• IE interfaces
• /
• v.7 no.3
• /
• pp.155-168
• /
• 1994

In computer integrated manufacturing environment, tool management plays an important role in controlling tool performance for machining operations. Knowledge of tool behavior during the cutting process and effective tool-behavior prediction contribute to controlling machine costs by avioding production delays and off-target parts due to tool failure. The purpose of this paper is to review and develop the tool condition monitoring scheme for drilling operation to assure a fast corrective response to minimize the damage if tool failures occur. If one desires to maximize system through-put and product quality as well as tooling resources, within an economic environment, real-time tool sensing system and information processing system can be coupled to provide the necessary information for the effective tool management. The example is demonstrated as to drilling operation when the aluminum composites are drilled with carbide-tipped HSS drill bits. The example above is limited to the situation that the tool failure mode of drill bits is wear.

• Journal of the Korean Society for Precision Engineering
• /
• v.23 no.1 s.178
• /
• pp.64-71
• /
• 2006

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmill is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for condition monitoring is presented. Also, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by the fuzzy C-means algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.4
• /
• pp.161-167
• /
• 2020

In the process of cutting large aircraft parts, the tool may be abnormally worn or damaged due to various factors such as mechanical vibration, disturbances such as chips, and physical properties of the workpiece, which may result in deterioration of the surface quality of the workpiece. Because workpieces used for large aircrafts parts are expensive and require strict processing quality, a maintenance plan is required to minimize the deterioration of the workpiece quality that can be caused by unexpected abnormalities of the tool and take maintenance measures at an earlier stage that does not adversely affect the machining. In this paper, we propose a method to indirectly monitor the tool condition that can affect the machining quality of large aircraft parts through real-time monitoring of the current signal applied to the spindle motor during machining by comparing whether the monitored current shows an abnormal pattern during actual machining by using this as a reference pattern. First, 30 types of tools are used for machining large aircraft parts, and three tools with relatively frequent breakages among these tools were selected as monitoring targets by reflecting the opinions of processing experts in the field. Second, when creating the CNC machining program, the M code, which is a CNC auxiliary function, is inserted at the starting and ending positions of the tool to be monitored using the editing tool, so that monitoring start and end times can be notified. Third, the monitoring program was run with the M code signal notified from the CNC controller by using the DAQ (Data Acquisition) device, and the machine learning algorithms for detecting abnormality of the current signal received in real time could be used to determine whether there was an abnormality. Fourth, through the implementation of the prototype system, the feasibility of the method proposed in this paper was shown and verified through an actual example.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.25 no.6
• /
• pp.445-451
• /
• 2016

The Inconel 718 alloy is a well-known super-heat-resistant alloy and a difficult-to-cut material. Inconel 718 with excellent corrosion and heat resistance is used in harsh environments. However, the heat generated is not released owing to excellent physical properties, making processes (e.g., adhesion and thermal fatigue) difficult. Tool condition monitoring in machining is significant in reducing manufacturing costs. The cutting tool is easily broken and worn because of the material properties of Inconel 718. Therefore, tool management is required to improve tool life and machinability. This study proposes a method of predicting the tool wear with non-contacting sensors (e.g., IR thermometer for measuring the cutting temperature and a microphone for measuring the sound pressure level in machining). The cutting temperature and sound pressure fluctuation according to the tool condition and cutting force are analyzed using experimental data. This experiment verifies the effectiveness of the non-contact measurement signals in tool condition monitoring.

• Steel and Composite Structures
• /
• v.21 no.3
• /
• pp.573-588
• /
• 2016

Carbon Fiber Reinforced Plastic (CFRP) and Titanium Alloy (Ti6Al4V) stack, extensively used in aerospace structural components are assembled by fasteners and the holes are made using drilling process. Drilling of stack in one shot is a complicated process due to dissimilarity in the material properties. It is vital to have optimal machining condition and tool geometry for better hole quality and tool life. In this study the tool wear and hole quality were analysed by experimental analysis using three modified twist drills and online tool condition monitoring using Acoustics Emission (AE) sensor. Helix angle and point angle influence tool performance and cutting force. It was found that a tool geometry (TG1) with high helix angle of $35^{\circ}$ with low point angle $130^{\circ}$ results in reduction in thrust force of 150-500 N range but the TG2 also perform almost similar to TG1, but when compared with the AErms voltage generated during drilling it was found that progressive rise in voltage in TG1 is less with respect to TG2 which can be attributed to tool life. In process wear monitoring was done using crest factor as monitoring index. AErms voltage were measured and correlated with the performance of the drills.

• Proceedings of the KIEE Conference
• /
• 2008.04b
• /
• pp.96-99
• /
• 2008

On-line condition monitoring is a useful tool for maintaining and extending the longevity of power transformers. An intelligent diagnostic system is desirable for operational safety and reliability. Bringing these concepts together results in a powerful support tool for engineers, reducing the volume of data to deal with, and making the data more meaningful. This paper describes how a multi-agent system for diagnosing the cause of transformer partial discharge activity was coupled with a method of UHF partial discharge monitoring, creating an on-line condition monitoring system. The challenges presented by the on-site environment are discussed, along with the implications for the complete system.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.2006-2007
• /
• 2007

On-line condition monitoring is a useful tool for maintaining and extending the longevity of power transformers. An intelligent diagnostic system is desirable for operational safety and reliability. Bringing these concepts together results in a powerful support tool for engineers, reducing the volume of data to deal with, and making the data more meaningful. This paper describes how a multi-agent system for diagnosing the cause of transformer partial discharge activity was coupled with a method of UHF partial discharge monitoring, creating an on-line condition monitoring system. The challenges presented by the on-site environment are discussed, along with the implications for the complete system.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.1
• /
• pp.11-18
• /
• 2010

Tool condition monitoring (TCM) is crucial for improvement of productivity in manufacturing process. However, TCM techniques have not been applied to monitor tool failure in an industrial gear shaving application. Therefore, this work studied a statistical TCM method for monitoring gear shaving tool condition. The method modeled the vibration signal of the shaving process using beta probability distribution in order to extract the effective features for TCM. Modeling includes rectifying for converting a bi-modal distribution into a unimodal distribution, estimating the parameters of beta probability distribution based on method of moments. The performance of features obtained from the proposed method was evaluated and discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2010.05a
• /
• pp.741-742
• /
• 2010

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.7 no.3
• /
• pp.14-21
• /
• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.