Modelling of process parameters in laser polishing of steel components using ensembles of regression trees

Bustillo, Andrés; Ukar, Eneko; Rodrı́guez, Juan J.; Lamíkiz, Aitzol

doi:10.1080/0951192x.2011.574155

Cited by 25 publications

(13 citation statements)

References 37 publications

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“…Some complex manufacturing problems like laser polishing (Bustillo et al 2011b), maintenance planning of five-axis milling (Freiburg et al 2014), laser micromachining of cavities (Teixidor et al 2015), and deep drilling (Bustillo et al 2016) have been successfully modeled using regression trees. In all these cases, the decision trees create models that are as accurate as other standard machine-learning techniques, such as ANNs, and are suitable for both continuous and discrete outputs.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N , in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power-that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements. Keywords

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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“…The conclusion that came after our survey was that most of the existing research on prediction models for machining processes used different types of correlation plots to show the relationship between inputs and outputs in such processes [7,9,10,14,26,30,35,37,38,43,51,53,54,57,59,60]. Only very few works used other techniques rather than correlation plots, such as scatter plots [37] and scree plots [25].…”

Section: Survey Of Visualization Techniques Applied To Machining Procmentioning

confidence: 97%

“…This kind of representation needs an extensive experimental test of different cutting conditions to generate a homogenous 3D surface, which is a very expensive solution. The second option is to show how a process engineer can find the best combination of two process inputs for a desired value of an output, because the 3D surface is generated by a prediction model and not by real experiments [7,10,26,51,59,60]. An example is shown in Fig.…”

Section: Survey Of Visualization Techniques Applied To Machining Procmentioning

confidence: 99%

Interpreting tree-based prediction models and their data in machining processes

Bustillo

Grzenda

Macukow

2016

ICA

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Machine-learning techniques frequently predict the results of machining processes, based on predetermined cutting tool settings. By doing so, key parameters of a machined product can be predicted before production begins. Nevertheless, a prediction model cannot capture all the features of interest under real-life industrial conditions. Moreover, careful assessment of prediction credibility is necessary for accurate calibration; aspects that should be addressed through appropriate modeling and visualization techniques. A machine process test problem is proposed to analyze data-visualization techniques, in which a real data set is analyzed that describes deep-drilling under different cutting and cooling conditions. The main objective is the efficient fusion of visualization techniques with the knowledge of industrial engineers. Common modeling and visualization techniques were first surveyed, to contrast standard practice with our novel approach. A hybrid technique combining conditional inference trees with dimensionality reduction was then examined. The results show that a process engineer will be able to estimate overall model accuracy and to verify the extent to which accuracy depends on industrial process settings and the statistical significance of model predictions. Moreover, evaluation of the data set in terms of its sufficiency for modeling purposes will help assess the credibility of these decisions.

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“…Usually, these objectives were incompatible and a balance had to be struck, depending on the type of metallic component and its industrial use; many examples of this dilemma may be found in the bibliography (Bustillo, Ukar, et al 2011;Bustillo and Correa 2012;Quintana, Bustillo, and Ciurana 2012). Some examples of these types of metallic components are cast iron moulds and dies for the automobile industry, welded steel main frames of wind turbines for the wind-power industry and aluminium ribs for the aeronautical industry.…”

Section: Introductionmentioning

confidence: 98%

New methodology for the design of ultra-light structural components for machine tools

Bustillo¹,

Oleaga

Zulaika

et al. 2014

International Journal of Computer Integrated Manufacturing

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Energy consumption is the key to the ecological impact of many machine tools, especially milling machines. One promising strategy for minimising the energy consumption of machine tools is to reduce the mass of their structural components. This solution, however, has a clear drawback: the mechanical stiffness of the machine is reduced, impairing its performance and, in the long run, its productivity. This study proposes a new methodology to overcome such limitations, which involves the design of machine tools with ultra-light structural components, and the development of strategies to counteract the loss of productivity as a consequence of lightweight machines. The new methodology includes the use of modular boxes built with carbon-fibre trusses, calculation of the dynamic stiffness of the new design, the identification of its weaknesses in terms of its cutting processes, and the design and integration of active damping systems in the machine to soften the expected vibrations under the most critical cutting conditions. This methodology has been tested in the new design of a ram of a bridge-type machining centre of medium size. The results show that a 60% reduction in mass can be achieved and that an active damper system can compensate a 60% reduction in mechanical stiffness, maintaining a level of performance that is comparable to heavier standard machines under high-cutting conditions.

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Modelling of process parameters in laser polishing of steel components using ensembles of regression trees

Cited by 25 publications

References 37 publications

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Interpreting tree-based prediction models and their data in machining processes

New methodology for the design of ultra-light structural components for machine tools

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