Monitoring and Control of Manufacturing Process to Assist the Surface Workpiece Quality When Drilling

Vrabeľ, Marek; Maňková, Ildikó; Beňo, Jozef

doi:10.1016/j.procir.2015.12.092

Cited by 16 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in many cases, ANN has proven its capability in solving prediction problems due to its capability to assimilate the relationship between input and output (Azadeh et al, 2014;Haykin, 2009;Mehrjerdi & Aliheidary, 2014). Several researchers have implemented the MLP in their studies and have successfully solved prediction problems in production operations (Karam, Centobelli, Doriana, Addona, & Teti, 2016;Poonia, Soni, & Khanam, 2016;Vrabel, Mankova, & Beno, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Variation on the Number of Hidden Nodes Through Multilayer Perceptron Networks to Predict the Cycle Time

Ahmarofi

Ramli

Abidin

et al. 2019

Journal of Information and Communication Technology

View full text Add to dashboard Cite

Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Variation on the Number of Hidden Nodes Through Multilayer Perceptron Networks to Predict the Cycle Time

Ahmarofi

Ramli

Abidin

et al. 2019

Journal of Information and Communication Technology

View full text Add to dashboard Cite

show abstract

“…Therefore, it is very suitable to be used for estimating the quality characteristics in machining processes. Vrabel et al [17] presented an ANN adaptive control system for the drilling process of a nickel-based superalloy of Udimet 720. The surface roughness was assessed based on the process monitoring indices such as torque, thrust force, power, feed rate, cutting spend, and tool flank wear.…”

Section: Introductionmentioning

confidence: 99%

Modeling for a small-hole drilling process of engineering plastic PEEK by Taguchi-based neural network method

Chang

Lin

2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Engineering plastics have specific properties in the strength, hardness, impact resistance, and aging persistence, often used for structural plates and electronic components. However, the holes made by the drilling process always shrink after the cutting heat dispersion due to their high thermal expansion coefficient. Especially for small-hole fabrication, drilling parameters must be discussed thoughtfully to acquire a stable hole quality. This study developed parameter models by the Taguchibased neural network method to save the experimental resources on drilling of engineering plastic, polyetheretherketone (PEEK). A three-level full-factorial orthogonal array experiment, L27, was first conducted for minimizing the thrust force, hole shrinkage in diameter, and roundness error. The experiments were operated by a peck-drilling process with cyclic lubricant, and the diameter was 1 mm. In terms of the network modeling, four variables were designated to the input layer neurons including the spindle speed, depth of peck-drilling, feed rate, and thrust force detected; and that of the output layer were the diameter shrinkage and roundness of the hole drilled. The models were trained by a stepped-learning procedure to expand the network's field information stage by stage. After three stages of training, the models developed can provide precise simulations for the network's training sets and accurately predict the hole's characteristics for the non-trained cases.

show abstract

“…The currently used relationships between the information signal and tool wear are phenomenological, often having no quantitative substantiation [42][43][44][45][46]. Therefore, at the present level of development of material processing by cutting, it is necessary to determine the degree of correlation between one of the promising information signals -the sound generated during the cutting process, the wear of the cutter and the surface roughness of the workpiece [47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control of the Metalworking Process With Using Sound Generated During Cutting

Nahornyi¹,

Panda²,

Valíček³

et al. 2021

Preprint

View full text Add to dashboard Cite

The presented paper proposes a method of adaptive control of the metal cutting process based on the analysis and interpretation of recorded sound for the identification of the cutting process. The presented research results clearly demonstrated the efficiency of using the sound generated during cutting by CNC lathe 16 K20T1 for the adaptive metalworking control. The sound makes it possible to control the accuracy of the workpiece part geometry and the surface quality in the online mode. The online mode also makes it possible to make an informed decision about a timely interruption of the cutting process or the continuation of the process in the altered modes for its successful completion. As the research results presented in the paper show, there is a close correlation between the sound generated by cutting and the roughness of the surface being machined or the degree of wear of the cutting tool. The correlation between the sound and roughness of the machined surface is 0.94, whereas between the sound and wear of the cutting tool is 0.93. The paper aims to use the generated sound as operational information needed for adaptive control of the metal processing process and timely monitoring and diagnostics of the condition of processed materials using the precision index, newly introduced as the machining quality index.

show abstract

Monitoring and Control of Manufacturing Process to Assist the Surface Workpiece Quality When Drilling

Cited by 16 publications

References 7 publications

Variation on the Number of Hidden Nodes Through Multilayer Perceptron Networks to Predict the Cycle Time

Variation on the Number of Hidden Nodes Through Multilayer Perceptron Networks to Predict the Cycle Time

Modeling for a small-hole drilling process of engineering plastic PEEK by Taguchi-based neural network method

Adaptive Control of the Metalworking Process With Using Sound Generated During Cutting

Contact Info

Product

Resources

About