The discrete element method (DEM) has been widely used as a modelling tool to investigate soil-tool interactions. Soil particle size has an important effect on soil disturbance behaviours and cutting ...forces in discrete element models. In this study, discrete element models were developed using the EDEM software and evaluated using laboratory tests to investigate the effect of varying modelled particle radii (ranging from 3 to 19 mm) on soil-subsoiler interactions in the models. Soil disturbance characteristics (soil rupture distance ratio, height of accumulated soil, soil disturbance area, soil density change rate, and fragmentation index), solution time of the computer, and soil cutting forces (draft and vertical) were measured. Based on the ANOVA outputs, all these variables were significantly affected by the treatments at p < 0.05, except for the height of accumulated soil. Reducing particle radius from 5 mm to 3 mm gave an approximately sevenfold increase in solution time (about 500 h). For the soil used in the tests, 7 mm radii particles are recommended as the best choice for the Hertz-Mindlin with bonding (HMB) model, as indicated by low relative errors between simulated and experimental data of soil disturbance and cutting forces. The particle size should be taken into account in the calibration of the discrete element model to accurately predict both soil cutting forces and data of soil disturbance. The present study provides critical information for the selecting soil particle size in DEM models.
•DEM models using Hertz-Mindlin with bonding and different radii particles developed.•Particle radius exerts significant influence on soil disturbance and cutting forces.•Fragmentation index can be used to describe soil failure in HMB model.•For the given soil, 7 mm radii particles recommended for HMB model.
Drilling is the most common machining operation and arguably the most crucial of the traditional machining processes. Cutting force predictions for every given set of cutting conditions are crucial ...for efficient product development. It has been hypothesized that the force produced during cutting is responsible for the majority of the issues seen in hole creating processes like drilling. Calculating forces accurately also aids in the development and assessment of cutting equipment and fixtures. The purpose of this article is to examine the effect of significant machining factors like thrust force and torque on the drilling operations of Aluminum 6061-T6 alloy. Theory and experiment will be compared with different types of drill bits. An average error of 5.81% was found in the Axial Force (N) and 0.85% was observed in Torque (N-cm). The error % of desired responses is found to be accepted.
Cutting force is the fundamental parameter determining the productivity and quality of the milling operation. The development of a generic cutting force model for end milling operation necessitates a ...large number of experiments. The experimental data contains multiple outliers due to noise and process disturbances lowering prediction accuracy of the model. This paper presents a novel approach combining the mechanistic model and the supervised neural network (NN) model to predict instantaneous cutting force variation during the end milling operation. The approach proposes training of an NN model using datasets generated from the mechanistic force model instead of using experimental data. The methodology generates a large number of datasets for the training of an NN model without conducting rigorous experimentation. A set of NN architectures were developed, and an appropriate network was derived by comparing performance parameters. A series of end milling experiments were conducted to examine the efficacy of the proposed approach in predicting cutting forces over a wide range of cutting conditions.
An application of hard engineering materials depends especially on their specific properties, included mechanical properties and their machinability. Technical ceramics belongs to such materials. ...Nowadays, due to its properties, it is a process of grinding that is applied in machining. Because the technical ceramics has high hardness and brittleness it is important to pay attention to the whole process of machining. In this case of the grinding, there is need to pay attention to the process from disc engagement to grind off the desired layer. The paper deals with an implementation of grinding of ceramic materials in context of determining of elements of cutting forces and the surface roughness evaluation. These are important aspects for determining the suitability of the cutting conditions and the possibility of their use in the production process.
•FEM is used to investigate effects of microbumped tools on machining of mild steel.•Microbumps on the rake face of a cutting tool are effective in reducing cutting forces.•Minimum cutting force may ...be realized around two width values at 20 and 400μm.•It is noted that an optimal edge distance value may exist between 320 and 420μm.•An optimal microbump width to height ratio exists to minimize the cutting force.
This paper investigates the performance of microbump textured cutting tool in dry orthogonal machining of mild steel (AISI 1045 steel) using AdvantEdge finite element simulation. Microbumps are designed on the rake face of cemented carbide (WC/Co) cutting inserts. The purpose is to examine the effect of microbump textured tools on machining performance and to compare it with non-textured regular cutting tools. Specifically, the following microbump parameters are examined: microbump width, microbump height, and edge distance (the distance from cutting edge to the first microbump). Their effects are assessed in terms of the main force, thrust force, and chip–tool contact length. It is found that microbump textured cutting tools generate lower cutting force and thrust force and consequently lower the energy consumption for machining. The micobump width, microbump height, and edge distance all have influence on cutting force in their own ways.
In multi-axis machining of thin-walled workpieces such as a blade, especially at the semi-finishing and finishing stage, the depth-of-cut is in general selected very conservatively in order to ...alleviate the deflection of the workpiece during the machining. Moreover, for both semi-finishing and finishing, the depth-of-cut is usually set to be a constant. Aiming at achieving higher machining efficiency while maintaining a good finish surface quality, in this paper we present a new multi-axis machining strategy for thin-walled workpieces based on the idea of variable depth-of-cut machining. The proposed machining strategy strives to maximize the depth-of-cut locally for every cutter-contact (CC) point while respecting a threshold of the cutting force that is normal to the workpiece surface as it is the major effective force causing the deflection. The threshold of the normal cutting force varies depending on the position of the CC point on the workpiece and it is calibrated through physical experiments. A variable offset distance function is defined by setting the offset at each CC point equal to the maximum depth-of-cut at the point and based on which a multi-pass semi-finishing tool path is generated by offsetting the finishing tool path with the computed variable offset distance. Both computer simulation and physical cutting experiments are performed and their results show that a substantial reduction in both total machining time and machined surface error can be achieved by the proposed machining strategy for thin-walled workpieces.
•A variable depth-of-cut milling strategy for thin-walled workpiece is proposed.•Local maximum depth-of-cut is defined based on the workpiece deflection constraint.•Multi-pass tool path is generated based on the variable depth strategy.•Effectiveness of the proposed method is verified from simulations and experiments.
A conventional straight knife cutterhead and three helical knife cutterheads were tested for planing sugar maple wood (Acer saccharum Marsh.). Effects of helix angle and feed per knife (FK) on ...maximum cutting forces, sound level, and power consumption were evaluated. A 3-axis dynamometer, an array microphone, and a watt transducer were used to simultaneously record the forces, sound level, and power consumption during machining, respectively. Parallel (F
P
), positive and negative normal (F
NP
and F
NN
), lateral (F
L
), resultant (F
R
) forces, and sound level increased as FK increased. Helical tools produced lower F
P
, F
NP
, F
NN,
and F
R
. Parallel forces tended to decrease as helical angle increased. Differences among helical tools were not significant for normal forces. Helical tools produced higher F
L
at medium (2.9 mm) and high (4.7 mm) feeds per knife. F
R
decreased as helix angle increased. The impacts of these cutting forces on the appearance of surface defects and ways to reduce them were discussed. Helical cutterheads considerably generated lower sound pressure level, with a maximum difference of up to 8 dB(A). At low FK (1.3 mm), helical tools required slightly lower cutting power.
Cutting tool characterization plays a crucial role in understanding the behavior of machining operations. The selection of a suitable cutting material, the operating conditions for the work piece, is ...necessary to yield good cutting-tool life. Several pieces of research have been carried out in cutting-tool characteristics for turning operation. Only a few pieces of research have focused on correlating the vibrations and stress with wear characteristics. This research article deals with stress induced in silicon carbide tool inserts and coated tool inserts while machining SS304 steel. Since this material is much less resistant to corrosion and oxidation it is widely used in engineering applications such as cryogenics, the food industry and liquid contact surfaces. Moreover, these materials have much lower magnetic permeability so they are used as nonmagnetic engineering components which are very hard. This article focuses on the machining of SS304 by carbide tool inserts and then, the cutting forces were observed with a tool dynamometer. Using observed cutting forces, the induced stress in the lathe tool insert was determined by FEA investigation. This research also formulates an idea to predict the tool wear due to vibration. Apparently, the worn-out tool vibrates more than new tools. Using the results, the relation between stress, strain and feed rate, depth of cut and speed was found and mathematically modeled using MINI TAB. It was observed that carbide tool inserts with coating withstand better than uncoated tools while machining SS304. The results were anticipated and correlation between the machining parameters furnished the prediction of tool life and obtaining the best machining outcomes by using coated tool inserts.
•The nested-ANN has superior prediction accuracy than the conventional ANN and RSM.•The nested-ANN corrects therandom error generated in the measurement.•The nested-ANN simplifies the number of ...inputs using only three cutting parameters.•The Ra is mostly affected by the feed rate.
This paper demonstrates a nested-ANN (Artificial Neural Network) model predicting surface roughness (Ra). The special ANN includes enclosed-ANNs and an output-ANN. The enclosed-ANN models use cutting parameters as inputs to predict the values of cutting forces and tool vibrations respectively, and then forward all outputs to the output-ANN model. Subsequently,the output-ANN adopts the forward valuesand cutting parameters as inputs to predict Ra. To verify the effectiveness of the nested-ANN model, it is compared with mathematical and statistical models based on conventional ANN and RSM (Response Surface Methodology) using the same experimental data. The results show that the nested-ANN uses less input variables to obtain superior prediction accuracy than other models. Additionally, the statistical analyses show that Ra is mostly affected by the feed rate and has a signification correlation with the feed rate, the cutting force in both radial and tangential directions as well as the tool vibrations.
Thin-walled micro parts are widely used in many fields, such as aviation, aerospace, precision engineering, and even micro-molds. Compared with other micro-fabrication technology, micro-milling can ...be considered as one of the most efficient 3D fabrication techniques. However, due to the vulnerable stiffness of thin-walled micro parts and micro tools, regenerative chatter is prone to occur during the micro-milling process. Severe chatter can lead to large fluctuations in cutting forces, resulting in deterioration of surface roughness/integrity and decreasing in tool life. Therefore, an in-process chatter detection strategy based on the feature extraction of micro-milling forces is proposed in this paper. Micro-milling force signals are measured and processed by advanced algorithms developed. After variational mode decomposition (VMD) for each group of cutting forces, the optimal intrinsic mode function (IMF) is adopted based on the Laplacian score (LS). The multi-scale permutation entropy (MSPE) values of the optimal IMF of each group are calculated, wherein the scale factor s, the embedding dimension m, and the delay time t are obtained by the genetic algorithm (GA). The obtained MSPE values are used as input vectors to train the support vector machine (SVM) classifier, which is used to monitor the stability states of the micro-milling processes. The effectiveness of the proposed strategy is compared with the other classic chatter detection methods and indicators in the micro-milling experiments of thin-walled parts. The results show that the method resulted from the strategy can extract cutting force features effectively and in-process detect chatters accurately in micro-milling thin-walled parts processes.
