Continuously Variable Posture Selection in Robotic Milling for Increased Chatter Stability

The demand for the usage of industrial robots for milling applications has surged owing to their superiority in terms of the large working envelope, reconfigurability, and low capital investment. Albeit such advantages, utilization of industrial robots for milling applications is yet to be a wonderland, where there are major challenges such as low tool path contouring accuracy, less static and dynamic rigidity. The former may be bearable for milling operations requiring less accuracy, such as roughing cycles. However, lowered dynamic rigidity causes decreased chatter stability, which is a roadblock towards effective robotic milling applications as a result of high vibration marks, bad surface quality, tool breakage and damage to the entire system. The position and orientation of the robots have a significant impact on milling stability. Therefore, identification of improved stable conditions is important to achieve increased productivity and process quality. In this thesis, dynamic modeling of the robots is studied to predict the variation in the robot dynamics with robot posture. Simulation results are compared to experimental modal analysis results and possible error sources are discussed. Milling dynamics and stability analysis are further extended to propose an alternative approach to increase chatter stability limits by benefiting the redundant axis of the 6-axis industrial robot. Different configurations of the robot based on the utilization of the redundant axis result in different stability limits by maintaining the same position of the tool. Preferable configuration sequences are generated for the improved cutting conditions through stability simulations based on measured frequency response functions of the tooltip. A proper robot programming scheme is also proposed in order to enable industrial application of the proposed methodology. Furthermore, the advantages of the proposed approach are discussed in accordance with the simulation result

Model based analysis of kinematic redundancy for increased chatter stability in robotic milling

Robotic milling is exposed to varying dynamic response at the tool tip due to varying serial kinematics and related dynamic properties of the robot. Therefore, stable milling conditions vary throughout the workspace based on the feed direction, as well. In this study, it is shown that kinematic redundancy of 6-axis serial arm robots can be used to reach improved stable cutting conditions and to eliminate the feed and position dependent on stability limits. The discussions are provided through simulations

Effect of quasi-static motion on the dynamics and stability of robotic milling

Robotic milling exhibits low frequency chatter, which is highly affected by the robot configuration and milling position. There has been significant effort to investigate the effect of robot structures on milling stability, most of which rely on the modal parameters which are measured under static conditions, i.e. robot is not moving. This study shows that the vibration response of industrial robots under quasi-static motion conditions differs from that of static conditions, which in return affects the stability limits at low frequency chatter conditions. Conclusions are derived from the experimental results to lead the requirement of on-the-fly identification of modal parameters

Effect of quasi-static motion on the dynamics and stability of robotic milling

Robotic milling exhibits low frequency chatter, which is highly affected by the robot configuration and milling position. There has been significant effort to investigate the effect of robot structures on milling stability, most of which rely on the modal parameters which are measured under static conditions, i.e. robot is not moving. This study shows that the vibration response of industrial robots under quasi-static motion conditions differs from that of static conditions, which in return affects the stability limits at low frequency chatter conditions. Conclusions are derived from the experimental results to lead the requirement of on-the-fly identification of modal parameters

Geriatrik Hastalar için Hemşire Gözlem Ölçeği’nin (GHHGÖ) Türkçe formunun psikometrik özellikleri

Objective: the aim of this study was to examine the psychometric features of the Turkish version of the Nurses' Observation Scale for Geriatric Patients (NOSGER), to expose its factor structure and to evaluate its validity and reliability. Methods: the study was carried out among 273 residents (180 women, 93 men; aged 60-96) in a nursing home in Izmir. in the study, the NOSGER, Geriatric Depression Scale (GDS) and the Standardized Mini-Mental Test (SMMT) were used as data collection tools. Internal consistency, item-total score correlations and reliability correlation between the interviewers were calculated to assess the reliability of the scale. Its validity was examined by correlation between the scales. Results: Reliability coefficient between the interviewers of the scale, which had a high internal consistency (0.89), was found to be 0.90. A strong relationship was observed between the NOSGER memory subscale and the SMMT. in factor analysis of the scale, two factors were acquired and it was confirmed that those two factors were related to SMMT and GDS. Conclusion: IIt was determined that the Turkish version of the NOSGER was a valid and reliable tool that could be used in the area.Amaç: Bu çalışmanın amacı, Geriatrik Hastalar İçin Hemşire Gözlem Ölçeği’nin (GHHGÖ) Türkçe formunun psikometrik özelliklerini incelemek, faktör yapısını ortaya koymak ve geçerlik ve güvenirliğini sınamaktır. Yöntemler: Çalışma, İzmir ilinde bir huzurevinde 60-96 yaşları arasındaki, 273 (180 kadın ve 93 erkek) sakin ile yürütülmüştür. Çalışmada veri toplama aracı olarak,Geriatrik Hastalar İçin Hemşire Gözlem Ölçeği (GHHGÖ), Geriatrik Depresyon Ölçeği (GDÖ) ve Standardize Mini Mental Test (SMMT) kullanılmıştır. Ölçeğin güvenirliği için iç tutarlılık, madde-toplam puan korelasyonları ve görüşmeciler arası güvenirlik katsayısı hesaplanmıştır. Geçerliği ölçekler arası korelasyon ile değerlendirilmiştir. Bulgular: Yüksek bir iç tutarlılığa (0.89) sahip olan ölçeğin, görüşmeciler arası güvenirlik katsayısı 0.90 bulunmuştur. GHHGÖ bellek alt ölçeği ile SMMT arasında kuvvetli bir ilişki bulunmuştur. Yapılan faktör analizi sonucunda iki faktör elde edilmiş ve bu iki faktörün SMMT ve GDÖ ile ilişkili olduğu saptanmıştır. Sonuç: GHHGÖ’nin Türkçe formunun alanda kullanılabilecek, geçerli ve güvenilir bir araç olduğu belirlenmiştir

Development of a vision based pose estimation system for robotic machining and improving its accuracy using LSTM neural networks and sparse regression

In this work, an eye to hand camera based pose estimation system is developed for robotic machining and the accuracy of the estimated pose is improved using two different approaches, namely Long Short Term Memory (LSTM) neural networks and sparse regression. To improve the accuracy obtained from the Levenberg–Marquardt (LM) based pose estimation algorithm, two distinct supervised data driven approaches are proposed which can take the dynamics into account during robotic machining through utilization of the torque information available from the sensors at each joint. The first one is a LSTM neural network and the second one is a method based on sparse regression. The proposed methods are validated by an experimental study performed using a KUKA KR240 R2900 ultra robot while machining a NAS 979 part, during which the orientation of the cutting tool was fixed, and free form milling, during which the orientation of the cutting tool continuously changed. A target object to be tracked by the camera was designed with fiducial markers to guarantee trackability with ±90°in all directions. The design of these fiducial markers guarantee the detection of at least two distinct non-parallel markers from any view, thus preventing pose estimation ambiguities. Moreover, in order to reduce the errors due to the construction of the camera target and placement of the markers on it, this work proposes a method for optimizing the positions of the corners of the fiducial markers in the object frame using a laser tracker. The proposed methods were compared with an Extended Kalman Filter (EKF) and the experimental results show that both of the proposed approaches significantly improve the pose estimation accuracy and precision of the vision based system during robotic machining while proving much more effective than the EKF approach. The attainable absolute position errors were 5.47 mm, 2.9 mm and 2.05 mm on average for NAS 979 machining and 5.35 mm, 2.17 mm and 0.86 mm on average for free form machining when using the EKF, the proposed LSTM network and the proposed sparse regression approaches, respectively. Moreover, the proposed sparse regression based method provides parsimonious models and better results when compared with the proposed LSTM based approach