Machining Path Research Articles

An Improved Mayfly Optimization Algorithm for Type-2 Multi-Objective Integrated Process Planning and Scheduling

The type-2 multi-objective integrated process planning and scheduling problem, as an NP-hard problem, is required to deal with both process planning and job shop scheduling, and to generate optimal schedules while planning optimal machining paths for the workpieces. For the type-2 multi-objective integrated process planning and scheduling problem, a mathematical model with the minimization objectives of makespan, total machine load, and critical machine load is developed. A multi-objective mayfly optimization algorithm with decomposition and adaptive neighborhood search is designed to solve this problem. The algorithm uses two forms of encoding, a transformation scheme designed to allow the two codes to switch between each other during evolution, and a hybrid population initialization strategy designed to improve the quality of the initial solution while taking into account diversity. In addition, an adaptive neighborhood search cycle based on the average distance of the Pareto optimal set to the ideal point is designed to improve the algorithm’s merit-seeking ability while maintaining the diversity of the population. The proposed encoding and decoding scheme can better transform the continuous optimization algorithm to apply to the combinatorial optimization problem. Finally, it is experimentally verified that the proposed algorithm achieves better experimental results and can effectively deal with type-2 MOIPPS.

A Study on Metallographic and Machining Characteristics of Functionally Graded Material Produced by Directed Energy Deposition

Directed energy deposition (DED) stands as a key process in metal additive manufacturing (AM) and offers the unique capability of creating functionally graded materials (FGMs). FGMs have garnered significant interest in high-value industries by advantages such as performance optimization, reducing material defects, and resolving joining issues. However, post-processing remains a crucial step, indicating a need for further research to understand the machinability of FGMs. This paper focuses on the characteristics analysis of fabricating and machining an FGM based on stainless steel 316L (SAE 316L) and Inconel 718. The FGM was fabricated by starting with SAE 316L at 100 wt.% and adjusting the composition ratio by incrementally increasing Inconel 718 by 20 wt.% while simultaneously decreasing SAE 316L. Following the FGM fabrication, microstructure and mechanical properties were comprehensively analyzed by hardness testing, optical microstructure measurements, energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). To investigate the post-processing aspects, end-milling experiments were conducted using two distinct milling methods (upward and downward milling) and machining paths (from SAE 316L towards Inconel 718, and vice versa). The mean cutting force peaked at 148.4 N in upward milling and dipped to 70.5 N in downward milling, and tool wear measurements further provided insights into the optimal milling direction when working with an FGM of SAE 316L and Inconel 718.

Control of corner arc radius in wire electrochemical micromachining

Wire electrochemical micromachining (WECMM) technology is commonly used to process micro parts. Some thick parts contain many sharp structures, and have high machining accuracy requirements for the arc radius at the corner, such as jet sheet, micro gear, etc. However, when machining thick workpiece, it is easy to form a large arc radius at the corner due to stray corrosion and difficult discharge of electrolytic products. In order to solve this problem, this paper proposes a corner path optimization method, which reduces the arc radius of the sharp structure by adjusting the processing path of the wire electrode, thereby improving the machining accuracy of the sharp structures. The model of the relationship between the inclination of the machining path and the angle of the sharp structure is established. The inclination and offset distance of the processing path are obtained through theoretical calculation, and further optimized through experiments to avoid overcut or undercut at the corner. In the experiment, tungsten wires with different diameters were used as wire electrodes to process acute angle, right angle and obtuse angle structures on 510 μm thick 9Cr18Mo stainless steel. The influence of wire electrode processing path on the corner arc radius of different angle sharp structures was explored. The experimental results show that compared with the original path, the arc radius of the sharp structure could be reduced from tens of microns to <5 μm by using the optimized processing path.

Towards region-based robotic machining system from perspective of intelligent manufacturing: A technology framework with case study

The region-based robotic machining system is mainly used for repair and remanufacturing of the complex components with local defects. Taking the automotive body manufacturing as an example, the resistance spot welding process is highly prone to randomly distributed and tiny-sized welding slag splashes, which poses a great challenge to the locally automated removal of the welding slag defects. In this paper, we construct a region-based robotic machining system that integrates the processes of defect detection, defect region division and positioning, and machining path decision-making, aiming to enhance the automation of locally grinding of automotive body welding slags. Both the framework of the system and the implementation process are proposed. In the framework, the improved YOLO v3 algorithm is put forward to accurately detect the automotive body welding slags by virtue of the machine vision and deep learning. Based on this, the K-means algorithm is used to divide the welding slags into the regions, so that the problem of positioning individual welding slag is transformed into the problem of positioning the boundary points of welding slag region. The binocular image-based region boundary points matching is then presented to accurately locate the welding slag regions accordingly. The Dijkstra algorithm is finally employed to make an autonomous decision on the pre-planned grinding path of automotive body welding slags based on the positioning results. The experiments on a case of locally robotic grinding of the welding slags on an automotive door frame are implemented to verify the effectiveness and practicality of the system. This study provides a valuable reference for the locally automated repair of automotive body welding slag defects, putty defects, paint defects, etc.

Research on Continuous Machining Strategy for Five-Axis Machine Tool: Five-Axis Linkage to Four-Axis Linkage

The article presents a novel strategy for enhancing the efficiency of machines that are used for complex structure machining. It proposes a low-cost five-axis four-linkage milling system as an alternative to the more expensive five-axis five-linkage system. Kinematic analysis of the machine tool is conducted to establish a correlation between the tool location data and the displacement of kinematic axes. An interpolation algorithm is then devised to determine a four-axis linkage milling strategy. The theoretical errors of the interpolation trajectory are observed to be reduced following the transformation. The research employs impeller processing as a case study, wherein the five-axis linkage machining path is translated into a more efficient five-axis four-linkage path using the interpolation algorithm. The practical application of this novel milling strategy confirms its effectiveness in processing the integral impeller within acceptable efficiency parameters. The results provide a theoretical foundation for the practical application of the low-cost five-axis four-linkage machining strategy in high-precision five-axis five-linkage machine tools.

One way to provide effective healthcare services for patients with advanced cancer to improve their quality of life and survival: Prediction modeling using machine learning techniques and causal relationship between self-management strategy, health-related quality of life, and survival.

e24005 Background: In healthcare services, self-management strategies can play a crucial role in enhancing the health-related quality of life (HRQoL) or survival of cancer patients, regardless of their stage or treatment plan. However, there needs to be more research addressing the relationship between the strategies, HRQoL, and survival. To address this gap, we investigated the relationship between the strategies, HRQoL, and survival using machine learning techniques (MLT) and path analysis with structural equation modeling (SEM). Methods: This study was a secondary analysis of a randomized controlled trial (RCT) to provide early palliative care to individuals with advanced cancer (ClinicalTrials.gov, ID NCT03181854). We employed dimensional multiple Cox proportional hazard regression analyses to identify the crucial predictors of 1-year survival. By integrating the critical predictors extracted from the Cox regression models, we established a final survival prediction model using the eXtreme Gradient Boost (XGBoost) and the BorutaSHAP in MLT. Furthermore, we used path analysis to investigate the causal relationship between the strategies, HRQoL, and survival. Results: The univariate dimensional Cox models indicated that factors such as worse ECOG performance status (hazard ratio (HR) = 2.18), living alone (HR = 1.74), male (HR = 1.65), low global QoL (HR = 1.81), high dyspnea (HR = 2.30), high pain (HR = 1.75), high appetite loss (HR = 1.94), high constipation (HR = 2.11), high depression (HR = 2.79) at baseline, and no significant positive changes in emotional functioning (HR = 1.88) negatively impacted survival. The final XGBoost model for survival prediction using repeated K-fold methods (K = 5, repeats = 20, area under the receiver operating characteristic curve (AUROC) = 0.76, area under the precision-recall curve (AUPRC) = 0.87) and bootstrapping (n = 300, AUROC = 0.81, AUPRC = 0.91) showed high survival prediction performance. Additionally, the path analysis revealed a causal relationship between self-management strategies, HRQoL, and survival in patients with advanced cancer ( p-value = 0.005). HRQoL was found to have a complete mediating relationship between the strategies and survival. Self-management strategies did not directly correlate with survival but showed an impact on survival through HRQoL. Conclusions: To the best of our knowledge, this is the first study to uncover the connection between self-management strategies, HRQoL, and survival in patients with advanced cancer using MLT and path analysis. These findings have the potential to deepen our understanding of self-management strategies and aid healthcare providers in providing improved cancer care for patients with advanced cancer. Clinical trial information: NCT03181854 .

Application of fuzzy control based feed rate strategy design in NC machining error automatic compensation

With the continuous development of manufacturing industry, the application range of NC machining technology has been further expanded. The contour accuracy is strongly related to the NC machining quality as a key machine tool performance indicator. Its application efficiency is plainly low as the majority of offline compensation-based contour accuracy adjustments rely heavily on manual experience. Moreover, the isolated research on automatic error compensation and its combination with algorithms does not start with the characteristics of contour accuracy in data processing. Therefore, based on the advantages of strong the robustness of the fuzzy algorithm and the high effectiveness of parameter adjustment, an automatic compensation method for NC machining contour error based on fuzzy control is proposed. The contour error prediction model is designed according to the machining path, and then the automatic compensation strategy for contour error under fuzzy control is designed based on the feed speed. The results showed that under this method, the contour error can reach a maximum of 0.06 and a minimum of 0.025, which was 0.015 lower than the minimum contour error of genetic algorithm. This indicated that the method greatly reduced the CNC machining contour error and improved the contour accuracy, as well as reducing the time cost of contour error compensation, improving the efficiency of contour error compensation, and realizing the automation of error compensation control capability. This is helpful for advancing CNC machining automation technology and supporting the intelligent development of machinery manufacturing.

Automatic joint motion planning of 9-DOF robot based on redundancy optimization for wheel hub polishing

Automatic joint motion planning is very important in robotic wheel hub polishing systems. Higher flexibility is achieved based on the joint configuration with multiple solutions, which means that the robot has kinematic redundancy for machining tasks. Redundant joints can be used to optimize the motion of the robot, but less research has been done on multi-dimensional redundant optimization. In this paper, a 6-axis robot with a 3-axis actuator is designed for wheel hub polishing. We propose an automatic joint motion planning method for a nine-axis industrial robot to achieve the shortest processing time. Firstly, offline programming is designed to generate paths for the complex surface of the hub. In order to reduce the machining path points on the surface of the hub, a improved Douglas-Peucker (DP) algorithm is proposed, which can take into account the change of the path point posture. Secondly, the Greedy Best First Search (GBFS) and Sine cosine algorithm (SCA) are combined to find the optimal joint motion efficiently. Moreover, we use nested SCA for comparison to test whether the combined algorithm can avoid local optima. Finally, the performance and computational efficiency of the method are validated in both simulation and real environments based on the hub surface.

Micro-Shaping of Pure Aluminum in Long-Duration Wire Electrochemical Micromachining Using Bipolar Nanosecond Pulses.

With the increasing application of three-dimensional pure aluminum microstructures in micro-electromechanical systems (MEMS) and for fabricating terahertz components, high-quality micro-shaping of pure aluminum has gradually attracted attention. Recently, high-quality three-dimensional microstructures of pure aluminum with a short machining path have been obtained through wire electrochemical micromachining (WECMM), owing to its sub-micrometer-scale machining precision. However, machining accuracy and stability decrease owing to the adhesion of insoluble products on the surface of the wire electrode in long-duration WECMM, which limits the application of pure aluminum microstructures with a long machining path. In this study, the bipolar nanosecond pulses are used to improve the machining accuracy and stability in long-duration WECMM of pure aluminum. A negative voltage of -0.5 V was considered appropriate based on experimental results. Compared with the traditional WECMM using unipolar pulses, the machining accuracy of the machined micro-slit and the duration of stable machining were significantly improved in long-duration WECMM using bipolar nanosecond pulses.

Machine Tool Equipped with a Counter Balance Feed Drive in the Z Direction with Right and Left Ball Screws and the Influence of Anisotropic Mechanical Behavior on Small-Diameter Drilling Processes

In recent years, electronic devices have become lighter, thinner, shorter, smaller, and more multifunctional, motivating micro drilling technology to become more sophisticated. To meet the demands of electronic applications, this paper proposes a drilling machine tool with a counterbalance vibration control mechanism. In this report, focusing on the timing of table feed rate changes during drilling, drilling experiments were conducted using four drilling paths while changing the table movement direction and feed rate along the machining path. The accuracy of the positions of the drilled holes and condition of the drill edge after drilling were then evaluated.

A simplified analytical method for the computation of machine path in filament winding of axisymmetric structures: a case study on a bell-contoured rocket nozzle extension

A simplified analytical method for the computation of machine path in filament winding of axisymmetric structures: a case study on a bell-contoured rocket nozzle extension

On Planning Algorithms for Free-Form Surface Machining Path under Fractal Theory

On Planning Algorithms for Free-Form Surface Machining Path under Fractal Theory

The effects of coolant on the cutting temperature, surface roughness and tool wear in turning operations of Ti6Al4V alloy

The titanium alloys are widely used in aeronautical engineering and medical device materials due to exceptional mechanical properties such as tensile resistance and toughness of fractures. High thermo-mechanical loads occur in metal cutting of Titanium alloy Ti6Al4V, which can decrease life of cutting tool and increase cost of part production. In this paper, the coolant effects on the cutting temperature, surface roughness and tool wear are investigated by using the developed virtual machining system. The cutting forces during turning operations of Ti6Al4V alloy are accurately calculated in order to be used in calculation of cutting temperature and tool wear. The modified Johnson–Cook methodology is utilized to obtain the cutting temperatures along machining paths. Then, the Coupled Eulerian-Lagrangian (CEL) approach is investigated to predict and evaluate the effects of coolants on the cutting temperature in turning operations of Ti6Al4V alloy. The finite element approach is employed to predict tool wear by using the Takeyama–Murata analytical model and modifying the cutting tool geometry during the chip production process. To verify the developed methodology in the study, the results of experiments for the measured cutting temperatures, surface quality and wear rate are compared to the results of virtual machining system obtained by the finite element simulation. Thus, utilizing the proposed virtual machining system in the study, cutting temperatures, surface quality and tool wear during the turning operations of Ti6Al4V alloys with and without coolant can be accurately predicted to enhance the accuracy as well as productivity in the CNC machining operations.

Research on discretization algorithm of free-form surface for robotic polishing

Traditional surface processing measures for free-form surface parts are unable to distinguish the curvature of the surface to be machined, nor can they identify the concave and convex characteristics of different areas of the surface, as well as the time-varying characteristics of the contact between the tool and the surface of the part, making it impossible to tailor the processing parameters to the different mathematical characteristics of each area of the part surface. The result is that different areas of the same surface cannot be machined uniformly. In order to solve the above problems, this study proposes a discrete partition algorithm for free-form surface parts from the perspective of free-form surface parts simplification. The algorithm takes the spatial coordinates of each triangular patch after meshing the 3D model of the free-form surface part as input parameters. The partition of the free-form surface part is realized by the criterion for determining the spatial position of triangular patches, the method for detecting overlapping edges of triangular facets based on the principle of co-linear determination and the strategy for determining the difference of normal vectors between triangular patches. The 3D solid model after the corresponding surface partition is further obtained by reverse modeling. In addition, in order to effectively improve the machining efficiency of the free-form surface parts after partition, this study proposes an optimal machining path planning algorithm based on the principle of genetic algorithm. This algorithm ensures that the processing equipment can finish each sub-surface and plan the polishing path based on the results of surface partition, and then achieving the complete polishing process of the surface in the shortest time. Finally, the simulation software was used to simulate and analyze the surface polishing quality before and after the partition processing, which proved that the proposed surface partition algorithm can effectively improve the machining accuracy of free-form parts.

Complex parts machining path planning through cross-source point cloud processing

When cleaning cast parts, production machinery such as robots and smart machines must be able to operate tools along appropriate machining paths. Planning the machining path using 3D point cloud processing techniques is a suitable approach. The local point clouds generated from in situ scans and the standard point clouds, however, differ in scale and accuracy, and these issues make the registration of cross-source point clouds a difficult academic topic. The study presented here suggests a novel cross-source point cloud registration technique that combines a point cloud slicing algorithm and a curve fitting algorithm to plan machining paths for complex cast parts. The results demonstrate the practicality of the proposed method, with the uniformity of the surface profile and the operational effectiveness of the machining process being 57.90% and 67.74% higher, respectively, for the machining paths generated using the proposed method compared to the manual method. By comparing trials of various machining path curve fitting algorithms, we find that the Fourier fitting approach performs best. In addition, the co-planar features chosen for coarse registration will be better registered if they are closer to the plane containing the intended machining path. The proposed method may be useful for online inspection by UAVs and interactive robots.

Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics

Proton hopping is a key reactive process within zeolite catalysis. However, the accurate determination of its kinetics poses major challenges both for theoreticians and experimentalists. Nuclear quantum effects (NQEs) are known to influence the structure and dynamics of protons, but their rigorous inclusion through the path integral molecular dynamics (PIMD) formalism was so far beyond reach for zeolite catalyzed processes due to the excessive computational cost of evaluating all forces and energies at the Density Functional Theory (DFT) level. Herein, we overcome this limitation by training first a reactive machine learning potential (MLP) that can reproduce with high fidelity the DFT potential energy surface of proton hopping around the first Al coordination sphere in the H-CHA zeolite. The MLP offers an immense computational speedup, enabling us to derive accurate reaction kinetics beyond standard transition state theory for the proton hopping reaction. Overall, more than 0.6 μs of simulation time was needed, which is far beyond reach of any standard DFT approach. NQEs are found to significantly impact the proton hopping kinetics up to ~473 K. Moreover, PIMD simulations with deuterium can be performed without any additional training to compute kinetic isotope effects over a broad range of temperatures.

Carbon footprint-based optimization method for remanufacturing machining paths

Carbon footprint-based optimization method for remanufacturing machining paths

Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art

This article presents the current state-of-the-art research on applying artificial intelligence (AI) technology in smart greenhouses to optimize crop yields, water, and fertilizer use efficiency, to reduce pest and disease, and to enhance agricultural sustainability. The key technologies of interest were robotic systems for pesticide application, irrigation, harvesting, bio-inspired algorithms for the automation of greenhouse processes, energy management, machine path planning and operation of UAVs (unmanned aerial vehicles), resolution of scheduling problems, and image signal processing for pest and disease diagnosis. Additionally, the review investigated the cost benefits of various energy-management and AI-based energy-saving technologies, the integration of photovoltaics and dynamic pricing based on real-time and time-of-use metrics, and the cost benefits of LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, and RFID (radiofrequency identification) technologies. The review established that commercially viable AI technologies for agriculture had increased exponentially. For example, AI-based irrigation and soil fertilizer application enabled farmers to realize higher returns on investment on fertilizer application and gross returns above the fertilizer cost, higher yields, and resource use efficiency. Similarly, AI image detection techniques led to the early diagnosis of powdery mildew. The precise operation of agricultural robots was supported by the integration of light imaging, detection, and ranging (LIDAR) optical and electro-optical cameras in place of the traditional GPS (geographic positioning systems) technologies, which are prone to errors. However, critical challenges remained unresolved, including cost, disparities between research and development (R&amp;D) innovations and technology commercialization, energy use, the tradeoff between accuracy and computational speeds, and technology gaps between the Global North and South. In general, the value of this review is that it surveys the literature on the maturity level of various AI technologies in smart greenhouses and offers a state-of-the-art picture of how far the technologies have successfully been applied in agriculture and what can be done to optimize their usability.

The use of the rapid unequal interval interpolation method (RUIIM) for a non-RTCP NC program and five-axis machining

In industries such as electronics 3C, medical, aviation, and energy, multi-axis machining machines are an important element of modern production systems, especially for post-processing, which necessitates the safe operation of control equipment. Some controller and software for five-axis simultaneous machining only interpolate equidistantly between cutter location (CL) points and do not support post-processing using a rotation tool center point (RTCP) so the machining path of the tool has manufacturing errors (overcutting or undercutting) and the workpiece fails to be machined to the correct size. Depending on the maximum allowable error (δMax), this study uses rapid unequal interval sub-point interpolation to perform sub-point interpolation on the original path of the five-axis machining machine, and the sub-point values are output in the computer numerical control (CNC) machining program in C# programming language. The tool tip is then moved to the desired machining position. Finally, an example involving helical milling of a curved surface is used to verify the feasibility of the rapid unequal interval interpolation method (RUIIM). The experimental results show that rapid unequal interval sub-point interpolation gives a more accurate original path that is generated in the CAM and a faster processing speed for the entire controller so the operation speed is increased.

A Simplified Analytical Solution for the Computation of Machine Path in Filament Winding of Cylindrical Angle-Ply and Double-Double Structures

This paper presents a simplified computation approach for the machine path (or winding trajectory) of grid structures and tubes with a circular cross-section and angle-ply or double-double layup. The solution for the machine path is given through controllable degrees of freedom of a low-cost two-axis filament winding machine (FWM): mandrel rotation and translation of the delivery eye along the axis of the mandrel. The efficiency of the analytical solution for the machine path of the FWM was ascertained by automated laying the cotton thread over the geodesic and non-geodesic groove imprinted on the surface of a cylindrical polylactide mandrel. These results validated the possibility of manufacturing cylindrical composite structures with an angle-ply layup or double-double stacking sequence, without the need for expensive software, making the winding technology accessible to society and promoting university extension.