Numerical Control Machining Process Research Articles

Low Observable Uncrewed Aerial Vehicle Wind Tunnel Model Design, Manufacturing, and Aerodynamic Characterization

Developing wind tunnel models is time consuming, labor intensive, and expensive. Rapid prototyping for wind tunnel tests is an effective, faster, and cheaper method to obtain aerodynamic performance results while considerably reducing acquisition time and cost for the models. Generally, the rapid prototyping models suffer from insufficient stiffness or strength to withstand the loads generated during a wind tunnel test. In the present study, a rapid prototype model reinforced with metallic inserts was produced to experimentally investigate the aerodynamic characteristics of an uncrewed aerial vehicle with various wingtip deflections. The fused deposition modeling process was used to make the outer mold, whereas the metallic parts were produced using laser cutting and the computer numerical control machining process. Then, the model was evaluated both experimentally and numerically. The test campaign presented in this work was conducted in the de Havilland low-speed wind tunnel facility at the University of Glasgow. For better characterization of flow patterns dominated by leading edge vortices, numerical simulations were run using OpenFOAM 8.0 and validated with experimental data. The experimental data obtained from the hybrid rapid-prototyped model agreed well with the numerical results. This demonstrates the efficacy of hybrid rapid-prototyped models in providing reliable results for initial baseline aerodynamic database development within a short period and at a reduced cost for wind tunnel tests.

Superhydrophobic Rotation-Chip for Computer-Vision Identification of Drug-Resistant Bacteria.

The transport, distribution, and mixing of microfluidics often require additional instruments, such as pumps and valves, which are not feasible when operated in point-of-care (POC) settings. Here, we present a simple microfluidic pathogen detection system known as Rotation-Chip that transfers the reagents between wells by manually rotating two concentric layers without using external instruments. The Rotation-Chip is fabricated by a simple computer numerical control (CNC) machining process and is capable of carrying out 60 multiplexed reactions with a simple 30 or 60° rotation. Leveraging superhydrophobic coating, a high fluid transport efficiency of 92.78% is achieved without observable leaking. Integrated with an intracellular fluorescence assay, an on-chip detection limit of 1.8 × 106 CFU/mL is achieved for ampicillin-resistant Escherichia coli (E. coli), which is similar to our off-chip results. We also develop a computer vision method to automatically distinguish positive and negative samples on the chip, showing 100% accuracy. Our Rotation-Chip is simple, low-cost, high-throughput, and can display test results with a single chip image, making it ideal for various multiplexing POC applications in resource-limited settings.

Numerical Control Machining Process Parameters of Nickel-based Alloy Casing

As a major component of engine, aviation casing is usually machined from superalloy blanks because it needs to withstand high temperature, high pressure and other environments during use. Surface integrity indexes such as surface roughness and residual stress will seriously restrict the performance and service life of such parts. In this paper, by studying the processing technology of the casing, Deform-3d software was applied to conduct three-dimensional dynamic modelling for the rough milling process of the casing made of nickel-based alloy GH4169, and the milling simulation analysis was carried out. With milling force and milling temperature as evaluation indexes, the variation rules of force and temperature under different coaxial and radial milling depths were obtained through simulation analysis. At the same time, the single factor optimization was carried out for different coaxial and radial milling depths according to evaluation indexes, and a set of optimal milling parameters were obtained, which provided theoretical guidance for practical machining.

Self-Progressive Near-Field Focusing 2-D Full Frequency Scanning Slot Array Antenna Based on Ridge-Gap Waveguide

A millimeter-wave self-progressive near-field focusing (NFF) 2-D full frequency scanning ridge-gap waveguide slot array antenna is proposed in this article. In the proposed array antenna, the adjacent linear NFF leaky-wave antennas (LWAs) are connected end to end in a double layer folded structure to construct a self-progressive phase shift feed network. Thus, the desired high phase progression in the design of the 2-D frequency scanning is provided by the linear NFF LWAs themselves with the superposed propagation length. As a result, the NFF 2-D frequency scanning performance can be achieved without increasing antenna area based on the proposed high-density self-progressive feed network, which is built by applying the ridge-gap waveguide. The proposed antenna prototype is fabricated through the computer numerical control (CNC) machining process and is verified by the experiment. The test information of the object under test in the near-field region can be directly obtained with a certain frequency band, showing good promise in the near-field detection application.

Methodology for the development of augmented reality applications for the elimination of errors in the interpretation of manufacturing drawings

Nowadays, precision machining processes have been widely used in the manufacture of products mainly focused on aerospace, automotive, mold manufacturing and various types of products that demand high production volumes, precision and surface quality. However, these manufacturing processes are not exempt from errors and failures during the machining process. Recent studies found that the main errors are due to the interpretation of the information in the manufacturing drawings due to lack of experience or training of the personnel. This research project aims to eliminate errors due to misinterpretation of information in Computer Numerical Control (CNC) machining processes through the implementation of a methodology to develop Augmented Reality (AR) applications for the interpretation of manufacturing drawings. Its main contribution is to provide virtual support to the operating personnel obtaining multiple benefits such as reducing downtime due to failures in the machining process, ensuring the optimal operation of the machines, avoiding collisions of the tools and ensuring the quality of the products.

Methodology to eliminate errors in machining processes through Augmented Reality Applications

Today, precision machining companies must implement new technologies to meet the requirements of the aerospace and automotive industries to maintain their competitiveness in the market and create new business opportunities. It has currently been observed that machining companies face errors in their processes that affect the quality of the products, the operation of the process, the useful life of the machines, and the tools. This research project aims to develop a methodology to reduce errors in Computerized Numerical Control machining (CNC) processes with Augmented Reality Applications (AR-Apps) that help operators interpret manufacturing drawings and assembly operations optimize Setup operations and ensure the reliability of the machining. It begins with a questionnaire applied to a sample of machining companies from Chihuahua Mexico City. To identify the factors that generate critical errors in the machining processes. The main results obtained in this paper are reduction of downtime, elimination of scrap, increment cutting tools life, optimize the machining process, increase productivity, and improved product quality. AR -apps implementation offers a low-cost alternative solution that operators and technicians can use very easily because it is a powerful tool that helps work more efficiently through virtual environments, reducing errors and optimizing the process of machining.

Fast-Update Iterative Learning Control for Performance Enhancement With Application to Motion Systems

Iterative learning control (ILC) algorithms are typically used to improve the performance of repetitive processes. Numerous successful applications of ILC, such as computer numerical control (CNC) machining processes, robot manipulation, and lithography processes, have been reported. However, ILC often exhibits less than satisfactory performance since the control unit operates at a limited sampling rate in consideration of cost. In this study, the multiloop, multirate structure of servo motor control systems is taken advantage of and a fast-update multirate ILC (FILC) scheme is proposed for high-accuracy trajectory tracking, where compensation is updated at a rate faster than that of the original position loop without the need for redesigning the feedback controller. The key difficulty related to the design of the FILC lies in the extremely complex and time-varying dynamics inherent in multirate systems. To address this problem, a novel equivalent single-rate parametric model description of the multirate system is derived, which enables the use of the efficient norm optimal ILC algorithm. Consequently, a computationally efficient FILC is obtained to improve the performance. Subsequently, the proposed FILC is applied to the position control of the CNC motion stage. Simulation and experimental results are used to verify the effectiveness of the proposed method.

Efficiency Research of Conformal Channel Geometries Produced by Additive Manufacturing in Plastic Injection Mold Cores (Inserts) Used in Automotive Industry.

In the production of geometries that traditional methods cannot produce, it is seen that additive manufacturing (AM) technology, which has come to the fore, has been used extensively in conformal cooling channel (CCC) applications in recent years. This study, conducted within the scope of CCC's use of applied mold cores in automotive industry plastic part production, aimed to reduce the cycle time in the injection printing process. The v1 geometry, which gives the analysis results for ideal printing quality from the channel geometries developed with three different design approaches, is produced with direct metal laser sintering, which is an AM laser sintering technology, and the injection printing cycle time has been reduced by 38%. CCC applied the study's primary motivation to develop duct geometry to provide balanced cooling in the automotive industry's mold cores produced with AM. It is known that the Computer Numerical Control machining process in traditional mold methods does not allow the processing of the channels in the internal geometries, and the deep areas where the heat is concentrated cannot be cooled sufficiently. In the study, CCC geometries where AM design parameters are used effectively and the balanced cooling performance expected from the die core can be achieved. The effects of different geometries on production are discussed.

Computer aided machining fixture design algorithm and software based on case learning for near-net-shaped jet engine blade

In the blade computer numerical control (CNC) machining process, the machining fixture design for each type of blade will be a very difficult task due to the large variety and quantity of blades. This study proposes a computer-aided machining fixture design algorithm and software based on case learning. The three-dimensional model of the case learning blade and the target blade is firstly projected into a cylinder surface based on the projection algorithm, and the cylinder surface is expanded into a two-dimensional plane based on expansion algorithm. Then, the matching machining fixture layout of the target blade relative to the case learning blade is obtained in two-dimensional space by the mesh affine transformation algorithm according to the geometric edge constraints and the matching relationship of the case learning blade and target blade. At last, the inverse transformations of projection and expansion are used to obtain the final fixture layout of the target blade three-dimensional model based on the previous projection and expansion relationship. The special computer-aided fixture design software of near-net shaped blade is developed, and the machining fixture design and CNC machining experiment based on two typical near-net-shaped blades proved that the computer-aided fixture design algorithm based on case learning is feasible and can be used to realize the intelligent design of near-net-shaped blade machining fixture.

Turning tool life reliability analysis based on approximate Bayesian theory

Turning tool is a critical part of numerical control machining, and its reliability directly affects machining efficiency and stability of the entire system. Turning tool life reliability analysis has major theoretical and practical significance. Laboratory test data or information monitoring are one of commonly employed methods for assessing mechanical performance. This information is conducive for determining mechanical property distribution and assessing the structural reliability. Therefore, experimental monitoring data is incorporated into turning tool life reliability analysis to obtain adequate evaluation results. Considering tool wear process uncertainty, reliability model based on Taylor tool life equation is proposed. Approximate Bayesian theory is introduced to update reliability model parameters. According to obtained parameter posterior samples, tool life reliability is analyzed via Monte Carlo simulation. Effectiveness of the proposed method is validated against experimental samples of turning tool wear. Results of tool life reliability analysis are in accordance with the actual working conditions, which provides a theoretical basis for the selection of numerical control machining process parameters and tool replacement strategies.

Design of a Bézier-Profile Plate for Closely Spaced Multimodal Vibration Energy Harvesting

A cubic Bézier-profile plate for multimodal vibration energy harvesting was developed. The design of the plate was based on an optimization procedure in which the profile of the plate was optimized via the parameters of a cubic Bézier curve to meet the requirements. The multimodal energy harvesting of the plate exploited its first bending mode and its first twisting mode. The conversion of vibration energy into electrical energy was by electromagnetic induction with a magnet attached to a corner of the plate. These two closely spaced vibration modes achieved the multi-modal energy harvesting of the device. Prototypes of the device were manufactured using a numerical-control machining process. The experimental results were in good agreement with the design specifications. With the same base lengths, height, and thickness, the maximum von Mises stress of the proposed plate was much lower due to its bell-shaped profile. The cubic Bézier curve chosen for the plate profile was effective for design of the closely-spaced multimodal vibration energy harvester. With the flexibility of its controllable parametric curve, a high design freedom of the energy harvester with specified frequency ratios could be achieved.

Process design and optimization Of Two-Stage Axial Micro Turbine Air motor

Based on the general machining center, a high-efficiency, mass-produced micro-turbine processing technology and a process scheme for improving metal removal rate are proposed to improve and optimize the metal removal rate of micro-machining. Taking the two-stage axial micro turbine air motor as the research object, the machining route of clamping the nozzle rotor is developed, and the metal removal rates of the turbine with the expandable surface when using the rounded end mill and the ball end mill are compared by the single factor analysis method. In the three processes of rough milling blade passage, finish milling blade and finish milling wheel hub, the metal removal rate of the rounded end mill is 2.96 times, 10 times and 2 times higher than the ball end mill, respectively. The Swarf finishing strategy of Powermill software is used to processing the side and bottom of the blade both roughly and accurately, and the suitable cutting parameters and side edge machining parameters are obtained. The surface roughness Ra of the side and bottom surfaces of the blade is tested to be 0.4-0.8μm using a surface roughness measuring instrument SJ-410. With the trigger type probe of the model Omp60, the dimensional tolerance of the blade is within 0.015 mm, and the coaxiality is from 0.002 to 0.003mm. The two-stage axial-type micro turbine air motor is assembled by the hot-packing method, and the torque and rotor speed are tested by a special platform. The results show that when the compressed air inlet pressure is 300KPa, the designed maximum speed of 100000rpm is satisfied, which meets the design requirement of the turbine motor and verifies that the numerical control machining process plan is feasible. Besides, the side edge machining of the blade using the cylindrical milling cutter is efficient to improve the metal removal rate. It also lays a foundation for the mass production of the two-stage axial-type micro turbine air motor.

Crossing principle-based feedrate planning method for 5-axis CNC milling with drive constraints

ABSTRACT The careful selection of the feedrate is necessary during the numerical control (NC) machining process, to prevent drive characteristics such as velocity, acceleration, and jerk of each axis from exceeding their physical limits, as this can damage the structure of the machine tool. In this study, a new off-line feedrate interpolation method based on the constraint intersection principle is proposed that can satisfy the chord error, velocity, acceleration, and jerk limits of the drive axes. The relationship between the feedrate of the cutter tip and the driving characteristics of each axis is established by changing the kinematics formula. Based on the principle of cross-constraints, the constraint overshoot of the driving axis in a sensitive area on the feed path can be prevented effectively, thereby ensuring the geometric accuracy and machine drive characteristics. Furthermore, illustrated examples are provided to validate the feasibility and applicability of the proposed feedrate interpolation method.

Parameters optimization considering the trade-off between cutting power and MRR based on Linear Decreasing Particle Swarm Algorithm in milling

As mechanical engineering is growing a current and urgent issue is rising in the manufacturing process, which is the ability to improve efficiency while reducing energy consumption during the processes. Cutting parameters are an important part of the computer numerical control (CNC) machining process, so a reasonable selection of cutting parameters can significantly enhance the machine’s energy efficiency. Previous studies mainly focused on cutting power (P) and material removal rate (MRR), respectively, without considering the trade-off relationship between them; In this paper, an optimization model of cutting parameters is developed by establishing a multi-objective model where P and MRR are identified. The proposed model utilizes the Grey Correlation Analysis (GRA) and experimental to determine the weight of the objective. After the Linear Decreasing Particle Swarm (LDPS) optimization algorithm was utilized to solve the model, several application cases are given and their results demonstrate the ability of our method through comparing with the traditional approach.

Analysis of machining deformation for adaptive CNC machining technology of near-net-shaped jet engine blade

Near-net-shaped process (NNSP) will be one of important development trend in TC4 blade manufacturing, while the blade precise machining after NNSP is a challenge. This paper analyzes the machining deformation of blade under the rigid-flexible coupling fixture (RFCF) and adaptive computer numerical control machining process (ACNCMP). Firstly, positioning and clamping scheme (PCS) of RFCF were introduced and analyzed, especially new material (PEEK-GF30) and new structure of RFCF were used in fixture manufacturing. Secondly, theoretical mechanic model of RFCF and blade was proposed. Thirdly, empirical model of cutting force and the main cutting parameters was established and analyzed by orthogonal cutting experiment (OCE). Fourthly, blade deformation under cutting force was analyzed by FEA and experiment. The results show that blade cutting deformation is a whole elastic deformation of 0.056mm which is acceptable for machining accuracy target of 0.1mm. The proposed RFCF can protect blade from occurring local deformation, and is of better application value in ACNCMP of NNSP blade.

Data-Driven Anomaly Diagnosis for Machining Processes

To achieve zero-defect production during computer numerical control (CNC) machining processes, it is imperative to develop effective diagnosis systems to detect anomalies efficiently. However, due to the dynamic conditions of the machine and tooling during machining processes, the relevant diagnosis systems currently adopted in industries are incompetent. To address this issue, this paper presents a novel data-driven diagnosis system for anomalies. In this system, power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis. To facilitate the analysis, preprocessing mechanisms have been designed to de-noise, normalize, and align the monitored data. Important features are extracted from the monitored data and thresholds are defined to identify anomalies. Considering the dynamic conditions of the machine and tooling during machining processes, the thresholds used to identify anomalies can vary. Based on historical data, the values of thresholds are optimized using a fruit fly optimization (FFO) algorithm to achieve more accurate detection. Practical case studies were used to validate the system, thereby demonstrating the potential and effectiveness of the system for industrial applications.

On-line part deformation prediction based on deep learning

Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a conventional neural network and a recurrent neural network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data.

Energy consumption analysis of automatic tool change processes

The automatic tool change system was used frequently in numerical control machining processes. Preliminary studies indicate that the power demand of an automatic tool change system is an important part of auxiliary units power demand for numerical control machining processes. Research to date on energy consumption modeling largely focuses on cutting processes while the power demand of automatic tool change processes is treated as a given constant. However, energy consumption modeling of automatic tool change processes is a method that has been overlooked in previous investigations. In fact, the tool change power demand and time vary depending on the turret motor rotation angle for changing tools. Thus, this study aims at solving the above problem by analyzing the power demands of different tool stations for changing tools. The energy consumption modeling method of an umbrella tool changer system was proposed to address the limitations of existing models and validated on a vertical milling machine. In this study, one tool change cycle was divided into five different power demand stages. This is different from the common method where the power demand of one tool change cycle is fixed. The milling case was investigated, and experimental results showed that the proposed modeling method can provide a reliable energy consumption prediction for tool change processes with high accuracy, which can assist manufacturers to develop potential energy saving strategies.

Application of multivariate statistical analysis for CNC milling of large Ti-6Al-4V components

Large-scale aircraft components made from difficult-to-machine materials such as titanium alloy Ti-6Al-4V, present a challenge to machining. Typical aircraft component features such as high thin walls or deep pockets, cause significant problems with cutter vibrations and chatter as a consequence of material properties. Premature failure of the cutting tools and damage of the machined surface can be minimised by selecting an appropriate machining strategy with optimised operational parameters. The aim of the research reported in this paper is to perform an exploratory case study through the use of a multivariate analysis technique known as Principal Component Analysis to graphically visualise the relationship between the variables of cutting speed, feed rate, radial depth of cut, shank length, milled surface quality and tool wear during peripheral milling of a workpiece made from annealed Ti-6Al-4V. The results of the research identify that the relationships between these variables are validated with that found in the literature. It is shown that there is significant potential in employing Principal Component Analysis as a data mining technique during computer numerical control (CNC) machining processes especially when large data sets are available.

An Effective Numerical Control Machining Process Optimization Approach of Part With Complex Pockets for Numerical Control Process Reuse

Recently, various process reuse technologies have come to be widely used in manufacturing. The existing methods mainly focus on searching and reusing similar NC processes. For the optimization of process schemes generated by reusable NC machining processes, however, no effective strategy has yet been proposed, especially when it comes to complex parts with intersecting features. To address this issue, we present, here, an effective NC machining process optimization approach of the part with complex pockets for NC process reuse. This approach involves two levels, that of the machining feature and that of the part. In the former case, the cutting tool and a cutting depth of the machining region is first optimized by making full use of the tool's machining capacity and avoiding air-cutting. In the latter case, the cutting sequence of the machining regions is further optimized in the macro process so as to maintain the efficiency of the process scheme. The effectiveness of the proposed approach is verified by a jag of the electronic product and an aircraft structural in our developed prototype system based on CATIA. The presented method can be integrated into commercial CAD/CAM software for rapid NC programming.