Cutter Location Research Articles

Hyper CLS-Data-Based Robotic Interface and Its Application to Intelligent Peg-in-Hole Task Robot Incorporating a CNN Model for Defect Detection

Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB.

Utility of 3D-Printed Models in the Surgical Planning for Primary Spine Tumors: A Survey of International Spinal Oncology Experts.

Survey study. The purpose of this study was to characterize the utility of 3D printed patient specific anatomic models for the planning of complex primary spine tumor surgeries. A survey of individual members of an international study group of spinal oncology surgeons was performed. Participants were provided a clinical vignette, pathologic diagnosis, and pre-operative imaging for three primary spinal oncology cases. Study participants provided a free text surgical plan for resection and were then presented an associated 3D printed model for each case and asked to re-evaluate their surgical plan. Ten spinal oncology surgeons participated in the study, representing nine institutions across five countries. Four of the surgeons (40%) made significant changes to their surgical plan after reviewing the 3D models, including sacrifice of an additional nerve root to obtain negative margins, sparing an SI joint that was originally planned for inclusion in the en bloc resection, adjusting the location of osteotomy cuts, changes to the number of surgical stages and/or staging order, and preservation of neurology that was originally planned for sacrifice. The overall impression of the 3D models was positive, with 90% of the participants stating they found the 3D model useful in developing a surgical plan. Surgical planning for resection of primary spinal column tumors is challenging and time intensive. 3D printed patient specific surgical models may be an additional tool that can augment surgical planning and execution by improving the chance of accomplishing surgical resection goals and minimizing morbidity.

AN EFFICIENT ITERATIVE METHOD TO SIMULATE THE VARYING STATIC RECEPTANCE OF A THIN-WALLED WORKPIECE

This paper introduces an accelerated iterative Conjugate Gradient method (CG-method) for simulating the varying static receptance of a thin-walled workpiece. The proposed approach enhances computational efficiency by computing an optimal initial displacement guess for a given cutter location. This guess is based on the previously calculated displacement vector from the preceding cutter location, serving as the start iteration vector for the CG-method. The validation of efficiency gains through this method is demonstrated by a significant reduction in computation time for a numerical example. Moreover, to ensure the accuracy of the accelerated iterative CG-method, comparisons are made with the Cholesky method. The results confirm the precision of the proposed approach.

Efficient Voxel-Based Workpiece Update and Cutter-Workpiece Engagement Determination in Multi-Axis Milling

Abstract This paper presents a new method to efficiently update workpiece and determine cutter-workpiece engagement (CWE) in multi-axis milling simulation based on a uniform voxel modeling space. At each cutter location, a novel algorithm named direct voxel tracing is developed and used to generate a functional cutter surface voxel model to reliably establish the internal space of the milling cutter. The cutter internal space is represented by its voxel boundary with small memory usage. Through the Boolean subtraction between two successive voxel boundaries of the cutter internal space, a minimal voxel deactivation region is attained within which all active workpiece voxels are deactivated (removed) to update the workpiece model. To determine the associated CWE map, a 3D circle voxelization algorithm is employed. By slicing the cutter surface by a sequence of planes perpendicular to and along the cutter axis, CWE can be determined as the sliced 3D circles are voxelized. Quantitative comparisons of the proposed method against existing voxel modeling and vector modeling-based methods have been made. The results have demonstrated much improved computational efficiency of the proposed method in simulating the complex multi-axis milling operations.

Fast extraction of cutter-workpiece engagement for milling force prediction in multi-axis machining

The paper proposes a fast extraction method for cutter-workpiece engagement (CWE), which greatly facilitates the milling force prediction during multi-axis machining. One of the key cores of the method is to reconstruct the nearby cut-off material of workpiece using a set of past neighboring cutter location points (CLPs), and obtain the CWE entity through local Boolean operation among the current tool, workpiece blank, and reconstructed entity. Moreover, by performing 2-dimensional (2D) image mapping, the engagement boundary of CWE entity will be quickly obtained. Combining with the analytical formula of instantaneous undeformed chip thicknesses (IUCTs), the composite IUCT for tool side and bottom cutting edges during actual immersion is further derived. Finally, accurate and fast prediction of milling force with consideration of tool attitude, corner feature and bottom cutting effect is achieved. The results reveal that the method can predict milling force with an excellent performance of first-order computation complexity.

Mathematical modeling and simulation of generation milling process to manufacture complex face gear surface through multi-axis machine kinematics

Manufacturing of face gear is a complex process, due to its varying geometry, hence the application is not wide as conventional gear geometry. The developments in analytical tools and kinematics of the CNC machine tools motivate researchers to explore the manufacturing process and associated applications. This article presents an efficient method for the generation milling of spur face gear utilizing a nutating head multi-axis CNC machine tool where the tool swings about an arbitrary axis in an inclined plane. The mathematical model of the disk milling cutter and face gear geometry were derived from the shaper geometry by applying the theory of gearing, equation of meshing and coordinate transformation. The cutter location (CL) data was evaluated by aligning the tool orthogonal unit vector and the tooth surface normal vector at the contact point by calculating the appropriate transformation matrices. The evaluation of machine coordinates on a multi-axis CNC milling machine was analyzed with respect to the machine pivot point and the axes movement during generation milling for the face gear drive was studied on the nutating head machine. The NC data is derived from the CL data using the inverse kinematics and the machining simulation was carried out using suitable software. The tooth surface deviation of a maximum of 43.5 μm was observed between the simulated model and the theoretical model, which is well within the requirement of roughing. The generation milling process with the proposed machine configuration is concluded as a profound face gear manufacturing technique.

Improving dynamic process stability in finishing of thin-walled workpieces by optimal selection of stock shape

In 5-axis milling of thin-wall parts, flexibility of the in-process-workpiece (IPW) governs static and dynamic deflections, especially at the semi-finishing and finishing stages. Thus, the near net shape preform, i.e. the stock shape left for semi/finishing is crucial for chatter stability. In this study, a methodology is presented to design the stock shape for improved stability. Coupled process simulation, which includes material removal, finite element analysis (FEA) and process stability simulations, is used to show the effect of stock shape on chatter stability. Spindle speed is optimized along the toolpath based on the varying process dynamic behavior for selected cutter locations (CL). The proposed method is experimentally demonstrated on case studies.

Layer-based information model for 5-axis milling processes using a GraphQL schema

The view of process-related information in NC-bound manufacturing technologies can range from a perspective of a single cutter location within a considered toolpath to aggregated data which represent the whole operation sequence of a manufacturing process. Data processing pipelines are often individually programmed for the process planning or machining stage addressing an isolated database abstraction layer. This paper proposes a data modeling approach based on a GraphQL schema empowering the user to query planning and actual process data cross-linked with different aggregation layers by a single request.

AN EFFICIENT METHOD TO SIMULATE THE VARYING STATIC RECEPTANCE OF A THIN-WALLED WORKPIECE

A new method to simulate the varying static receptance of a thin-walled workpiece based on the Cholesky decomposition is presented in this paper. In this method, the system stiffness matrix is updated by subtracting the elemental stiffness matrices of the removed elements from the initial system stiffness matrix. The updated system stiffness matrix is permuted by using a novel method based on element removing sequence and considering fill-in reduction. The Cholesky factor of the permuted system stiffness matrix is reused to compute the static receptance of the workpiece for multiple cutter locations. The reduction of computation time by using this new method is proved by a numerical test and the accuracy of the simulation is demonstrated with a machining test.

Accurate prediction of five-axis machining cycle times with deep neural networks using Bi-LSTM

This paper presents a novel machine learning (ML) based approach to predict machining cycle (part program running) times for complex 5-axis machining. Typical 5-axis machining toolpaths consist of short-segmented discrete linear cutter location lines (CL-lines) with simultaneously varying tool center point (TCP) and orientation vectors (ORI). As the 5-axis machine tool NC (numerical control) system tries to interpolate such part programs smoothly, the TCP motion decelerates and accelerates repeatedly causing the actual feedrate to fluctuate. The actual observed (resultant) feedrate can be approximately 30 % lower than the user commanded one. Furthermore, if the toolpath requires the 5-axis machine tool to travel closer to its singular point, or if the workpiece placement on the table is not optimal, actual feedrate is even lowered further. This paper presents two ML-based approaches to accurately predict 5-axis machining toolpaths. The first strategy uses a bidirectional long short-term memory (Bi-LSTM) network to model the machine tool behavior and generates a direct final cycle time estimation for any given part program. This approach only uses the toolpath geometry to predict the cycle time, and for training it only needs the “final” cycle times of similar part programs making it practical on today’s shop floors. The second strategy requires individual CL-line processing times for training. In return, it can provide highly accurate cycle time predictions. Both strategies can capture the interpolation dynamics of 5-axis machine tool NC systems accurately. Simulation studies and experimental validations are conducted on modern 5-axis machine tools with varying workpiece placements and interpolation parameters. Proposed approaches have shown to predict cycle times with 90–95 % accuracy on real-life complex 5-axis machining toolpaths.

An effective process design intent inference method of process data via integrating deep learning and grammar parsing

With the constantly generation of process data in industries, process knowledge reuse and learning from process dataset has shown promising potential in machining process planning. However, existing methods cannot effectively extract different granularity process intents, and thus the process data cannot be effectively utilized. In this paper, an effective process design intent inference method of process data via integrating deep learning and grammar parsing is proposed. First, based on the projection of cutter location segments, an association approach between machining operations and machining features is proposed to extract the process intent of each machining operation. Then, a deep learning based working step process intent sequence decision-making model is proposed to estimate the probability distributions of alternative working step process intents at each time. Moreover, according to the working step process intent sequences of existing parts in an industry, the process knowledge And-Or graph (PK-AOG) is constructed by introducing the probabilistic grammar graph model. Finally, the optimal process intents of each working step are jointly obtained based on grammar parsing by taking the PK-AOG as a guide. In the experiments, the effectiveness of our approach is validated by developing a prototype system, and the results show that our approach is practical in process intents extraction.

Experimental studies of fission product release from model fuel elements at the physical start-up of the IVG.1M research reactor

During the physical start-up of the IVG.1M research reactor, which involved the conversion to low-enriched uranium fuel, various studies were conducted using physical mock-ups of the fuel assemblies. The mock-up consisted of 468 model low-enriched uranium fuel elements that were cut into three sections without end coatings at the cut locations. This configuration allowed for the simulation of fuel element decompression and the experimental determination of fission product release into the coolant.The coolant samples obtained from the mock-up were analyzed using a gamma-spectrometer equipped with a high-purity germanium detector. As a result of the studies, the activities of fission products in the coolant and the averaged relative fission product release were determined. The relative release is defined as the ratio of the release rate of reference fission products to its birth rate.For the first time, fission product release was experimentally obtained in a situation simulating the decompression of IVG.1M fuel cladding.

Choosing the location of the opening cut to expose brown coal deposits – problem solving and decision making with the use of multiple-criteria decision analysis (MCDA)

Choosing the location of the opening cut to expose brown coal deposits – problem solving and decision making with the use of multiple-criteria decision analysis (MCDA)

Accessibility and Trajectory Planning of Cutter Changing Robot Arm for Large-Diameter Slurry Shield

Aiming at solving the problems of huge cost, long cycle and high risky during manual cutter changing process for shield machine, a cutter changing robot was designed to adapt to its narrow and complex space, the workspace and motion path of cutter changing robot arm were studied. Firstly, by analyzing the internal space and cutter layout characteristic, the configuration of the robot arm was proposed, and the dimension of each rod was designed using multi-objective optimization method. The kinematic model of the robot arm was established by using the D-H model method to analyze its reachable working space. While the inverse kinematic model of the robot arm was solved by the Newton-Raphson iterative method to complete the trajectory planning of the arm for all cutter locations, where the relationship between the target position and the joint variables was established. The motion stability of the robot was verified by virtual simulation and test. The results show that the working space of the designed robot arm meets the cutter changing requirements that all the cutter position can be reached. Moreover, the power output of the drive is smooth, which can transport the cutter weighing up to 250kg.

A framework for practically effective creation of postprocessors for 5-axis CNC machines with all possible configurations and working mechanisms

The 5-axis CNC (Computer Numerical Control) machining plays an important role in manufacturing, especially in making parts of complex shapes such as turbine or propeller blades. However, there are always challenges of generating 5-axis CNC machining programs when working with different 5-axis CNC machines of various structures; and 5-axis CNC programs must be automatically generated by CNC postprocessors which calculate the inverse kinematics and convert the Cutter Location data to the CNC programs that are used for operating 5-axis CNC machines. Since the family of 5-axis CNC machines has a wide spectrum of machine configurations, with hundreds of mechanisms of 5 degrees of freedom, it is therefore practically challenging for engineers and CNC machine operators to create CNC postprocessors for specific 5-axis CNC machines. It is more challenging to create CNC postprocessors if engineers or CNC operators do not have strong backgrounds and professional skills in mathematical modeling and kinematics of machines. This paper presents a universal and intuitive framework and practical guidance to create CNC postprocessors for all 5-axis CNC machines, with the focus on a novel mathematical formulation of inverse kinematics of three main groups of 5-axis CNC machines. The case studies of creating CNC postprocessors with the commercially available 5-axis CNC machines were successfully demonstrated, in which the simulation scenarios and experiments were implemented to verify the created CNC postprocessors. The proposed frameworks and guides for generating CNC postprocessors can be conveniently and effectively applied in industrial practices, without the required strong backgrounds and skills for engineers or CNC operators in mathematical modeling and kinematics of machines, especially mathematical modeling of multibody systems.

Offline Feed-Rate Scheduling Method for Ti–Al Alloy Blade Finishing Based on a Local Stiffness Estimation Model

In the aerospace field, Ti–Al alloy thin-walled parts, such as blades, generally undergo a large amount of material removal and have a low processing efficiency. Scheduling the feed rate during machining can significantly improve machining efficiency. However, existing feed-rate scheduling methods rarely consider the influence of machining deformation factors and cannot be applied in the finishing stages of thin-walled parts. This study proposes an offline feed-rate scheduling method based on a local stiffness estimation model that can be used to reduce machining errors and improve efficiency in the finishing stage of thin-walled parts. In the proposed method, a predictive model that can rapidly calculate the local stiffness at each cutter location point and a cutting-force prediction model that considers the effect of cutting angle are established. Based on the above model, an offline feed-rate scheduling method that considers machining deformation error constraints is introduced. Finally, an experiment is performed by taking the finishing of actual blade parts as an example. The experimental results demonstrate that the proposed feed-rate scheduling method can improve the machining efficiency of parts while ensuring machining accuracy. The proposed method can also be conveniently applied to feed-rate scheduling in the finishing stage of other thin-walled parts without being limited by machine tools.

Milling Features Recognition Methodology of Rectangular Fillet Blind Pocket for Universal Data Structure

Various studies have focused on developing an interface system that could integrate the machining process with CNC (Computer Numerical Control) machine tools by producing tool paths using STEP (Standard for the Exchange of Product model data) file as an input function. Studies to recognize features in STEP file by encoding the data of geometric entities of a CAD (Computer-Aided Design) design has been done by many researchers. However, they only focus on Cutter Location (CL) which eventually describes the machining profile in the x and y planes without considering the depth of the machining feature in the z-axis. Identifying and recognizing machining features in the STEP file is important since STEP is a platform for converting industry-standard CAD systems and software data models. The aim of this paper is to identify profiles of geometric machining features for rectangular fillet blind pockets using the Geometric Data Extraction (GDE) approach. Three cases have been carried out namely single rectangular fillet blind pocket, double rectangular fillet blind pocket and triple rectangular fillet blind pocket. The results were verified by manual graph plotting and automatic graph plotting using CPM 3D Plotter.

KCS 선형의 모형시험 및 CFD 해석을 통한 Newman-Sharma 파형저항 추정법의 검토

The present study aims to review the wave pattern resistance analysis method suggested by the International Towing Tank Conference. From the experimental database of a container carrier ship model, the wave pattern measurement and resistance test results are utilized. The wave pattern resistance at the design Froude number is obtained to be compared with the wave making resistance of experiments. Wave pattern resistance is lower than wave making resistance by 1978 ITTC and uniform regardless of transverse location of wave cut. The method is also applied to the wave height field by Computational Fluid Dynamics (CFD) analyses with Froude number variation. Although numerical damping suppressed waves in downstream, waves around the hull and wave pattern resistance are properly predicted.

Tool path generation for 5-axis flank milling of ruled surfaces with optimal cutter locations considering multiple geometric constraints

Ruled surfaces found in engineering parts are often blended with a constraint surface, like the blade surface and hub surface of a centrifugal impeller. It is significant to accurately machine these ruled surfaces in flank milling with interference-free and fairing tool path, while current models in fulfilling these goals are complex and rare. In this paper, a tool path planning method with optimal cutter locations (CLs) is proposed for 5-axis flank milling of ruled surfaces under multiple geometric constraints. To be specific, a concise three-point contact tool positioning model is firstly developed for a cylindrical cutter. Different tool orientations arise when varying the three contact positions and a tool orientation pool with acceptable cutter-surface deviation is constructed using a meta-heuristic algorithm. Fairing angular curves are derived from candidates in this pool, and then curve registration for cutter tip point on each determined tool axis is performed in respect of interference avoidance and geometric smoothness. On this basis, an adaptive interval determination model is developed for deviation control of interpolated cutter locations. This model is designed to be independent of the CL optimization process so that multiple CLs can be planned simultaneously with parallel computing technique. Finally, tests are performed on representative surfaces and the results show the method has advantages over previous meta-heuristic tool path planning approaches in both machining accuracy and computation time, and receives the best comprehensive performance compared to other multi-constrained methods when machining an impeller.

Calculation method of cutter location points considering corner transition speed in CNC

The calculation method of cutter location (CL) points only considers the geometric errors, but cannot consider the requirements of the corner transition algorithm in CNC, which results in the smoothing algorithm in the CNC not playing a maximum role or even failing. To solve this problem, a calculation method of the CL points considering corner transition speed in CNC is proposed. Firstly, the key geometric parameters of the CL points that affect the corner speed are analyzed. Secondly, according to the analysis results, the objective function is established to connect the CL points calculation in CAM and the corner transition speed in CNC. By solving the objective function, the calculation method of the CL points is developed. Finally, the effectiveness of this method is verified by experiment. Comparing with the commonly used iso-chord height method, the proposed method can increase the speed at the corner of the CL points and reduce the acceleration and jerk at the same time. By the proposed method, the tracking accuracy, trajectory accuracy, and machining efficiency can be significantly improved. The proposed method can be integrated into CAM software to generate high quality CL points.