Cutting force, Vibration, and Temperature in Drilling on a Thermoplastic Material of PEEK

2021 ◽  
pp. 089270572110523
Author(s):  
Dar-Yuan Chang ◽  
Chien-Hung Lin ◽  
Xing-Yao Wu ◽  
Chun-Chun Yang ◽  
Shan-Chao Chou
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Spindle Speed ◽  
Thermoplastic Material ◽  
Low Level ◽  
Multiple Sensors ◽  
Acceleration Amplitude ◽  
The Time Domain ◽  
High Level ◽  
Machining Characteristics

Polyetheretherketone (PEEK) is one of the semi-crystalline thermoplastic polymers with excellent machinability and chemical stability applied to precise structural plates and electronic components. This study installed multiple sensors to analyze the machining characteristics in the PEEK drilling. According to the time domain signals, the effects of spindle speed and feed rate on the machining characteristics of cutting force and vibration were investigated. In addition, an infrared thermography was installed to record the temperature variation within the drilling area. The experimental hole was 2-mm diameter with a 4.5-mm depth. Experimental results showed that the effect of the feed rate on thrust force is greater than the spindle speed; drilling by a low-level spindle speed with a low-level feed rate can obtain the smallest cutting force and acceleration amplitude in the spindle axis; the temperature within the drilling area is inverse to the feed rate and a high-level feed rate is helpful for forming regular curl chips. When adequate airflow was applied during the drilling operation, the hole’s shrinkage ratio and roundness can be decreased. The data presented in this paper provide valuable references for realizing the drilling of the thermoplastic—PEEK.

Characterizing the Effect of Cutting Condition, Tool Path, and Heat Treatment on Cutting Forces of Selective Laser Melting Spherical Component in Five-Axis Milling

2018 ◽  
Vol 140 (5) ◽  
Author(s):  
Amir Mahyar Khorasani ◽  
Ian Gibson ◽  
Moshe Goldberg ◽  
Guy Littlefair
Keyword(s):  
Heat Treatment ◽  
Production Process ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Forces ◽  
Tool Path ◽  
Annealing Temperature ◽  
Spindle Speed ◽  
Cutting Condition ◽  
Scallop Height

Additive manufacturing (AM), partly due to its compatibility with computer-aided design (CAD) and fabrication of intricate shapes, is an emerging production process. Postprocessing, such as machining, is particularly necessary for metal AM due to the lack of surface quality for as-built parts being a problem when using as a production process. In this paper, a predictive model for cutting forces has been developed by using artificial neural networks (ANNs). The effect of tool path and cutting condition, including cutting speed, feed rate, machining allowance, and scallop height, on the generated force during machining of spherical components such as prosthetic acetabular shell was investigated. Also, different annealing processes like stress relieving, mill annealing and β annealing have been carried out on the samples to better understand the effect of brittleness, strength, and hardness on machining. The results of this study showed that ANN can accurately apply to model cutting force when using ball nose cutters. Scallop height has the highest impact on cutting forces followed by spindle speed, finishing allowance, heat treatment/annealing temperature, tool path, and feed rate. The results illustrate that using linear tool path and increasing annealing temperature can result in lower cutting force. Higher cutting force was observed with greater scallop height and feed rate while for higher finishing allowance, cutting forces decreased. For spindle speed, the trend of cutting force was increasing up to a critical point and then decreasing due to thermal softening.

scholarly journals A Study on End-milling of die/mold with Constant Cutting Force by Feed Rate and Spindle Speed Control

2007 ◽  
Vol 73 (3) ◽  
pp. 367-371 ◽  
Author(s):  
Susumu NISHIDA ◽  
Yoshiaki KAKINO ◽  
Kazuya FUJIMOTO ◽  
Heisaburo NAKAGAWA
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
End Milling ◽  
Speed Control ◽  
Spindle Speed ◽  
Constant Cutting Force

Experimental Study of Cutting Performance for Inconel 718 Milling by Various Assisted Machining Techniques

2019 ◽  
Vol 294 ◽  
pp. 129-134
Author(s):  
Shen Yung Lin ◽  
Bing Hsueh Yang
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Inconel 718 ◽  
Cutting Tool ◽  
Process Parameter ◽  
Spindle Speed ◽  
Cutting Performance ◽  
Subsequent Stage ◽  
Cutting Depth ◽  
Tool Service Life

The five stage experiments including without assistance, single and hybrid assisted machining systems on Inconel 718 milling were conducted in this study. First of all, the milling experiment without assistance was performed to investigate the variations of cutting performance and the results were used for a suitable process parameter planning in the subsequent stage experiments. Next, a laser assisted system was introduced in the second stage where the spacing distance between the laser spot and cutting-tool along the cutting direction was modified to test whether laser preheating may effectively reduce the cutting force. A biaxial ultrasonically assisted system with only one-axis oscillation (x or y direction) and with two-axis simultaneous oscillations (x and y directions) were subsequently introduced at the third to fourth stage experiments, respectively. While a biaxial ultrasonically and the laser assisted systems were integrated together to construct a hybrid assisted cutting system at the last stage experiment. Under these assistances, milling experiments of Inconel 718 by cutting-tool of tungsten carbide with nanoSi® coating were conducted. And the full-factorial experiments of process parameter combinations such as spindle speed, radial cutting depth and feed rate were planned. The results indicated that the laser-preheating assisted system could effectively reduce the cutting force as well as enhance the cutting performance. The effect of the biaxial ultrasonic oscillation on tool service life could greatly be promoted. Furthermore, the cutting performance exhibited in the integrated hybrid assisted milling prevails over that in milling without assistance as well as with each single assisted system. Under this hybrid assisted milling, the better surface roughness of 0.216μm was obtained under a combination of spindle speed of 6000 rpm, radial cutting depth of 0.01 mm, and feed rate of 300mm/min, accompanied by a maximum cutting-tool wear of 13.849μm.

Experimental Study on Thermal and Force Characteristics in the Dry Slotting of Cortical Bone

2016 ◽  
Vol 1136 ◽  
pp. 233-238
Author(s):  
Liang Wen ◽  
Zi Han Zhao ◽  
Jin Bang Song ◽  
De Dong Yu ◽  
Ming Chen ◽  
...  
Keyword(s):  
Cutting Force ◽  
Cortical Bone ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Temperature ◽  
Growth Direction ◽  
Milling Process ◽  
Spindle Speed ◽  
Milling Force ◽  
Milling Temperature

Cutting force and temperature are the two chief factors affecting bone rehabilitation during bone cutting in many orthopedic surgeries. To reveal new knowledge of thermal and force when milling cortical bone, slotting experiments were carried on high-speed milling platform. Cutting force and temperature were measured during the milling process. The effects of cutting inputs on cutting thermal and force were researched in detail. The results showed that: feed rate and spindle speed had a great impact on the milling temperature, while the milling force was mainly influenced by spindle speed. A feed rate of 1.0-1.4 mm/s is recommended to obtain preferable milling force and temperature, and a larger feed rate of 1.2-1.4 mm/s is advised to use with a lower spindle speed (8000-20000 r/min), while a smaller feed rate of 1.0-1.2 mm/s should be chosen when spindle speed was between 20000-40000 r/min. Feeding parallel to the growth direction of the cortical bone can significantly reduce the milling temperature, but there was no obvious change in milling force. The lowest cutting temperature obtained during the experiment was around 50 °C without coolant, which was acceptable for orthopedic surgeries.

scholarly journals A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

2020 ◽  
Vol 6 (8) ◽  
pp. 78
Author(s):  
Dario Cazzato ◽  
Claudio Cimarelli ◽  
Jose Luis Sanchez-Lopez ◽  
Holger Voos ◽  
Marco Leo
Keyword(s):  
Object Detection ◽  
Unmanned Aerial Vehicles ◽  
Semantic Knowledge ◽  
Low Level ◽  
Multiple Sensors ◽  
Aerial Vehicles ◽  
Trade Offs ◽  
Methodological Approaches ◽  
High Level ◽  
Technological Constraints

The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.

Investigation of Cutting Performance for NAK80 Hardened Steel under Different Assisted Machining Systems

2020 ◽  
Vol 976 ◽  
pp. 133-138
Author(s):  
Shen Yung Lin ◽  
Z.K. Wang
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Tool ◽  
Flank Wear ◽  
Spindle Speed ◽  
Cutting Performance ◽  
The Other ◽  
Cutting Depth ◽  
Cold Air

In this study, five different assisted techniques, including without assistance, laser-assisted, flood assisted, MQL-assisted and cold air-assisted were employed for NAK80 mold steel milling. The 18 set experiments were performed in each assisted system but 27 sets were executed in the without assistance situation. Under each assisted milling circumstance, the effects of process parameters on the variations of surface roughness, cutting force, tool wear and surface morphology of the workpiece are thus investigated. Milling experiments of NAK80 mold steel by cutting-tool of extra-fine particle tungsten carbide with TiSiN coating were conducted. And the experiments of the process parameter combinations such as spindle speed, feed rate and radial depth of cut were also planned. The results show that, under the same milling conditions, the surface roughness and cutting force are decreased as the spindle speed is increased, but both of them is increased as the feed rate and radial cutting depth are increased. In addition, the maximum flank wear of the cutting-tool is increased as the spindle speed, feed rate and radial cutting depth are increased. In terms of cutting assistance, the cutting performance exhibited in the cold air-assisted milling prevails over that in milling without assistance as well as with the other each assisted system. As a result, the cutting-tool flank wear, surface roughness and cutting force are all better than the other assisted techniques.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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