on the basis of the unity of fuse d signal features and deep learning architecture. The force and vibration signals produced in the milling of P20 die steel are collected, and time and frequency domain feature from the acquired signals are extracted by variational modal decomposition. The GA-MI algorithm is taken to select the signal features that are relevant to the surface roughness of the workpiece. The optimal feature subset is analyzed and used as the input of the prediction model. DBN is adopted to estimate the surface roughness and the model parameters are optimized by ISSA. (Reviewer 1, Q1) The separate force, vibration and fusion signal information are brought into the DBN model and the ISSA-DBN model for the prediction of surface roughness, and the results show that the accuracy of the roughness prediction is as follows, respectively DBN: 78.1%, 68.8% and 84.4%, and ISSA-DBN: 93.8%, 87.5% and 100%.

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Ensemble learning with a genetic algorithm for surface roughness prediction in multi-jet polishing

Cited by 22 publications

References 30 publications

Prediction of Surface Roughness of SLM Built Parts after Finishing Processes Using an Artificial Neural Network

Prediction of Surface Roughness of SLM Built Parts after Finishing Processes Using an Artificial Neural Network

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

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

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