Jung Soo Nam

A study on tool condition monitoring and diagnosis of micro-grinding process based on feature extraction from force data

In this article, a new algorithm for diagnosing tool conditions in micro-scale grinding process is proposed using features extracted from measured tangential grinding force data with the aid of wavelet packet decomposition and back-propagation neural network methods. The tangential grinding forces are measured in a series of micro-grinding experiments by varying depth of cut and feed rate, and those measured profiles are analyzed to define the tool conditions—sharp, middle and dull. From each tangential grinding force signal, 32 node energies are extracted by applying a wavelet packet decomposition method, and a total of 34 features including 32 node energies, depth of cut and feed rate are used to build the micro-grinding tool condition diagnosis model based on a back-propagation neural network approach. In this model, the grinding tool condition can be represented as a numerical confidence value. The experimental verification is conducted and it is demonstrated that the developed model is applicable for effectively diagnosing the micro-grinding tool conditions.

A Parametric Analysis on Micro-Drilling Process With Nanofluid Minimum Quantity Lubrication

This paper presents a parametric analysis on microdrilling process using nanofluid minimum quantity lubrication (MQL). In this paper, the effects of several machining parameters such as a feed rate, rotational speed and drill diameter on micro drilling performances are investigated under various lubrication conditions — compressed air lubrication, pure MQL and nanofluid MQL. For nanofluid MQL, nanodiamond particles are used with the volumetric concentration of 4 %. A series of microdrilling experiments are carried out in the miniaturized machine tool system. The experimental results show the nanofluid MQL can be effective for reducing average drilling torques and thrust forces, in particular, at relatively low feed rate (10 mm/min) and low spindle speed (30,000 RPM) in the case using the drill with small diameter (0.1 mm). Meanwhile, in the case using the drill with large diameter (0.5 mm), the nanofluid MQL may not be effective for reducing average torques and thrust forces.Copyright

Lens injection moulding condition diagnosis and form error analysis using cavity pressure signals based on response surface methodology

In this article, the condition diagnosis and form error prediction models for lens injection moulding process are developed based on a response surface method by using features extracted from in-process cavity pressure signals. In the lens injection moulding experiments, cavity pressure signals are captured by pressure sensors embedded in a lens mould, and form errors of manufactured lenses are measured afterwards. Then, three features such as filling point, maximum pressure and inflection point pressure are identified from the measured cavity pressure profile, and they are used to formulate the response surface functions for each injection moulding condition. In addition, the response surface functions for the lens form error with the input variables of the above-mentioned three features are also formulated. It is reported that the overall average accuracies for the lens injection moulding condition diagnosis and form error estimation are better than 97% and 80%, respectively, in the actual industrial site.

A Development of Feature Extraction and Condition Diagnosis Algorithm for Lens Injection Molding Process

In this paper, a new condition diagnosis algorithm for the lens injection molding process using various features extracted from cavity pressure, nozzle pressure and screw position signals is developed with the aid of probability neural network (PNN) method. A new feature extraction method is developed for identifying five (5), seven (7) and two (2) critical features from cavity pressure, nozzle pressure and screw position signals, respectively. The node energies extracted from cavity and nozzle pressure signals are also considered based on wavelet packet decomposition (WPD). The PNN method is introduced to build the condition diagnosis model by considering the extracted features and node energies. A series of the lens injection molding experiments are conducted to validate the model, and it is demonstrated that the proposed condition diagnosis model is useful with high diagnosis accuracy.

Injection-moulded lens form error prediction using cavity pressure and temperature signals based on k-fold cross validation:

This article discusses the development of lens form error prediction models using in-process cavity pressure and temperature signals based on a k-fold cross-validation method. In a series of lens injection moulding experiments, the built-in-sensor mould is used, the in-process cavity pressure and temperature signals are captured and the lens form errors are measured. Then, three features including maximum pressure, holding pressure and maximum temperature are identified from the measured cavity pressure and temperature profiles, and the lens form error prediction models are formulated based on a response surface methodology. In particular, the k-fold cross-validation approach is adopted in order to improve the prediction accuracy. It is demonstrated that the lens form error prediction models can be practically used for diagnosing the quality of injection-moulded lenses in an industrial site.

Experimental Study on Micro-Grinding Process of Titanium Alloy Using Electro-Hydro-Dynamic Spray With Nanofluid and Compressed Air

In this paper, the micro-scale grinding processes of titanium alloy (Ti-6Al-4V) using electro-hydro-dynamic (EHD) spray with nanofluid and compressed air are experimentally investigated. In the experiments, specific micro-grinding forces and surface roughness of the ground workpiece are quantitatively analyzed as a function of nanofluid’s concentration and size of nanoparticles. In addition, the ground surface quality is qualitatively investigated by comparing the optical microscopic images. The experimental results show the effectiveness of EHD spraying with nanofluid and compressed air for reducing the specific micro-grinding forces and enhancing ground surface quality.© 2015 ASME

A Study on Machining and Environmental Characteristics of Micro-Drilling Process Using Nanofluid Minimum Quantity Lubrication

This paper presents two basic experimental studies of a micro-drilling process with nanofluid minimum quantity lubrication (MQL) in terms of machining and environmental characteristics. By using a miniaturized desktop machine tool system, a series of micro drilling experiments were conducted in the cases of dry, compressed air and nanofluid MQL. The experimental results imply that nanofluid MQL significantly reduces the adhesion of chips when compared with the cases of dry and compressed air micro-drilling. As a result, it is observed that the magnitudes of average drilling torque and thrust force are decreased and the tool life of micro drills is extended in the case of nanofluid MQL micro-drilling process. In addition, the empirical study on environmental characteristics of MQL micro-drilling process is conducted by measuring MQL oil mist with the oil sampling method. The results show that remaining MQL oil mist is tiny enough not to have a detrimental effect on human health.Copyright

Development of Real-Time Condition Diagnosis System Using LabVIEW for Lens Injection Molding Process

1 성균관대학교 대학원 기계공학과 (Department of Mechanical Engineering, Graduate School, Sungkyunkwan University)2 한국기계연구원 초정밀기계시스템연구실 (Department of Ultra Precision Machines and Systems, Korea Institute of Machinery and Materials)3 서울과학기술대학교 기계·자동차공학과 (Department of Mechanical and Automotive Engineering,Seoul National University of Science and Technology)4 성균관대학교 기계공학부 (School of Mechanical Engineering, Sungkyunkwan University) Corresponding author: sangwonl@skku.edu, Tel: +82-31-290-7467

Experimental Study on the Size Effect and Formability of Sheet Materials in Microscale Deep Drawing Process

This study investigates the effects of the size of copper sheets on the plastic deformation behavior in a microscale deep drawing process. Tensile tests are conducted on the copper sheets to study the flow stress of the materials with different grain sizes before carrying out the microscale deep drawing experiments. After the tensile tests, a novel desktop-sized microscale deep drawing system is used to perform the microscale deep drawing process. A series of microscale deep drawing experiments are subsequently performed, and the experimental results indicate that an increase in the grain size results in the reduction of the deformation load of the copper sheets due to the effects of the surface grain. The results also show that the blank holder gap improves both the formability of copper sheets and the material flow.

A Study on Condition Monitoring and Diagnosis of Injection Molding Process Using Probabilistic Neural Network Method

The objective of this research is the development of condition diagnosis model for injection molding process based on wavelet packet decomposition (WPD), feature extraction from cavity pressure, nozzle pressure and screw position signals and probability neural network (PNN) method. The node energies from the WPD of cavity and nozzle pressure signals are identified. In addition, five (5), seven (7) and two (2) critical features are extracted from the cavity pressure, nozzle pressure and screw position signals via the new feature extraction algorithm. The node energies and critical features are input to the PNN based condition diagnosis model for the injection modeling process. A series of injection modeling experiments are conducted and their results are used to validate the model. It is demonstrated that the proposed model is applicable to diagnose the injection molding process conditions. In particular, it is also shown that the utilization of cavity pressure and screw position signals in the model can result in higher diagnosis accuracy from the case studies.Copyright