Selection Of Process Parameters Research Articles

Impact of zirconia slurry in steel powder on melt pool characteristics in laser powder bed fusion

Purposedding dopants to a powder bed could be a cost-effective method for spatially varying the material properties in laser powder bed fusion (LPBF) or for evaluating new materials and processing relationships. However, these additions may impact the selection of processing parameters. Furthermore, these impacts may be different when depositing nanoparticles into the powder bed than when the same composition is incorporated into the powder particles as by ball milling of powders or mixing similarly sized powders. This study aims to measure the changes in the single bead characteristics with laser power, laser scan speed, laser spot size and quantity of zirconia nanoparticle dopant added to SS 316 L powder.Design/methodology/approachA zirconia slurry was inkjet-printed into a single layer of 316 SS powder and dried. Single bead experiments were conducted on the composite powder. The line type (continuous vs balling) and the melt pool geometry were compared at various levels of zirconia doping.FindingsThe balling regime expands dramatically with the zirconia dopant to both higher and lower energy density values indicating the presence of multiple physical mechanisms that influence the resulting melt track morphology. However, the energy density required for continuous tracks was not impacted as significantly by zirconia addition. These results suggest that the addition of dopants may alter the process parameter ranges suitable for the fabrication of high-quality parts.Originality/valueThis work provides new insight into the potential impact of material doping on the ranges of energy density values that form continuous lines in single bead tests. It also illustrates a potential method for spatially varying material composition for process development or even part optimization in powder bed fusion without producing a mixed powder that cannot be recycled.

129 Protein and Amino Acid Modifications During Extrusion

Abstract Extrusion cooking is a high-temperature, short-time process and the foremost technique to produce attractive, nutritious and safe pet foods. As extruded foods are often routinely fed throughout the entire life of pet dogs and cats, it is important to understand the factors that influence protein quality. Protein quality, i.e. the quantity and bioavailability of amino acids, depends on the ingredients and the combination of interrelated process parameters including moisture, temperature, pressure, and mechanical shear. Few studies on the effects of extrusion of pet foods are available and both positive and negative effects on the bioavailability of proteins and amino acids were noted. Effects can in part relate to changes in the food matrix rendering proteins available for digestive enzymes. Moreover, various molecular transformations and chemical reactions can occur including denaturation, aggregation, Maillard reaction, cross-linking, oxidation, and racemisation. Denaturation of proteins is likely not important for facilitating digestion, but denaturation of enzyme inhibitors is key for plant-based pet foods as well as the reduction or elimination of non-protein anti-nutritional factors. It is clear that both ingredient selection and extrusion process parameters impact changes in quantities of bioavailable lysine via the Maillard reaction. The supply of reactive lysine can be insufficient for growing dogs and should be monitored. Bioavailability of methionine and cysteine can also be compromised due to oxidation during processing but is currently understudied. As with studies on lysine, specific analytical procedures should be applied to prevent overestimation of methionine and cysteine in pet foods. In conclusion, the extrusion is a complex process that can impact protein quality in many ways and to various degrees and more studies are required to advance our understanding of the exact underlying molecular mechanisms and to assure the quality of pet foods.

Simulation of the Influence of the as-Deposited Wall Thickness on Arc Shape and Stability during Wire Arc Additive Manufacturing

The single-pass multi-layer depositing strategy is usually used to fabricate thin-wall structures with wire and arc additive manufacturing (WAAM) technology. Various deposited wall thicknesses often lead to a change in arc shrinkage in the wall thickness direction, which affects the arc shape and stability, and even the microstructure and properties. To systematically study the effect of wall thickness (δ) on arc shape and stability, 3D numerical models were established, with wall thickness varying from 1 to 14 mm during the WAAM process. The characteristics of the arc shape, temperature field, velocity field, current density, and the electromagnetic force were investigated. When δ is smaller than the arc diameter (Φ), the thinner wall will result in a longer arc along the deposition direction. When δ is greater than the Φ, the arc shape tends to be a bell shape. When δ < Φ, the peak temperature in the arc centre, the peak current density, and the electromagnetic intensity along the welding direction decreased with the increase in the wall thickness. However, the opposite observations were found when δ < Φ. The simulation results are consistent with the actual arc shape collected and showed that when δ is slightly less than Φ, the forming quality of the deposited wall is the best. The research in this paper can fill the research gap and provide a theoretical basis for the matching selection of process parameters and wall thickness in WAAM applications.

Numerical investigation of the influence of process parameters and tool path on the temperature in the laser glass deposition (LGD) process

Additive manufacturing has gained interest in the industry due to its flexibility in design and the possibility to integrate functionalities. Thereby, glass has a high potential to be developed also in this field due to its thermal stability, chemical resistance, and optical transmission. Laser glass deposition is a method for fabricating glass components on a glass substrate. The energy input and the resulting temperature are crucial factors in this process, which can influence the material properties and the resulting geometry. Also, depending on the temperature in the substrate, difficulties such as high residual stresses or thermal shock can occur. The temperature on the glass substrate and in the melt zone can be changed either directly by the laser power or laser spot size, or indirectly by other process variables such as travel speed or path planning strategy. In this study, the energy input and the resulting temperature in the melt zone are numerically investigated under selected process parameters. Based on this, a regression function was created so that the generated temperature can be calculated by corresponding laser power, laser spot diameter, and axis velocity. Moreover, different tool path strategies for the production of horizontally multilayered surfaces were thermally investigated. The results showed a more uniform temperature profile with zigzag movement than the spiral tool path. The influence of the turning point angle in path planning on the temperature change was also investigated. It was observed that the 90° corner in contrast to the smaller angle has no significant influence on the temperature change.

Molecular dynamics insights on temperature and pressure effects on electroporation

Electroporation is a cell-level phenomenon caused by an ionic imbalance in the membrane, being of great relevance in various fields of knowledge. A dependence of the pore formation kinetics on the environmental conditions (temperature and pressure) of the cell membrane has already been reported, but further clarification regarding how these variables affect the pore formation/resealing dynamics and the transport of molecules through the membrane is still lacking. The objective of the present study was to investigate the temperature (288–348 K) and pressure (1–5000 atm) effects on the electroporation kinetics using coarse-grained molecular dynamics simulations. Results shown that the time for pore formation and resealing increased with pressure and decreased with temperature, whereas the maximum pore radius increased with temperature and decreased with pressure. This behavior influenced the ion migration through the bilayer, and the higher ionic mobility was obtained in the 288 K/1000 atm simulations, i.e., a combination of low temperature and (not excessively) high pressure. These results were used to discuss some experimental observations regarding the extraction of intracellular compounds applying this technique. This study contributes to a better understanding of electroporation under different thermodynamic conditions and to an optimal selection of processing parameters in practical applications which exploit this phenomenon.

Multi-layer thermo-fluid modeling of powder bed fusion (PBF) process

In this study, a 3D thermo-fluid computational method has been developed and employed to simulate the laser powder bed fusion (PBF) process. The particular objective is to demonstrate the feasibility of multi-layer simulations of the laser PBF process and to discuss the potential and challenges of this approach. The approach includes using the discrete element method (DEM) to simulate particle spreading on a powder bed, as well as computational fluid dynamics (CFD) and heat transfer to simulate laser-powder/matter interactions, in a sequential manner to about 10 layers only with a small scan area, limited by currently available computational power. The simulation results can offer insight such as melt pool shapes and sizes, also solidified surface morphology along different build layers. The model also includes a surface tracking algorithm to account for the formation of voids and lack-of-fusion pores. In addition to achieving the main objective of this study, i.e., feasibility demonstration of this computational process, it is also noted that, for the case studied, the defects in a former layer may shrink, or even vanish, due to the extra thermal energy received from laser scanning of subsequent layers. This study has successfully demonstrated the sequential linkage between a discrete element method and a thermo-fluid model in a multi-layer deposition fashion and the experimental validation will be performed in future work. With significantly greater computational capabilities in the future, the developed method may potentially be utilized as a means to understand physical phenomena and select key process parameters for PBF fabrications of small features.

Performance Prediction in EDM Process for Al 6061 Alloy Using Response Surface Methodology and Genetic Algorithm

The Electric Discharge (EDM) method is a novel thermoelectric manufacturing technique in which materials are removed by a controlled spark erosion process between two electrodes immersed in a dielectric medium. Because of the difficulties of EDM, determining the optimum cutting parameters to improve cutting performance is extremely tough. As a result, optimizing operating parameters is a critical processing step, particularly for non-traditional machining process like EDM. Adequate selection of processing parameters for the EDM process does not provide ideal conditions, due to the unpredictable processing time required for a given function. Models of Multiple Regression and Genetic Algorithm are considered as effective methods for determining the optimal processing variables of Electrical Discharge Machining. The material removal rate (MRR) and tool wear (Tw) were investigated using the process variables of pulse on time (Ton), pulse off time (Toff), and current intensity (Ip). The established empirical models were used to perform Genetic Algorithm (GA) to maximize (MRR) and minimize (Tw). The optimization results were utilized to establish machining conditions, validate empirical models, and obtain optimization outcomes. The optimal result that appears in this work was the pulse on (176.261 μs), pulse off (39.42 μs), and current intensity (23.62 Amp.) to maximize the MRR to (0.78391 g/min) and reduce tool wear to (0.0451 g/min).

Morphology modelling and validation in nanosecond pulsed laser ablation of metallic materials

Nanosecond pulsed lasers are widely used in the surface treatment of metallic materials due to their low cost and simple operation. During pulsed laser processing, many parameters such as laser and material properties are involved. Consequently, the existing mathematical models for pulsed laser ablation of metals are complex and computationally difficult. In this paper, a simple and novel model was proposed for nanosecond pulsed laser processing based on heat transfer and geometrical mathematics. The geometrical parameters functions of the recast layer under pulsed laser single ablation and continuous superimposed ablation are presented, respectively. The feasibility of the models is validated by experiments. Errors in diameter and depth of single-pulse laser ablation craters were 2.56%–7.14% and 6.82%–18.91%. The recast layer depth error is about 3.47%–12.47% for successive pulses of superimposed ablation. It predicts the shape of the recast layer on pulsed laser ablated metal surfaces. The model is a guide to the surface morphology of laser woven materials and the preparation of surface functionalities, particularly the selection of process parameters for microstructure machining.

Grinding parameters prediction under different cooling environments using machine learning techniques

ABSTRACT Selection of optimum process parameters is vital for performing a sound grinding operation on Inconel 751 alloy. This paper co-relates the relationship between the most influential input parameters like cutting velocity, depth of cut, feed rate, and environmental conditions to the output parameters, namely, tangential grinding forces, normal grinding forces, temperature, and surface roughness. Three types of machine-learning (ML) algorithms such as support vector machine (SVM), Gaussian process regression (GPR), and boosted tree ensemble techniques are employed to develop a ML model for predicting the output variables during grinding operation of Inconel 751. In order to develop a better ML model, K-fold technique is employed on a total of 81 datasets which are extracted from experimental studies. ML models developed from different algorithms are compared based on performance metrics like R2 score and root-mean-square error (RMSE). GPR algorithm exhibits best results with relatively better R2 score and RMSE value in predicting grinding forces and temperature at wheel work interface. From analyzing the ML models, it is found that cooling environments determined the output grinding parameters to a greater extent when compared with the input grinding parameters.

Selection of Optimal Process Parameters for a Mild Steel Weld using Technique for Order of Preference by Similarity to Ideal Solution

Welding is a major operation in many industries such as production and manufacturing, oil and gas, amongst others. Obtaining an optimal weld with the required properties; tensile strength and hardness is of high importance. The work used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select the optimal process parameters for a mild steel weld by employing five (5) standard steps in ranking 30 experimental steps. The result obtained gave an optimal tensile strength and hardness of 496.5MPa and 190.2 respectively for a combined input parameters of 170Amp, 20volts, 22l/min, and 3.2mm. These data is very important as it will provide the necessary information for evaluating the durability and lifespan of the weld product which are factors considered in assessing its quality.

Laser powder bed fusion of TiB2-modified Cu15Ni8Sn alloy: Processability, microstructure and mechanical performance

Cu15Ni8Sn is widely used in the aerospace and electronics domains because of its good conductivity and toughness. Due to the material's high laser reflectivity and thermal conductivity, however, employing the laser powder bed fusion (LPBF) additive manufacturing process on Cu15Ni8Sn alloy has been a challenge. This issue was addressed in the present study by modifying the Cu15Ni8Sn powder by adding 1 wt% submicrometre TiB2 ceramic particles. The results indicate that the LPBF's processability was improved by the increased laser absorption rate caused by the addition of 1 wt% TiB2, which eliminated pores and unmelted area defects. The addition of 1 wt% TiB2 also improved the components' surface quality by reducing the surface roughness value of 39.3% compared to the original Cu15Ni8Sn. The findings also indicate that the added TiB2 particles reduced Sn-segregation behaviour, implying that the content and size of the segregated phase could be tailored by the suitable selection of ceramic materials and LPBF process parameters. Another advantage lies in the enhancement of mechanical performance, where a 11% increase in yield strength and 14% increase in ultimate tensile strength were achieved in the LPBF-fabricated modified Cu15Ni8Sn material. In this study, the primary strengthening mechanisms for LPBF-fabricated original Cu15Ni8Sn were found to include dislocation strengthening and grain boundary strengthening. Orowan precipitation strengthening also played a significant role in the Cu15Ni8Sn-1wt.% TiB2 material, as did dislocation strengthening and grain boundary strengthening. These findings provide a promising strategy to improve the LPBF processability of the advanced copper-based materials used in several key fields.

Modeling and Analysis of Micro-Grinding Processes with the Use of Grinding Wheels with a Conical and Hyperboloid Active Surface.

In this article, a method of grinding small ceramic elements using hyperboloid and conical grinding wheels was presented. The method allowed for machining with a lower material removal speed and extending the grinding zone without reducing the efficiency of the process. In order to assess the process output parameters, numerical simulations were carried out for single-pass machining. This strategy allows for automation of the process. Grinding with a low material removal speed is recommended for the machining of small and thin elements, since this can avoid fracturing the elements. The methodology for selecting process parameters as well as the results of the abrasive grains activity analyses were presented. The analyses also concerned the roughness of machined surfaces and the variability of their textures. This grinding method was applied in the production of small ceramic elements that are used in the construction of electronic systems, and in the processing of small piezoceramic parts. This grinding technique could also be used in other grinding processes, where the removal of small machining allowances with high efficiency is required.

Predicting Spray Dried Dispersion Particle Size Via Machine Learning Regression Methods

Spray dried dispersion particle size is a critical quality attribute that impacts bioavailability and manufacturability of the spray drying process and final dosage form. Substantial experimentation has been required to relate formulation and process parameters to particle size with the results limited to a single active pharmaceutical ingredient (API). This is the first study that demonstrates prediction of particle size independent of API for a wide range of formulation and process parameters at pilot and commercial scale. Additionally we developed a strategy with formulation and target particle size as inputs to define a set of “first to try” process parameters. An ensemble machine learning model was created to predict dried particle size across pilot and production scale spray dryers, with prediction errors between −7.7% and 18.6% (25th/75th percentiles) for a hold-out evaluation set. Shapley additive explanations identified how changes in formulation and process parameters drove variations in model predictions of dried particle size and were found to be consistent with mechanistic understanding of the particle formation process. Additionally, an optimization strategy used the predictive model to determine initial estimates for process parameter values that best achieve a target particle size for a provided formulation. The optimization strategy was employed to estimate process parameters in the hold-out evaluation set and to illustrate selection of process parameters during scale-up. The results of this study illustrate how trained regression models can reduce the experimental effort required to create an in-silico design space for new molecules during early-stage process development and subsequent scale-up.

Effect of Abrasive Grain Concession on Micromechanical Behavior of Lapping Sapphire by FAP.

Aiming at exploring the material removal mechanism for sapphire using diamond abrasive grains at the microscopic level, this paper modeled and analyzed the microscopic yield behavior of diamond abrasive grains in the FAP grinding process of sapphire. Molecular dynamics were used to simulate the effects of abrasive particle size on the cutting force, potential energy, and temperature in the Newtonian zone during micro-cutting. The effect of different abrasive particle sizes on material removal was analyzed through experiments. The simulation results show that the abrasive particle radius was 12 Å, the micro-cutting force reached more than 3500 nN, while the cutting force with an abrasive particle radius of 8 Å only reached 1000 nN. Moreover, the potential energy, cutting force, and temperature in the Newtonian zone between the sapphire crystal atoms also increased. The results showed that the material removal rate saw a nonlinear increasing trend with the increase in particle sizes, while the surface roughness showed an approximately linear increase. Both of them showed a similar trend. The experimental results lay a theoretical basis for the selection of the lapping process parameters in sapphire.

Optimization of stochastic feature properties in laser powder bed fusion

Process parameter selection in laser powder bed fusion (LPBF) controls the as-printed dimensional tolerances, pore formation, surface quality and microstructure of printed metallic structures. Measuring the stochastic mechanical performance for a wide range of process parameters is cumbersome both in time and cost. In this study, we overcome these hurdles by using high-throughput tensile (HTT) testing of over 250 dogbone samples to examine process-driven performance of strut-like small features, ~1 mm2 in austenitic stainless steel (316 L). The output mechanical properties, porosity, surface roughness and dimensional accuracy were mapped across the printable range of laser powers and scan speeds using a continuous wave laser LPBF machine. Tradeoffs between ductility and strength are shown across the process space and their implications are discussed. While volumetric energy density deposited onto a substrate to create a melt-pool can be a useful metric for determining bulk properties, it was not found to directly correlate with output small feature performance.

Porosity management and control in powder bed fusion process through process-quality interactions

Laser Powder bed fusion (L-PBF) has gained much attention for its ability to manufacture high-precision and high-complexity metal components for the aircraft and automobile industries. However, to a certain degree, the print quality (shape, GD&T, mechanical property) is difficult to predict and control due to the multi-variant processability. Recent research predominantly focuses on building a semantic/qualitative process relationship from the single/a few process parameters on the ultimate print qualities, such as thermal distortion failure/defect probabilities. However, such semantic/qualitative process relationships cannot reflect specific governing effects from process variables and provide an optimal selection of the parameters to ensure the build quality. Therefore, there is a strong need to develop a quantitative model within a process network and achieve a constant quality level by controlling the process parameter set. To address the aforementioned challenges, this research focuses on developing a multi-dimensional process-quality interaction model to predict and control the porosity of Titanium 6Al-4V in L-PBF. The process network is first derived from existing literature composing the potential, influential process parameters that can deviate the porosity level, such as laser power and scanning speed. Then the quantitative model is built based on a multi-dimensional quantitative modeling technique from a collection of experimental data. The multi-dimensional modeling provides an architecture that replaces the artificial neural network (ANN) or regression model to depict the mathematical relationship. The established quantitative process models analyze the intercorrelated effects from process parameters to the porosity. The printable zone can also be derived from such a quantitative model and visualized through a multi-dimension variable-control response graph to optimize the selection of process parameters. This would result in a comprehensive understanding of selecting the process parameters according to the desired porosity level and pave the path to fully control the metal L-PBF print qualities by adjusting the process variables. The experimental data also validate the proposed quantitative model to demonstrate its effectiveness and correctness.

Limitations of linear energy density for laser powder bed fusion of Mg-15Gd-1Zn-0.4Zr alloy

Linear energy density is often used as a design parameter to select laser power and scanning speed for the laser powder bed fusion (LPBF) of alloys. In the present study, the effects of laser power and scanning speed on the depth and width of remelted track and porosity of the as-built Mg-15Gd-1Zn-0.4Zr (GZ151K, wt%) alloy were systematically studied. The experimental results present that the depth and width of remelted track and porosity vary significantly despite using the same linear energy density value but different combinations of laser power and scanning speed values, indicating the limitations of linear energy density when changing laser power and scanning speed simultaneously. Furthermore, sensitivity level analysis was conducted to assess the relative importance of laser power and scanning speed to the depth and width of remelted track and porosity. It was found that scanning speed is more important to the depth and width of remelted track than laser power, while laser power is more important to the porosity than scanning speed. Therefore, using the linear energy density as a design parameter for LPBF is not always reliable, and the present study can provide some guidance for selecting process parameters for the LPBF of alloys. • The depth and width of remelted track and porosity vary significantly despite using the same linear energy density. • Linear energy density is not always reliable especially when changing laser power and scanning speed simultaneously. • Scanning speed is more important to the depth and width of remelted track than laser power. • Laser power is more important to the porosity than scanning speed.

Simultaneous consideration of relative density, energy consumption, and build time for selective laser melting of Inconel 718: A multi-objective optimization study on process parameter selection

Additive manufacturing (AM) is regarded as sustainability friendly thanks to its significantly reduced production steps and simplified supply chain at the system level, but it is often not energy efficient at the process level. In this study, we propose that the optimization of AM process parameters should not only maximize build quality, but also minimize environmental impact and build time. A multi-objective optimization problem of maximizing relative density (RD), minimizing energy consumption and build time per unit volume (ev and tv respectively) is formulated and solved for selective laser melting (SLM) of Inconel 718 alloy. A high accuracy RD prediction model with R2 of 93.23% is obtained by fitting SLM experiment data from wide ranges of laser power (P), scanning speed (SS), hatch spacing (HS), and layer thickness (LT). The ev and tv models are created considering printing area utilization rate (U) based on laser scanning and powder coating process. The two approaches of Pareto front and global criterion method are adopted for solving the multi-objective optimization problem. It is found that the highest RD of close to 100% can be obtained at the expense of high ev and tv of close to 500 J/mm3 and 0.3 s/mm3, respectively, and about three times ev and tv are needed to increase RD from about 95% to 100% than from 85% to 95%. At the same U level, the solutions obtained from prioritizing ev and tv are consistent, while those from prioritizing RD show smaller process parameter values. The multi-objective optimization formulation provides an enabling tool for industry to obtain high quality SLM-ed parts with improved sustainability and efficiency at the process level. The methodology developed is applicable to selective laser melting of other materials, and such consideration can be readily expanded to other AM processes as well.

Numerical Simulation and Experimental Study the Effects of Process Parameters on Filament Morphology and Mechanical Properties of FDM 3D Printed PLA/GNPs Nanocomposite.

The selection of optimal process parameters has a decisive effect on the quality of 3D printing. In this work, the numerical and experimental methods were employed to investigate the FDM printing deposition process of PLA/GNPs nanocomposite. The effect of process parameters on cross-sectional morphology and dimension of the deposited filament, as well as the mechanical property of the FDM printed specimens were studied. The extrusion and the deposition process of the molten PLA/GNPs nanocomposite was simulated as a fluid flow by the paradigm of CFD, the effects of printing temperature and shear rate on thermal-physical properties, such as viscosity and surface tension, were considered in models. Under the assumptions of non-Newtonian fluid and creep laminar flow, the deposition flow was controlled by two key parameters: the nozzle temperature and the nozzle velocity. The numerical model was verified by experiments from four aspects of thickness, width, area, and compactness of the deposited PLA/GNPs nanocomposite filament cross-section. Both the numerical simulation and experiment results show that with the increase of nozzle temperature and nozzle velocity, the thickness, area, and compactness of the deposited filament decreases. While the width of deposited filament increased with the increase of nozzle temperature and decrease of nozzle velocity. The decrease in thickness and the increase in width caused by the change of process parameters reached 10.5% and 24.7%, respectively. The tensile strength of the printed PLA/GNPs specimen was about 61.8 MPa under the higher nozzle temperatures and velocity condition, an improvement of 18.6% compared to specimen with the tensile strength of 52.1 MPa under the lower nozzle temperatures and velocity condition. In addition, the experimental results indicated that under the low nozzle velocity and nozzle temperature condition, dimensional standard deviation of the printed specimens decreased by 52.2%, 62.7%, and 68.3% in X, Y, and Z direction, respectively.

TECHNLOGICAL PREREQUISITES FOR DEFORMATION AND FAULT OF THE CUT LAYER DURING THE SPEED GEAR MILLING OF HARDENED CYLINDRICAL GEARS ON THE BASIS OF THE ATOMIC APPROACH

The scientific foundations of the technological regulation for the selection and assignment of processing parameters during gear milling are based on the fundamental theoretical provisions of the friction theory, mechanical engineering technology and cutting theory. For the first time, the sliding angle was scientifically substantiated during plastic displacement (pre-deformation) of the material, when the material flows around the indenter (tool) without separation from the main mass. Determination of the minimum sliding angles during the processing of large-module gear wheels by milling, based on the physicochemical processes of friction mechanics, can significantly increase the efficiency of the tooth shaping process and the operational properties of their surfaces. An atomic approach to the processes of deformation and destruction of chips during cutting is presented. The resistance to plastic deformation of the cut layer depends on the type of crystal lattice of the processed material, its stacking fault energy, and the presence of impurities at the grain boundaries. The influence of these factors is manifested through the types of dissipative structures that are formed in the process of deformation of the cut layer and determine its localization.