Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments

Sağlam, Hilal

doi:10.1007/s00170-010-3133-1

Cited by 25 publications

(8 citation statements)

References 29 publications

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“…Indeed, the mean absolute cutting force values estimated from the cutting force F(t) measurements at v c1 and v c3 were |F|=360 N and |F|=301 N, respectively. This indicates that, although blade wear in band sawing is expected to increase at a higher cutting speed v c [20], the time of cut, which contributes significantly to the cutting economy, can be lowered by increasing both the feed rate v f and the cutting speed v c . It is thus advisable to avoid chatter in band sawing by increasing the cutting speed.…”

Section: Chatter Avoidancementioning

confidence: 99%

Stability diagrams and chatter avoidance in horizontal band sawing

2015

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Section: Chatter Avoidancementioning

confidence: 99%

Stability diagrams and chatter avoidance in horizontal band sawing

2015

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“…To investigate the influence of these parameters on the performance of the EPABC, we implement the Taguchi method of design of experiment (DOE) [38][39][40][41][42] by using the problem case 1. We set four-factor levels for each parameter as shown in Table 1.…”

Section: Investigation Of Parameters Settingmentioning

confidence: 99%

An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling

Wang

Zhou

et al. 2011

Int J Adv Manuf Technol

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In this paper, an enhanced Pareto-based artificial bee colony (EPABC) algorithm is proposed to solve the multi-objective flexible job-shop scheduling problem with the criteria to minimize the maximum completion time, the total workload of machines, and the workload of the critical machine simultaneously. First, it uses multiple strategies in a combination way to generate the initial solutions as the food sources with certain quality and diversity. Second, exploitation search procedures for both the employed bees and the onlooker bees are designed to generate the new neighbor food sources. Third, crossover operators are designed for the onlooker bee to exchange useful information. Meanwhile, it uses a Pareto archive set to record the nondominated solutions that participate in crossover with a certain probability. To enhance the local intensification, a local search based on critical path is embedded in the onlooker bee phase, and a recombination and select strategy is employed to determine the survival of the individuals. In addition, population is suitably adjusted to maintain diversity in scout bee phase. By using Taguchi method of design of experiment, the influence of several key parameters is investigated. Simulation results based on the benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EPABC algorithm.

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“…In addition, there are currently few published papers that focus on parameter optimization of the band saw machine. Saglam (Saglam, 2011) applied Taguchi's approach along with Artificial Nerual Network (ANN) to estimate teeth wear of band saw blade. In this study, they considered speed, feed, cutting length and material hardness as input parameters and investigated the effect of these parameters on blade wear.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Method for Cutting Parameter Optimization for Band Saw Machine

et al. 2019

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In the rough machining stage, band saw machines are widely used to cut various raw materials into the required dimensions. The replacement of blade due to the blade degradation accounts for a large part of the total cost of band saw machine users. Therefore, mitigating the blade degradation by dynamically optimizing the cutting parameters can produce great economic benefits and is also a good exploration for smart manufacturing. To achieve this goal, the Convolutional Neural Network (CNN) model is proposed to map the complicated relationship between the cutting parameters and blade degradation. Then a simulation technique is used to search the optimal cutting parameters based on the blade degradation estimation from the model output, which would help alleviate the blade degradation. The proposed optimization method is validated on the data collected during the real manufacturing process of band saw machines. The comparative results demonstrate that the obtained optimal cutting parameters can effectively extend the service life of the blade for a band saw machine.

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Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments

Cited by 25 publications

References 29 publications

Stability diagrams and chatter avoidance in horizontal band sawing

Stability diagrams and chatter avoidance in horizontal band sawing

An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling

A Deep Learning-Based Method for Cutting Parameter Optimization for Band Saw Machine

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