An Agent-Based Method for Feature Recognition and Path Optimization of Computer Numerical Control Machining Trajectories.

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), which often result in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models and analyze the key geometric information in the G-code generated during CNC machining, and it uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bézier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. The effectiveness of the proposed method is validated through the optimization of process files for gear models, pentagram bosses, and maple leaf models. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths and reduce machining defects, offering substantial application value.

Molecular dynamics studies of the solubility behavior of carbon dioxide (CO2 ), difluoromethane (R-32), 1-chloro-3,3,3-trifluoropropene (R-1233zd(E)) and 2,3,3,3-tetrafluoro-1-propene (R-1234yf) in pentaerythritol tetra(2-ethylhexanoate) (PEB8), pentaerythritol tetrabutyrate (PEC4) and pentaerythritol tetraoctanoate (PEC8).

To reduce the climate impact of thermal engines such as heat pumps or refrigeration machines, refrigerants with a low global warming potential need to be paired with fitting lubricants. As the contamination of those liquid components influences the efficiency and lifetime of these machines, knowledge about their solubility behavior is of great interest. Molecular simulations offer mighty tools to investigate these solubilities while giving structural insight into the systems. Here the solubility behavior of CO2 , R-32, R-1233zd(E), and R-1234yf in PEB8, PEC4, and PEC8 is compared through the solvation free energy ∆GSolv obtained by molecular dynamics simulations. To derive ∆GSolv at low computational cost, an iterative method is used to find an optimal number and distribution of intermediate states. The resulting distributions are investigated with regard to different parameters of the employed softcore-potential. ∆GSolv values for the different refrigerant-lubricant pairings at different temperatures are provided, followed by a structural analysis.

Multi-Objective Path Optimization in Fog Architectures Using the Particle Swarm Optimization Approach.

IoT systems can successfully employ wireless sensor networks (WSNs) for data gathering and fog/edge computing for processing collected data and providing services. The proximity of edge devices to sensors improves latency, whereas cloud assets provide higher computational power when needed. Fog networks include various heterogeneous fog nodes and end-devices, some of which are mobile, such as vehicles, smartwatches, and cell phones, while others are static, such as traffic cameras. Therefore, some nodes in the fog network can be randomly organized, forming a self-organizing ad hoc structure. Moreover, fog nodes can have different resource constraints, such as energy, security, computational power, and latency. Therefore, two major problems arise in fog networks: ensuring optimal service (application) placement and determining the optimal path between the user end-device and the fog node that provides the services. Both problems require a simple and lightweight method that can rapidly identify a good solution using the constrained resources available in the fog nodes. In this paper, a novel two-stage multi-objective path optimization method is proposed that optimizes the data routing path between the end-device and fog node(s). A particle swarm optimization (PSO) method is used to determine the Pareto Frontier of alternative data paths, and then the analytical hierarchy process (AHP) is used to choose the best path alternative according to the application-specific preference matrix. The results show that the proposed method works with a wide range of objective functions that can be easily expanded. Moreover, the proposed method provides a whole set of alternative solutions and evaluates each of them, allowing us to choose the second- or third-best alternative if the first one is not suitable for some reason.

A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator.

The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator.

Automated input structure generation for single-ended reaction path optimizations.

The exploration of a reaction network requires highly automated workflows to avoid error-prone and time-consuming manual steps. In this respect, a major bottleneck is the search for transition-state (TS) structures, which frequently fails and, therefore, makes (manual) revision necessary. In this work, we present a technique for obtaining suitable input structures for automated TS searches based on single-ended reaction path optimization algorithms, which makes subsequent TS searches via this method significantly more robust. First, possible input structures are generated based on the spatial alignment of the reactants. The appropriate orientation of reacting groups is achieved via stepwise rotations along selected torsional degrees of freedom. Second, a ranking of the obtained structures is performed according to selected geometric criteria. The main goals are to properly align the reactive atoms, to avoid hindrance within the reaction channel and to resolve steric clashes between the reactants. The developed procedure has been carefully tested on a variety of examples and provides suitable input structures for TS searches within seconds. The method is in daily use in an industrial setting.

Effectiveness of China's provincial industrial carbon emission reduction and optimization of carbon emission reduction paths in "lagging regions": Efficiency-cost analysis.

Accurately assessing the effectiveness of industrial carbon emission reduction in each province and optimizing the emission reduction path have important practical significance for China's Nationally Determined Contribution (NDC) emission reduction achievement targets. This study first evaluates the industry's emission reduction effects across 30 provinces of China. Then, the emission reduction paths of "lagging regions," which fail to meet the 2030 industrial carbon emission reduction target, are optimized based on the two-dimensional perspective of carbon emission efficiency and emission reduction cost. This study found that (1) China has exceeded its 2020 industrial carbon emission reduction target. There are 9 potential "lagging regions" that failed to meet their 2020 targets, (2) if the current emission reduction rate is maintained, China is capable of exceeding its 2030 industrial carbon emission reduction target, but there are still 11 "lagging regions," (3) there are clear differences in carbon emission efficiency and shadow price among the "lagging regions," and (4) under the premise of ensuring feasibility and fairness, the three provinces of Liaoning, Guangxi, and Shaanxi can set strict emission reduction targets, while other "lagging regions" can set flexible targets.

Optimal Path Planning for Selective Waste Collection in Smart Cities.

Waste collection is one of the targets of smart cities. It is a daily task in urban areas and it entails the planning of waste truck routes, taking into account environmental, economic and social factors. In this work, an optimal path planning algorithm has been developed together with a practical software platform for smart and sustainable cities that enables computing the optimal waste collection routes, minimizing the impact, both environmental (CO2 emissions and acoustic damage) and socioeconomic (number of trucks to be used and fuel consumption). The algorithm is executed in Net2Plan, an open-source planning tool, typically used for modeling and planning communication networks. Net2Plan facilitates the introduction of the city layout input information to the algorithm, automatically importing it from geographical information system (GIS) databases using the so-called Net2Plan-GIS library, which can also include positions of smart bins. The algorithm, Net2Plan tool and its extension are open-source, available in a public repository. A practical case in the city of Cartagena (Spain) is presented, where the optimal path planning for plastic waste collection is addressed. This work contributes to the urban mobility plans of smart cities and could be extended to other smart cities scenarios with requests of optimal path planning.

Deterministic Agent-Based Path Optimization by Mimicking the Spreading of Ripples.

Inspirations from nature have contributed fundamentally to the development of evolutionary computation. Learning from the natural ripple-spreading phenomenon, this article proposes a novel ripple-spreading algorithm (RSA) for the path optimization problem (POP). In nature, a ripple spreads at a constant speed in all directions, and the node closest to the source is the first to be reached. This very simple principle forms the foundation of the proposed RSA. In contrast to most deterministic top-down centralized path optimization methods, such as Dijkstra's algorithm, the RSA is a bottom-up decentralized agent-based simulation model. Moreover, it is distinguished from other agent-based algorithms, such as genetic algorithms and ant colony optimization, by being a deterministic method that can always guarantee the global optimal solution with very good scalability. Here, the RSA is specifically applied to four different POPs. The comparative simulation results illustrate the advantages of the RSA in terms of effectiveness and efficiency. Thanks to the agent-based and deterministic features, the RSA opens new opportunities to attack some problems, such as calculating the exact complete Pareto front in multiobjective optimization and determining the kth shortest project time in project management, which are very difficult, if not impossible, for existing methods to resolve. The ripple-spreading optimization principle and the new distinguishing features and capacities of the RSA enrich the theoretical foundations of evolutionary computation.

Reducing treatment time for robotic radiosurgery by considering path efficiency during node selection.

BACKGROUND: Robotic radiosurgery treatments allow for precise non-coplanar beam delivery by utilizing a robot equipped with a linac that traverses through a set of predetermined nodes. High quality treatment plans can be produced but treatment times can grow large, with one substantial component being the robot traversal time. PURPOSE: The aim of this study is to reduce the treatment time for robotic radiosurgery treatments by introducing algorithms for reducing the robot traversal time. The algorithms are integrated into a commercial treatment planning system. METHODS: First, an optimization framework for robotic radiosurgery planning is detailed, including a heuristic optimization method for node selection. Second, two methods aimed at reducing the traversal time are introduced. One utilizes a centrality measure focusing on the structure of the node network, while the other is based on the direct computation of traversal times during optimization. A comparison between plans with and without the time-reducing algorithms is made for three brain cases and one liver case with basis in treatment time, plan quality, monitor units, and network structure of the selected nodes. RESULTS: Large decreases in traversal times are obtained by the traversal time reducing algorithms, with reductions of up to 49 % in the brain cases and 31 % in the liver case. The resulting reductions in treatment times are up to 30 % and 13 %, respectively. Small differences in plan quality are observed, with similar dose-volume histograms, dose distributions, and conformity/gradient indices. CONCLUSIONS: The total treatment time of the robotic radiosurgery treatments can be reduced by selecting nodes with more efficient robot traversal paths, while maintaining plan quality.

[Metabolic engineering of Escherichia coli for de novo synthesis of 1,4-butanediol].

1,4-butanediol is an important intermediate widely used in chemical, agricultural, and pharmaceutical industries. This study constructed a new short path for the production of 1,4-butanediol with glucose as the substrate by combining enzyme engineering and metabolic engineering. Firstly, a novel path catalyzed by α-ketoglutarate decarboxylase (SucA), carboxylate reductase (Car), and alcohol dehydrogenase (YqhD) was designed by database mining, and the de novo synthesis of 1,4-butanediol was achieved after introduction of the path into Escherichia coli W3110 (K-12) chassis cells. To further improve the synthesis efficiency of this path, we deleted the genes encoding lactate dehydrogenase A (LdhA) and pyruvate formate lyase B (PflB) to block the metabolic bypass. Furthermore, the expression of citrate synthase (GltAR163L) was up-regulated to increase the α-ketoglutarate metabolic flux. In addition, we improved the synthesis of the key cofactor NADPH and up-regulated the expression of sucA, car, and yqhD by substituting with strong promoters to increase the efficiency of supplying precursors to 1,4-butanediol synthesis. Eventually, the recombinant strain produced up to 770 mg/L of 1,4-butanediol within 48 h in a shake flask, and 4.22 g/L of 1,4-butanediol within 60 h in a 5 L fermenter with a yield of 12.46 mg/g glucose. Compared with the previously reported method, the novel path designed in this study for the de novo synthesis of 1,4-butanediol does not need acetyl coenzyme A and avoids the byproduct acetate or the addition of ammonia. Therefore, the outcome is expected to provide a new idea for the metabolic engineering of microbial chassis for the production of 1,4-butanediol and its high-value derivatives.

Optimization Design of Laser Arrays Based on Absorption Spectroscopy Imaging for Detecting Temperature and Concentration Fields.

Detecting temperature and concentration fields within engine combustors holds paramount significance in enhancing combustion efficiency and ensuring operational safety. Within the realm of engine combustors, the laminar absorption spectroscopy technique has garnered considerable attention. Particularly crucial is the optimization of the optical path configuration to enhance the efficacy of reconstruction. This study presents a flame parameter field reconstruction model founded on laminar absorption spectroscopy. Furthermore, an optimization approach for refining the optical path configuration is delineated. In addressing non-axisymmetric flames, the simulated annealing algorithm (SA) and Harris's Hawk algorithm (HHO) are employed to optimize the optical path layout across varying beam quantities. The findings underscore a marked reduction in imaging errors with the optimized optical path configuration compared to conventional setups, thereby elevating detection precision. Notably, the HHO algorithm demonstrates superior performance over the SA algorithm in terms of optimization outcomes and computational efficiency. Compared with the parallel optical path, the optimized optical path of the HHO algorithm reduces the temperature field error by 25.5% and the concentration field error by 26.5%.

Lissajous curves as aerial search patterns.

Manned and unmanned systems are prevalent in a wide range of aerial searching applications. For aircraft whose trajectory is not or cannot be planned on-the-fly, optimal deterministic search pattern generation is a critical area of research. Lissajous curves have recently caught attention as excellent candidates for all kinds of aerial search applications, but little fundamental research has been done to understand how best to design Lissajous pattern (LP)s for this use. This paper examines the optimization of these search patterns from analytical, numerical, and data-driven perspectives to establish the state of the field in Lissajous curves for aerial search. From an analytical perspective, it was found that the average expected distance between a Lissajous searcher and a random target on a unit square approaches 0.586 as search time increases. Furthermore, an analytical approximation for the average searcher speed was found to guarantee error of no more than 22.1%. Important outcomes from the numerical optimization of Lissajous search patterns include the development of an intuitive evaluation criterion and the conclusion that irrational frequency ratios near 0.8 typically yield highest performance. Finally, while a robust predictive model for fast pattern optimization is yet out of reach, initial results indicate that such an approach shows promise.

Workshop AGV path planning based on improved A* algorithm.

This article proposes an improved A* algorithm aimed at improving the logistics path quality of automated guided vehicles (AGVs) in digital production workshops, solving the problems of excessive path turns and long transportation time. The traditional A* algorithm is improved internally and externally. In the internal improvement process, we propose an improved node search method within the A* algorithm to avoid generating invalid paths; offer a heuristic function which uses diagonal distance instead of traditional heuristic functions to reduce the number of turns in the path; and add turning weights in the A* algorithm formula, further reducing the number of turns in the path and reducing the number of node searches. In the process of external improvement, the output path of the internally improved A* algorithm is further optimized externally by the improved forward search optimization algorithm and the Bessel curve method, which reduces path length and turns and creates a path with fewer turns and a shorter distance. The experimental results demonstrate that the internally modified A* algorithm suggested in this research performs better when compared to six conventional path planning methods. Based on the internally improved A* algorithm path, the full improved A* algorithm reduces the turning angle by approximately 69% and shortens the path by approximately 10%; based on the simulation results, the improved A* algorithm in this paper can reduce the running time of AGV and improve the logistics efficiency in the workshop. Specifically, the walking time of AGV on the improved A* algorithm path is reduced by 12s compared to the traditional A* algorithm.

Mechanical Characterization and Production of Various Shapes Using Continuous Carbon Fiber-Reinforced Thermoset Resin-Based 3D Printing.

Continuous carbon fiber-reinforced (CCFR) thermoset composites have received significant attention due to their excellent mechanical and thermal properties. The implementation of 3D printing introduces cost-effectiveness and design flexibility into their manufacturing processes. The light-assisted 3D printing process shows promise for manufacturing CCFR composites using low-viscosity thermoset resin, which would otherwise be unprintable. Because of the lack of shape-retaining capability, 3D printing of various shapes is challenging with low-viscosity thermoset resin. This study demonstrated an overshoot-associated algorithm for 3D printing various shapes using low-viscosity thermoset resin and continuous carbon fiber. Additionally, 3D-printed unidirectional composites were mechanically characterized. The printed specimen exhibited tensile strength of 390 ± 22 MPa and an interlaminar strength of 38 ± 1.7 MPa, with a fiber volume fraction of 15.7 ± 0.43%. Void analysis revealed that the printed specimen contained 5.5% overall voids. Moreover, the analysis showed the presence of numerous irregular cylindrical-shaped intra-tow voids, which governed the tensile properties. However, the inter-tow voids were small and spherical-shaped, governing the interlaminar shear strength. Therefore, the printed specimens showed exceptional interlaminar shear strength, and the tensile strength had the potential to increase further by improving the impregnation of polymer resin within the fiber.

Investigation of scan path optimization in improving proton pencil beam scanning continuous delivery.

Objective. To investigate the impact of scan path optimization on the dose accuracy and beam delivery time (BDT) of proton pencil beam scanning in the dose-driven continuous scanning (DDCS).Approach. A diverse set of six clinical plans, representing various spot patterns and treatment sites, was used to evaluate the effectiveness of scan time optimization and scan length optimization. The DDCS dose discrepancy and BDT with optimized scan paths was compared to the default serpentine scan path.Main results. Both scan time optimization and scan path optimization were able to reduce the DDCS dose discrepancy compared to the default serpentine scan path. All plans, except for the layer repainting lung plan, achieved a 2%/2 mm gamma pass rate of over 99% and less than 1% PTV DVH root mean square error (RMSE) through scan path optimization. In the case of the layer repainting lung plan, when compared to the default serpentine scan path, the 2%/2 mm gamma pass rate showed improvements from 91.3% to 93.1% and 95.8%, while the PTV DVH RMSE decreased from 2.1% to 1.7% and 1.1% for scan time optimization and scan length optimization, respectively. Although scan time optimization resulted in shorter total scan times for all plans compared to the default scan path and scan length optimization tended to have longer total scan times. However, due to the short total scan times and their minimal contribution to the total BDT, the impact of scan path optimization on the total BDT was practically negligible.Significance. Both scan time optimization and scan length optimization proved to be effective in minimizing DDCS dose discrepancy. No definitive winner can be determined between these two optimization approaches. Both scan time and scan length optimization had minimal effect on the total BDT.

On the Path to Optimal Alchemistry.

Alchemical free energy calculations have become a standard and widely used tool, in particular for calculating and comparing binding affinities of drugs. Although methods to compute such free energies have improved significantly over the last decades, the choice of path between the end states of interest is usually still the same as two decades ago. We will show that there is a fundamentally arbitrary, implicit choice of parametrization of this path. To address this, the notion of the length of a path or a metric is required. A metric recently introduced in the context of the accelerated weight histogram method also proves to be very useful here. We demonstrate that this metric can not only improve the efficiency of sampling along a given path, but that it can also be used to improve the actual choice of path. For a set of relevant use cases, the combination of these improvements can increase the efficiency of alchemical free energy calculations by up to a factor 16.

A multi-center joint distribution optimization model considering carbon emissions and customer satisfaction.

Logistics enterprises are searching for a sustainable solution between the economy and the environment under the concept of green logistics development. Given that, this study integrates carbon emission as one of the costs into the vehicle routing problem with time window (VRPTW) and establishes a multi-center joint distribution optimization model taking into account distribution cost, carbon emission, and customer satisfaction. In the study of carbon emissions, this paper selected the vehicle load rate and vehicle distance as the main indicators. An improved ant colony algorithm is designed to solve the model by introducing the elite strategy, the saving strategy, vehicle service rules, and customer selection rules. Simulation results show that compared with the traditional ant colony optimization and genetic algorithm, the improved ant colony algorithm can effectively reduce the distribution cost and carbon emission and, improve customer satisfaction.

NA-OR: A path optimization method for manipulators via node attraction and obstacle repulsion.

This paper is concerned with the issue of path optimization for manipulators in multi-obstacle environments. Aimed at overcoming the deficiencies of the sampling-based path planning algorithm with high path curvature and low safety margin, a path optimization method, named NA-OR, is proposed for manipulators, where the NA (node attraction) and OR (obstacle repulsion) functions are developed to refine the path by iterations. In the iterations of path optimization, the node attraction function is designed to pull the path nodes toward the center of their neighbor nodes, thereby reducing the path curvature and improving the smoothness. Also, the obstacle repulsion function is developed to push the path nodes out of the potentially unsafe region by generating a repulsive torque on the path nodes, thus improving the safety margin of the motion. By introducing the effect of NA-OR, the optimized path has a significant improvement in path curvature and safety margin compared with the initial path planned by Bi-RRT, which meaningfully enhances the operation ability of manipulators for the applications that give a strong emphasis on security. Experimental results on a 6-DOF manipulator in 4 scenarios demonstrate the effectiveness and superiority of the proposed method in terms of the path cost, safety margin, and path smoothness.

Sigmoidal Auxiliary Tendon-Driven Mechanism Reinforcing Structural Stiffness of Hyper-Redundant Manipulator for Endoscopic Surgery.

The overtube of an endoscopic surgery robot is fixed when performing tasks, unlike those of commercial endoscopes, and this overtube should have high structural stiffness after reaching the target lesion so that sufficient tension can be applied to the lesion tissue with the surgical tool and there are fewer changes in the field of view of the endoscopic camera from this reaction force. Various methods have been proposed to reinforce the structural stiffnesses of hyper-redundant manipulators. However, the safety, rapid response, space efficiency, and cost-effectiveness of these methods should be considered for use in actual clinical environments, such as the gastrointestinal tract. This study proposed a method to minimize the positional changes of the overtube end tip due to external forces using only auxiliary tendons in the optimized path without additional mechanical structures. Overall, the proposed method involved moving the overtube to the target lesion through the main driving tendon and applying tension to the auxiliary tendons to reinforce the structural stiffness. The complete system was analyzed in terms of energy, and the sigmoidal auxiliary tendons were verified to effectively reinforce the structural stiffness of the overtube consisting of rolling joints. In addition, the design guidelines of the overtube for actual endoscopic surgery were proposed considering hollowness, retroflexion, and high structural stiffness. The positional changes due to external forces were confirmed to be reduced by 60% over the entire workspace.

Study on 4D taxiing path planning of aircraft based on spatio-temporal network.

In recent years, China vigorously develops energy conservation and emission reduction, in order to actively respond to the national call to make the aircraft operation process reduce unnecessary costs and strengthen the safety of the aircraft taxiing process. This paper studies the spatio-temporal network model and dynamic planning algorithm to plan the aircraft taxiing path. First, the relationship between the force, thrust and engine fuel consumption rate during aircraft taxiing is analyzed to determine the fuel consumption rate during aircraft taxiing. Then, a two-dimensional directed graph of airport network nodes is constructed. The state of the aircraft is recorded when considering the dynamic characteristics of the node sections, the taxiing path is determined for the aircraft using dijkstra's algorithm, and the overall taxiing path is discretized from node to node using dynamic planning to design a mathematical model with the shortest taxiing distance as the goal. At the same time, the optimal taxiing path is planned for the aircraft in the process of avoiding aircraft conflicts. Thus, a state-attribute-space-time field taxiing path network is established. Through example simulations, simulation data are finally obtained to plan conflict-free paths for six aircraft, the total fuel consumption for the six aircraft planning is 564.29 kg, and the total taxiing time is 1765s. This completed the validation of the dynamic planning algorithm of the spatio-temporal network model.