Intelligent monitoring and decision control system for peripheral milling process

Vallejo, Antonio J.; Alique, José R.

doi:10.1109/icsmc.2008.4811519

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…One of the most critical factors in metal-cutting processes is the cutting tool that performs the removal of material by a relative motion between its cutting edges and the raw material. Several authors have investigated this field [6][7][8][9][10]. Companies usually have to face the problem of cutting tool selection among the great variety of characteristics, coatings, geometries, costs and performance of cutting tools that are available in the market.…”

Section: Introductionmentioning

confidence: 99%

Cutting Tool Selection through Tool Wear, Cost, Power Consumption and Surface Roughness Analyses

Quintana

García-Romeu

Ciurana

2012

AMR

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The current requirements for an efficient dimensional inspection of manufactured parts have lead to development of different in process and on-machine measurement (OMM) techniques. Touch trigger probes (TTP) are the most common technologies utilized, inspired on contact probes used on coordinate measuring machines (CMMs). The on-machine accuracy of TTPs depends upon precision of the tool-machine control as well as upon the procedure for TTP presetting. Taking this into account, a different OMM technique is considered in this work, which consists on a laser micrometer (LM) that is commonly used for in-process measurement of continuous products. The behaviour of TTP and LM is analysed and discussed in terms of repeatability and reproducibility. Results obtained by both techniques are compared each other by measuring a cylindrical workpiece and by checking the results with those obtained on a CMM.

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Section: Introductionmentioning

confidence: 99%

Cutting Tool Selection through Tool Wear, Cost, Power Consumption and Surface Roughness Analyses

Quintana

García-Romeu

Ciurana

2012

AMR

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“…This will result that if there is enough training data, HMM can capture any high order relations [Bilmes, 2006, Fish et al, 2003. In this way, HMM can be viewed as a generator and acceptor in the Language theory [Bilmes, 2006, Doostfatemeh and , 1991, ,Vallejo et al, 2008. Originally, it seemed that HMM is not as accurate as other models for a nonlinear system.…”

Section: Pros and Consmentioning

confidence: 99%

“…Therefore, the accuracy of the output of predictions and detections, [Ertunc and Oysu, 2004, Wang et al, 2002, Vallejo et al, 2008, Atlas et al, 2000,Ertunc et al, 2001,Heck and McClellan, 1991,Owsley et al, 1997a, Jr. et al, 2005] might be unnecessarily affected.…”

Section: Wellmentioning

confidence: 99%

Computational intelligence for predictive condition monitoring and approaches for online analysis

Jahromi¹

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modeling technique for this process regarding its goals, conditions, and specifications. After comprehensive study of the available methods for modeling HSM processes, to build up a proper condition monitoring system, sensor signals are to be utilized to form a reference model which non-intrusively reflects the performance of the system. Therefore, a desired reference model has to apply more efficient feature extraction and artificial intelligence (AI) techniques to be more repeatable and generalizable. Since milling signals are complex, a time-frequency analysis method, namely wavelet, is applied for feature extraction. Considering the high dimension of the wavelet features, clustering methods are used for dimension reduction and also as an interpretation layer between the signal feature extraction subsystem and artificial intelligence blocks. This research illustrates the performance of artificial intelligence based techniques for modeling of high speed end milling experimental data. Studied and developed methods are applied on wavelet features of force and vibration signals to illustrate the repeatability and accuracy of their results. It is shown that the proposed structure as well as the developed artificial intelligent method can present the status of the process and can be applied for fault diagnosis and TCM purposes. It is also discussed that how application of available data mining methods with a proper structure may improve the performance of existing reference models towards more efficient utilization of available experimental data and easily generalizable reference models.

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Design the Adaptive Controller for Delta Robots with the Decoupling-Current Servo Motor

Huang

Hsu

2015

Intelligent Robotics and Applications

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Intelligent monitoring and decision control system for peripheral milling process

Cited by 6 publications

References 7 publications

Cutting Tool Selection through Tool Wear, Cost, Power Consumption and Surface Roughness Analyses

Cutting Tool Selection through Tool Wear, Cost, Power Consumption and Surface Roughness Analyses

Computational intelligence for predictive condition monitoring and approaches for online analysis

Design the Adaptive Controller for Delta Robots with the Decoupling-Current Servo Motor

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