Tool Path Length Research Articles

Non-Cutting Moving Toolpath Optimization with Elitist Non-Dominated Sorting Genetic Algorithm-II

Path planning (PP) is fundamental in the decision-making and control processes of computer numerical control (CNC) machines, playing a critical role in smart manufacturing research. Apart from improving optimization in PP, enhancing efficiency while decreasing CNC machine cycle time is important in manufacturing. Many methods have been offered in the literature to improve the cycle time for obtaining optimal trajectories in toolpath optimization, but these methods are mostly considered for improvements in path length or machining time in optimal PP. This study demonstrates a method for creating a smoothing path. It aims to minimize both cycle time and toolpath length, while demonstrating that the non-dominated sorting genetic algorithm (NSGA-II) is efficient in addressing the multi-objective PP problems within static situations. Pareto optimality for performance comparisons with multi-objective genetic algorithms (MOGAs) is presented in order to highlight the positive features of the non-dominant solving generated by the NSGA-II. According to the comprehensive analysis results, the optimization of the path carried out with the NSGA-II emphasizes its shorter and smoother attributes, with the optimal trajectory achieving approximately 30% and 7% reductions in path length and machining cycle time, respectively.

LCA involving process planning

In the context of biological transformation, the goal in process planning is to support the user towards more efficient and especially more sustainable processes. A life cycle assessment (LCA) is a tool used to evaluate the environmental impact of a product or process over its entire life cycle. So far, the modelling of a milling process strongly depends on the removed volume. Information available from a state-of-the-art CAM software like tool path length, linking movements and retractions of the machine are neglected. Therefore, this paper methodically investigates the potential for process planning to include LCA models for milling operation to implement more sustainable manufacturing processes.

Effects of Toolpath Parameters on Engagement Angle and Cutting Force in Ellipse-Based Trochoidal Milling of Titanium Alloy Ti-6Al-4V

Trochoidal milling is an efficient strategy for the rough machining of difficult-to-cut materials. The true trochoidal toolpath has C2 continuity and avoids sharp changes in engagement angle and cutting load, resulting in smooth machine tool movement. However, its total length is too long, and its engagement angle is uneven. These factors limit further improvements in the material removal rate. Based on the true trochoidal toolpath model, this paper develops an ellipse-based trochoidal toolpath generation method by introducing a compression ratio in the trochoidal step direction. The analytical model of engagement angle and the mechanistic model of the cutting force are proposed. A series of simulations and milling experiments were conducted to analyze the effects of toolpath parameters on the engagement angle and the cutting force. The results show that the compression ratio has the most significant effects. A compression ratio of 50% is optimal, using which the total toolpath length is reduced by 34.0%, and the variance of the engagement angle is reduced by 31.2% compared with that of the true trochoidal toolpath. The profile of the total cutting force corresponds to that of the engagement angle.

Optimal toolpath planning strategy prediction using machine learning technique

Finding the best toolpath planning strategy for Computer Numerical Control (CNC) machining requires a trial-and-error approach. One needs to follow a number of steps: generating the toolpath, performing simulations/machining, measuring various parameters to quantify the quality of the toolpath, and then selecting the best toolpath. This conventional iterative approach is time-consuming and often error-prone, which is a bottleneck in the current CNC machining industry. This paper presents a novel framework to create a machine learning based system for choosing the best toolpath planning strategy for CNC machining (finishing) of complex freeform surfaces directly from the CAD model. Three tool path planning strategies are considered: Adaptive planar, Iso-scallop, and Hybrid. At first, a novel toolpath analysis module is presented to evaluate the quality of the toolpath considering three performance parameters: surface finish, toolpath length, and smoothness. This quality measurement technique is extensively tested for robustness and accuracy. It is then used to analyze and label a large number of CAD models to create a dataset for supervised learning. Finally, a Convolutional Neural Network (CNN) is designed and trained using this dataset to predict the best toolpath planning strategy. Compared to the conventional approaches, the developed system uses multiple performance parameters and chooses the best strategy directly from the CAD model. Results show that the proposed data-driven model achieved 96.8% test accuracy.

Increasing the Performance of Computer Numerical Control Machine via the Dhouib-Matrix-4 Metaheuristic

The Computer Numerical Control (CNC) machine represents a turning point in today's production which has high requirements for product accuracy. The CNC machine enables a high flexibility in work and time saving and also reduces the time required for product accuracy control. Moreover, the CNC machine are used for several activities, most often for turning, drilling and milling operations. Usually, the productivity of any CNC machine can be increased thanks to the minimization of the non-productive of tool movement. In this paper, the results of a new metaheuristic named Dhouib-Matrix-4 (DM4) with an application on the NP-hard problem based on the Travelling Salesman Problem are presented. DM4 is used for increasing the performance of the CNC Machine by optimizing a tool path length in the drilling process performed on the CNC milling machine. The proposed algorithm (DM4) achieves a solution closed to the optimum, compared with the results obtained with the Ant Colony Optimization algorithm and the results found with the manual programming in G code by using a control unit for the selected CNC milling machine.

COMPARISON OF THE SUCCESS OF META-HEURISTIC ALGORITHMS IN TOOL PATH PLANNING OF COMPUTER NUMERICAL CONTROL MACHINE

Carrying out an engineering process with the least cost and within the shortest time is the basic purpose in many fields of industry. In Computer Numerical Control (CNC) machining, performing a process by following a certain order reduces cost and time of the process. In the literature, there are research works involving varying methods that aim to minimize the length of the CNC machine tool path. In this study, the trajectory that the CNC machine tool follows while drilling holes on a plate was discussed within the Travelling Salesman Problem (TSP). Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) methods were used to solve TSP. The case that the shortest tool path was obtained was determined by changing population size parameter in GA, PSO, and GWO methods. The results were presented in tables.

Minimization of Nonproductive Time in Drilling: A New Tool Path Generation Algorithm for Complex Parts

In computerized tool path programming, the operator/user can generate the tool path based on the shape and geometry of the part to be produced by choosing from a set of predefined strategies available in the library of Computer Aided Manufacturing (CAM) software. These tool paths are typically not optimum, specifically for complex geometries. This paper employed Travelling Salesman Problem (TSP) as a foundation to propose a new tool path optimization algorithm for drilling to minimize the tool path length and subsequently reduce the time spent on nonproductive movements. The proposed algorithm was solved using local search approach in the presence of multiple constraints including geometric obstacles and initial location of tool origin. The outcome was a near-optimum tool path for drilling operations with no collision with workpiece features. The computational efficiency of the proposed algorithm was also compared with other methods in available literature using a standard workpiece as a benchmark. The results confirmed that for given examples, the near-optimum collision-free tool paths using the developed model in this paper were almost 50% shorter than the tool path generated by a commercial CAM software.

Energy performance-oriented design candidate selection approach for additive manufacturing using toolpath length comparison method

To ensure environmental benefits of additive manufacturing (AM), the energy performance should be optimized in the product design for AM (DfAM). We are proposing a new approach to select the design candidate (DC) for AM with the highest energy performance. In the first stage, multiple DCs are generated using topology optimization. In the second stage, the energy performance of each DC is evaluated based on the Toolpath-Length-Comparison (ToPaLC) method, in which the toolpath lengths of all DCs are calculated and compared. The candidate with the minimum path length is selected as the final design solution with the highest energy performance.

Optimization of Tool Path Length on Three-Dimensional Drilling Application Using Ant Colony Algorithm

The lower machining time is important characteristic in the drilling machining process. Drilling process costs will increase if the machining time is high. Therefore, the main objective of this research is to develop Ant Colony Algorithm (ACO) to reduce the machining time by obtain the optimal tool path length. By using this algorithm, it can minimize the tool path length and significantly decreasing the machining time of drilling process. Simulating in 3-dimensional drilling on ACO has been constructed to minimize the shortest path of the drilling process. There are two type of workpiece has been used, which is simple block with 10 holes and complex block design that has 154 holes. ACO algorithm has been developed in Matlab R2017b to determine the optimal parameters of ACO of tool path length in drilling. Besides, simulation also has been done to investigate the effect of ACO parameter which is weight of pheromone (α), weight of trail (β), evaporation coefficient (e), and number of iterations. As a result, by define the parameter of iteration number at 900, the optimum parameter of weight of pheromone (α) is 5, weight of trail (β) is 4 and evaporation coefficient (e) is 0.4. Based on these parameters, the minimal tool path length obtain for simple and complex model are 286.965 mm and 6770.9860 mm respectively. Then, the result of tool path length of ACO simulation has been compared with the Mastercam outcome. ACO achieves a total tool path length of 286.965 mm while Mastercam achieved 569.878 mm for simple block design. Meanwhile, for complex block design, ACO produces a total tool path length of 6770.9860 mm while Mastercam has generate 55828.9050 mm of tool path length. By comparing these two approaches, ACO and Mastercam, ACO has that the short total tool path length by 49.64% on simple block design and 87.87% for complex block design.

Application of the Traveling Salesman Problem in Generating an Optimized Collision-Free Tool Path for CNC Drilling

In drilling, the tool path is usually generated according to the workpiece geometry and the arrangement of holes. To perform drilling, majority of Computer-Aided Manufacturing (CAM) software offer a set of predefined drilling strategies. In this context, tool traveling time between the holes is considered as a non-value-adding movement and thus must be minimized. However, generating the optimum tool path with the shortest possible traveling distance in the presence of obstacles received little attention and therefore, CAM-generated tool paths are not necessarily optimum. This paper introduces a new algorithm based upon Traveling Salesman Problem (TSP) to minimize the tool path length while considering collision avoidance. The developed optimization model considers multiple constraints, including the location of tool origin and the presence of obstacles along the tool path, to generate a collision-free trajectory with minimum length. Performance of the proposed model was compared to the tool path generated by HSMWorks CAM software and the results confirmed the ability of the algorithm in generating an optimum or near-optimum, collision-free tool path for real-world drilling applications with a minimal computational time.

The Use of 3D Motion Capture for the Quantitative Assessment of Surgical Tool Motion in Expert Laparoscopic and Naïve Surgeons

Study Objective This proof-of-concept study sought to evaluate the efficacy of using quantitative variables derived from 3D motion analysis to differentiate laparoscopic surgical skill level. Design Observational case-control. Setting Simulation-based setting; surgical task completion. Patients or Participants Expert laparoscopic surgeons (n=7) and naïve surgeons (n=10). Interventions Participants were recruited to complete the Fundamentals of Laparoscopic Surgery (FLS) peg transfer task. Measurements and Main Results All participants watched an instructional video prior to data collection and completed the task three times. A 3D motion capture system recorded trajectories of retroreflective markers placed on two Maryland graspers and location of surgical tool tips were computed relative to a box trainer. Variables of interest: completion time, surgical tool translation (sagittal, frontal, coronal planes, surgical tool pathlength, and symmetry ratios (non-dominant vs. dominant tool motion). Independent one-tailed T-tests evaluated significant between group differences at the p<0.05 level. Experts completed the task with significantly shorter (mean±stdev) times (119.4±61.7s) (p=0.007) compared to naïve surgeons (201.9±78.4s). This is complemented by significantly shorter tool pathlengths (dominant=195.1±108.7cm; non-dominant=187.1±103.9cm) (p=0.038,0.004) and more symmetrical grasper use (symmetry ratio=0.95±0.19) (p=0.019) in experts compared to naïve surgeons (dominant=297.6±110.7cm; non-dominant=356.9±118.3cm; symmetry ratio=1.24±0.29). No between group differences in surgical tool tip translations were observed. Conclusion 3D motion variables can be used to quantify between group differences in surgical tool motion. Not only did expert surgeons complete tasks more quickly, they also moved more efficiently and displayed more symmetrical use of their hands compared to naïve surgeons, who had greater movement of their non-dominant hands during the task. Objective, quantitative methods allowing trainees to improve surgical skill outside the OR are critical to ensure the highest possible standard of care is provided to patients. However, current evaluation models lack feedback relating to quality of movement. We believe motion capture can be a valuable adjunct to current skills acquisition models to improve self-directed surgical education and resident technicity. This proof-of-concept study sought to evaluate the efficacy of using quantitative variables derived from 3D motion analysis to differentiate laparoscopic surgical skill level. Observational case-control. Simulation-based setting; surgical task completion. Expert laparoscopic surgeons (n=7) and naïve surgeons (n=10). Participants were recruited to complete the Fundamentals of Laparoscopic Surgery (FLS) peg transfer task. All participants watched an instructional video prior to data collection and completed the task three times. A 3D motion capture system recorded trajectories of retroreflective markers placed on two Maryland graspers and location of surgical tool tips were computed relative to a box trainer. Variables of interest: completion time, surgical tool translation (sagittal, frontal, coronal planes, surgical tool pathlength, and symmetry ratios (non-dominant vs. dominant tool motion). Independent one-tailed T-tests evaluated significant between group differences at the p<0.05 level. Experts completed the task with significantly shorter (mean±stdev) times (119.4±61.7s) (p=0.007) compared to naïve surgeons (201.9±78.4s). This is complemented by significantly shorter tool pathlengths (dominant=195.1±108.7cm; non-dominant=187.1±103.9cm) (p=0.038,0.004) and more symmetrical grasper use (symmetry ratio=0.95±0.19) (p=0.019) in experts compared to naïve surgeons (dominant=297.6±110.7cm; non-dominant=356.9±118.3cm; symmetry ratio=1.24±0.29). No between group differences in surgical tool tip translations were observed. 3D motion variables can be used to quantify between group differences in surgical tool motion. Not only did expert surgeons complete tasks more quickly, they also moved more efficiently and displayed more symmetrical use of their hands compared to naïve surgeons, who had greater movement of their non-dominant hands during the task. Objective, quantitative methods allowing trainees to improve surgical skill outside the OR are critical to ensure the highest possible standard of care is provided to patients. However, current evaluation models lack feedback relating to quality of movement. We believe motion capture can be a valuable adjunct to current skills acquisition models to improve self-directed surgical education and resident technicity.

Experimental and analytical evaluation of tool path error using computer integrated nonlinear kinematical modeling for a 4DOF parallel milling machine

ABSTRACT Machining errors in parallel kinematic machines primarily depend on their extent of kinematic nonlinearity. In this paper, a computer integrated model of the kinematic nonlinearity and trajectory interpolation method for a 4DOF parallel milling machine is developed. The nonlinear errors prompted during various trajectory modes are analyzed. It is proved that in order to avoid significant non-linearity, the actuator and the end-effector space must possess identical order of trajectory. The effects of tool path length, its spatial location, and the Jacobian matrix properties on the kinematic nonlinearity error, are studied. Results showed that the path length is the governing factor for the nonlinear behavior of the mechanism. Moreover, the kinematic error is illustrated to have a reverse relationship with maximum singular values of the inverse Jacobian matrix. Experiments are conducted using digital dial indicators. Experimental results verified the accuracy of the proposed mathematical approach and confirmed its applicability in actual machining processes. Moreover, the proposed interpolation algorithm successfully limits the kinematic error under the machining tolerance by minimal segmentation. Finally, it is demonstrated that the proposed method is superior in terms of accuracy, to the median osculating circle (MOC) method.

Region-Based Efficient Computer Numerical Control Machining Using Point Cloud Data

Abstract This paper presents an efficient tool path planning strategy for three-axis computer numeric control (CNC) machining using curvature-based segmentation (CBS) of freeform surface from its representation in the form of a point cloud. Curvature parameters estimated over the point data are used to partition the surface into convex, concave, and saddle-like regions. Grid-based adaptive planar tool path planning strategy is developed to machine each region separately within its boundaries. In addition to the region-by-region machining, a strategy to stitch the obtained regions is also developed to minimize the tool lifts and tool marks. The developed region-based tool path planning strategy is compared with the point-cloud-based adaptive planar strategy, iso-scallop strategy, and commercial software for parts with various complexities. The result shows significant improvement in terms of performance parameters, namely, machining time, tool path length, and code length while maintaining the desired part surface quality. The proposed method is also tested by machining a real surface and analyzing its surface quality.

Efficient trochoidal milling based on medial axis transformation and inscribed ellipse

Trochoidal milling is a promising strategy for machining pockets with difficult-to-cut materials. Conventional trochoidal toolpath is composed of a set of circular curves and transition curves. However, the actual radial cutting width has a large variation along processing path due to geometric features of inscribed circles, which results in large fluctuation of cutting width and low material removal rate. In this paper, a new trochoidal toolpath based on elliptical curve is proposed to overcome the mentioned drawbacks. Medial axis transformation is adopted to represent the 2D pocket geometry. An algorithm is developed to determine a sequence of inscribed ellipses according to pre-defined machining parameters. Hermit curve is utilized to generate the transition curves between two adjacent elliptical curves. The overall toolpath length is significantly reduced. Machining experiments are conducted to validate the feasibility and efficiency of the proposed method. The experimental results showed an improvement of 12.7% in machining efficiency without increasing the maximum cutting force, compared with conventional trochoidal toolpath.

T-Spline Surface Toolpath Generation Using Watershed-Based Feature Recognition

The freeform surface is treated as a single machining region for most traditional toolpath generation algorithms. However, due to the complexity of a freeform surface, it is impossible to produce a high-quality surface using one unique machining process. Hence, region-based methods are widely investigated for freeform surface machining to achieve an optimized toolpath. The Non-Uniform Rational B-spline Surface (NURBS) represented freeform surface is not suitable for region-based toolpath generation because of the surface gaps caused by NURBS trimming and merging operations. To solve the limitation of the NURBS, T-spline is proposed with the advantages of being gap-free, having less control points, and local refinement, which is an ideal tool for region-based toolpath generation. Thus, T-spline is introduced to represent a freeform surface for its toolpath generation in the paper. A region-based toolpath generation method for the T-spline surface is proposed based on watershed technology. Firstly, watershed-based feature recognition is presented to divide the T-spline surface into a set of sub-regions. Secondly, the concept of a PolyBoundingBox that consists of a set of minimum bounding boxes is proposed to describe the sub-regions, and Manufacturing-Suitable Regions are constructed with the help of T-spline local refinement and the PolyBoundingBox. In the end, an optimized multi-rectangles toolpath generation algorithm is applied for sub-regions. The proposed method is tested using three synthetic T-spline surfaces, and the comparison results show the advantage in toolpath length and toolpath reversing number.

A smooth tool path planning method on NURBS surface based on the shortest boundary geodesic map

Abstract To fill the development gap between computer-aided design (CAD) and computer-aided manufacturing (CAM) on non-uniform rational b-spline (NURBS), a general tool path planning method on NURBS surface is proposed in this study. The generated tool path is composed of a series of closed paths which are derived by mapping the contours of a shortest boundary geodesic map (SBGM) from parameter space to 3D Euclidean space. SBGM that is created in parameter space depicts the shortest boundary geodesic distance (SBG) from internal points on NURBS surface to surface boundaries. It is calculated in discrete form firstly and then reconstructed by using a bicubic smoothing spline algorithm to improve the smoothness of the generated tool path. The tool path interval is deduced according to the requirement of residual scallop height constraint and determined conveniently based on the SBG difference of the two adjacent paths. It is confirmed that the proposed tool path planning method can be applied on all kinds of NURBS surface without the requirements of any other post-processing procedures. Typical case studies and experiments are presented to verify the effectiveness of the proposed method. Comparing with other traditional approaches, the proposed tool path planning method has excellent performance in improving the smoothness and reducing the total length of tool path.

An efficient iso-scallop tool path generation method for three-axis scattered point cloud machining

Shortening total length of tool path is preferred in numerical control (NC) machining since it can reduce machining time effectively. Compared with iso-planar tool path, iso-scallop tool path is shorter in length due to the pursuing of maximum interval values. However, the process of iso-scallop tool path generation is more complicated and time-consuming. To simplify the computing process and improve efficiency, this paper presents an efficient iso-scallop tool path generation method for three-axis scattered point cloud machining. Avoiding offsetting points or fitting surface, scallop points and iso-scallop cutter location (CL) points are directly calculated based on scattered data points by iterative algorithms. In order to reduce the times of iterative calculations, initial scallop-height points and CL points are calculated to be closer to the wanted theoretical points. Only a small number of key data points are searched for use so as to reduce calculation amount, and the number gradually decreases with the increase of iterations. Two typical point clouds are used to test the presented method. The experiment results indicate that the scallop height on the machined surface is uniform, and total length of the generated tool paths is much shorter than that of iso-planar tool paths. Moreover, the computation efficiency is also improved and is higher than our previous method (Int J Adv Manuf Technol 63: 137–146, 2012).

Spiral toolpath generation method for pocket machining

Spiral toolpath plays a significant role in 2.5D pocket machining with the advantages of free of sharp corners and without unwanted un-cutting materials. Technically, efficiency of pocket machining toolpath is determined by its smoothness and total length. Most of existing spiral toolpath generation methods pay more attention on acquiring a smooth toolpath without tool retractions, lacking consideration of total length minimization. In this work, an improved spiral toolpath generation method based on medial axis (MA) transformation is developed to decrease the total length of toolpath, so as to improve the machining performance. The proposed method can be applied to pockets with arbitrary geometries. To avoid unwanted interference between cutting tool and pocket boundary, cutting tool accessibility analysis is firstly conducted. MA sub-cycle for each layer is acquired by trimming the original MA of the pocket. Toolpath of each layer is determined by the envelope curve of a set of disks with various diameters that located on its MA sub-cycle. Toolpath between adjacent layers is guaranteed to be free of intersections. The spiral toolpath is generated by connecting the isolated toolpaths of all layers based on partially linear morphing. Then a toolpath modification algorithm is developed to smooth the spiral toolpath around root node of pocket MA. Finally, machining experiment is conducted to validate the advantages of the proposed method on machining efficiency improvement.

Development of Direction-Parallel Strategy for Shorting A Tool Path in The Triangular Pocket Machining

The machining strategy is one of the parameters which practically influences the time of the different manufacturing geometric forms. The machining time directly relates to the machining efficiency of the tool paths. In area milling machining, there are two main types of tool path strategies: a direction-parallel milling and contour-parallel milling. Then direction-parallel milling is simple compared with a contour-parallel strategy. This paper proposes a new model of the direction-parallel machining strategy for triangular pockets to reduce the tool path length. The authors develop an analytical model by appending additional the tool path segments to the basis tool path for cutting un-machined area or scallops, which remained along the boundary. To validate its results, the researchers have compared them to the existing model found in the literature. For illustrating the computation of this model, the study includes two numerical examples. The results show that the proposed analytic direction-parallel model can reduce the total length of machining. Thus, it can take a shorter time for milling machining.

Optimization of Turning Process and Cutting Force Using Multiobjective Genetic Algorithm

Application of artificial intelligence in manufacturing process has great impact factor. This work paper is focused into optimization of machining by turning process regarding to the analysing of tool selection (TS), tool path length (TPL) and machining parameters for turning operation using the artificial Intelligence. Except of solving of problems for tool selection and tool path length, here also will be analysed the cutting force (Fc) by turning process whereas as case of research material is steel C45. The results of measurement of the main cutting force Fc, are compared and predicted in theoretical and practical way. Also, all of requirements are fulfilled in regard of the expression. In same time are optimized the main machining parameters regarding to the cutting force with utilization of the Multi-Objective Genetic Algorithm (MOGA). Results for cutting power Pc and Metal removal rate MRR using Pareto Front are obtained using MOGA.