Matrix placement machines improve production efficiency of printed circuit board assembly (PCBA), addressing critical needs for flexible and intelligent electronics manufacturing. However, their complex head structure renders solutions for traditional beam-head placement machines inefficient for matrix placement machines. This article proposes a phased hybrid algorithm with adaptive hyper-nondominated sorting genetic algorithm II (NSGA-II) for PCBA optimization. A bidirectional search mechanism is applied to derive feeder distributions and nozzle configurations, and iteratively tighten the solution space using priority-based search strategies. The softmax, max greatest common divisor, and max matching mechanisms are proposed for placement and pickup sequences, which facilitates construction of solution pools. Initial solutions are extracted from the pool and, subsequently, hyperheuristic mechanisms dynamically adjust genetic operators within NSGA-II to minimize placement, pickup, and recognition times with better convergence speed. Experimental validation with real-world production data demonstrates that the proposed algorithm achieves 6.05%–38.18% performance improvements compared to state-of-the-art solutions.

Operational efficiency of placement machines constrains the overall production capacity of printed circuit board (PCB) assembly lines. Existing state-of-the-art algorithms face challenges, such as conflicts between multiple objectives and coupling within different problems. This article proposes a multiobjective hybrid evolutionary multitasking algorithm (MOHEMTA) to address PCB assembly optimization in beam-head placement machines. The algorithm divides the problem into pickup and placement tasks, leveraging implicit parallelism to enhance solution efficiency. A nozzle block encoding method and heuristic decoding strategies with domain knowledge are introduced to reduce encoding complexity and accelerate algorithm convergence. MOHEMTA enhances offspring population diversity and quality through an elitist strategy, evolutionary operators, and knowledge transfer mechanisms, while incorporating safeguards against negative transfer. Experiments demonstrate that the multiobjective solution performance and practical results of MOHEMTA are better than those of other state-of-the-art algorithms.

In surface mount technology, solder paste printing quality critically affects product reliability. Accurate segmentation of solder paste regions is essential for defect detection, quantitative analysis, and process optimization. Traditional threshold-based methods lack robustness under varying surface textures and lighting, while deep learning approaches require large annotated datasets and expensive hardware, limiting their use in cost-sensitive manufacturing. We propose a fast, annotation-free segmentation framework based on parameteric multimodal learning, integrating RGB color with 3-D height data. Height priors generate an initial mask, followed by a lookup table–based parameteric color model that adapts to different printed circuit board types and batches. A convolutional feature fusion operator then constructs a joint height–color probability space, suppressing interference from substrate variations and uneven illumination, yielding a refined probability map for final segmentation. Tests on a 3D-solder paste inspection industrial dataset achieve 96.0% mean intersection over union and 98.8% pixel accuracy, matching state-of-the-art deep learning performance while greatly improving efficiency and suitability for real-world deployment without annotated data.

In printed circuit board assembly (PCBA), placement route schedule (PRS) significantly affects assembly efficiency of the beam head placement machine. The PRS is typically solved by decomposing it into placement point assignment problem (PPAP) and beam heads sequencing problem (BHSP). This article first proposes a deep reinforcement learning framework to tackle PPAP, which is a key determinant of overall process quality. Then, to mitigate the impact of placement position and angle on assembly efficiency, a dynamic programming-based beam head sequencing algorithm is introduced to solve BHSP. Since component types and placement point assignment states vary across different pick-and-place cycles, a dynamic combinatorial mask encoding method is proposed to effectively extract feature information between placement points. Inspired by the beam head placement process, a decoder that combines gated recurrent units and an attention mechanism is finally introduced, which fully utilizes historical node information to predict the next node. Experimental results demonstrate that the proposed method reduces PCBA routing distance by an average of 4.62%, outperforming other State-of-the-Art approaches.

Time-optimal path generation is critical for maximizing throughput in high-speed PCB assembly, yet existing approaches predominantly focus on geometric distance minimization, overlooking the fundamental impact of acceleration dynamics and multi-axis coordination on temporal efficiency. This study addresses this gap by introducing a physics-based time estimator that explicitly models trapezoidal acceleration profiles for synchronized X/Y/Z/R-axis motions, enabling precise performance evaluation under realistic kinematic constraints. Experimental validation on production PCBs demonstrates that the proposed estimator achieves much higher estimation accuracy, outperforming conventional methods. When integrated as the objective function in multi-chromosome genetic algorithm optimization, time-optimal solutions effectively reduce actual movement times compared to distance-optimal baselines, despite requiring longer travel paths. These findings confirm the time-optimal estimator’s superiority over pure distance minimization, proving that peak efficiency is achieved by balancing travel distance with movement speed.

In surface mount technology (SMT) assembly, the increasing complexity and miniaturization of electronic components pose critical challenges to balancing placement precision and production efficiency. This paper presents a quality-efficiency driven scheduling and process co-optimization (SPCO) methodology for the pick-and-place (PAP) process in SMT production lines. Leveraging a cyber-physical system framework integrated with automated optical inspection, the proposed approach dynamically couples offline scheduling with online process capability feedback to achieve adaptive allocation of components. A precision-aware SPCO model is formulated to assign components to placement heads based on real-time process capability indices, ensuring compliance with stringent precision constraints. To enable real-time deployment, a precision-prioritized allocation heuristic (PPAH) is introduced, supporting component-head assignments under heterogeneous head capabilities. Experiments on industrial datasets demonstrate that PPAH completely eliminates precision violations while improving the overall process capability margin by 2.6-fold compared to state-of-the-art benchmarks, with only a moderate increase in total PAP time. These results validate the effectiveness of the proposed co-optimization strategy in improving first-pass yield and robustness in high-mix SMT environments.

In the field of smart surface mount technology (SMT) production, integrating machines through a cyber-physical system (CPS) architecture holds significant potential for improving assembly quality and efficiency. However, fully unifying inspection and production systems to effectively address assembly-related quality issues remains a challenge. This study seeks to close these gaps by introducing collaborative optimization methods to ensure seamless operations. The research is driven by the need for precise control of key assembly parameters, such as placement height, x-offset, y-offset, rotation angle deviations, and blowing durations, all of which are major contributors to defects. To address these challenges, we propose a self-adaptive collaborative optimization (SACO) framework that prioritizes enhancements based on their impact on both quality and efficiency. The SACO framework combines customized Bayesian optimization and particle swarm optimization techniques, allowing for dynamic adjustments to process parameters, guided by real-time data from automatic optical inspection (AOI) systems. The primary goal of this study is to reduce defects and improve efficiency in the SMT assembly process through these targeted improvements. Experimental results validate the effectiveness of the proposed methods, demonstrating significant advancements in placement accuracy and overall assembly efficiency. Our findings confirm that the SACO framework provides a robust solution to persistent challenges in SMT production, addressing critical gaps in quality control and process optimization.

Recent advances in video tracking with discriminative correlation filters leverage diverse observation models. However, fusing hand-crafted and deep convolutional neural network representations equivalently would overly constrain resolution conditions for template matching, leading to peak response slippage and jittery neighboring search processes, especially problematic in autonomous driving scenarios. This article addresses the inference conservatism issue in multitype feature tracking. We propose a target-observation constraint framework to formalize discrimination conservatism across feature map channels. A learning constraint transformation methodology is introduced to cluster similar representations while pushing dissimilar ones apart. These discriminant constraints are further fine-tuned through joint learning with correlation filters, improving the positional precision of detection responses. Additionally, we propose an updating strategy that suppresses low scores of symmetric dispersion ratio, enhancing tracking robustness. Extensive evaluations on five tracking datasets demonstrate the superior performance of our approach: UAV20L, UAVDT, OTB-100, VOT-2019, and LaSOT.

Route scheduling for automatic optical inspection (AOI) of printed circuit boards (PCBs) impacts the productivity of surface mount production lines. Current state-of-the-art mathematical models in the area are not rigorous enough and neglect significant practical constraints, such as component geometric constraints. This article proposes a hierarchical mixed integer programming model to describe the route scheduling problem for AOI of PCBs. The model allows theoretical optimal solutions to be obtained for small-scale problems. In addition, a two-stage heuristic framework, consisting of clustering and path planning stages, is proposed to improve efficiency in solving large-scale problems, achieving near-optimal solutions. Taking into account that component distribution affects clustering results, the clustering stage is developed with a hierarchical heuristic algorithm based on block density with an aggregation strategy. The Lin–Kernighan algorithm is first used to quickly generate the scheduling sequence in the path planning stage. Image acquisition centers are initially adjusted with a customized heuristic. After that, a hybrid evolutionary multitask algorithm is proposed to further reduce path distance by dividing the image acquisition center adjustment task into several subtasks using heuristic rules. The algorithm obtains better quality results and is faster than traditional evolutionary algorithms. Experiments on an actual industrial AOI platform demonstrate that the proposed two-stage heuristic route scheduling algorithm outperforms state-of-the-art research in the area.

Structured light imaging is a typical technique for industrial 3-D microscopic measurement. Extensive research on structured light codecs has been conducted to accurately correlate camera and projector pixels. However, these methods suffer significant degradation when measuring low-reflectivity and complex surfaces. This article introduces a deep correlation-based cascade structured light network (CasSLNet) that utilizes deep phase and column features to calculate correspondences at the subpixel scale. To mitigate the huge computational cost of full correlation, a coarse-to-fine approach is proposed. Specifically, multiscale features from the camera observation sequence and the 1-D encoding pattern are extracted through a pseudosiamese network, and cascade cost volumes are constructed. An initial column map is then regressed from the low-resolution column cost volume. Based on this, an iterative update operator is introduced to refine initial estimates, resulting in a full-resolution column map. Furthermore, a structured light dataset has been collected and experiments have been conducted on a typical structured light imaging platform. Experimental results demonstrate that CasSLNet outperforms both traditional and state-of-the-art deep learning-based methods.

Dual-gantry surface mount optimization effectively improves the productivity of printed circuit board assembly (PCBA), but also brings new challenges. Optimizing workload allocation to balance the front and rear gantry placement completion time is a significant challenge for improving PCBA productivity. This study proposes a finite potential game heuristic algorithm (FPGHA) to solve the workload allocation problem. The algorithm generates game agents by analyzing the feeding characteristics of the dual-gantry placement machine and using an improved bisection K-means clustering method. Agent utility is calculated based on metrics affecting productivity of the pick-and-place process, including the number of simultaneous pickups, nozzle changes, cycles, and mounting points. Nash equilibrium of FPGHA is obtained by a best-response dynamics and heuristic algorithm. Then, the effectiveness of FPGHA in solving the workload allocation problem is first demonstrated in simulated experiments with different nozzle and feeder configurations. Finally, FPGHA is compared with the hierarchical restricted balance algorithm, adaptive clustering algorithm, and the popular industrial optimizer software in actual placement experiments using real-world industrial printed circuit boards. The effectiveness and accuracy of FPGHA are verified by analyzing the correlation between three variables: The FPGHA estimated value, the actual assembly value, and the PCB assembly time.

This article conducts the issue of event-triggered reduced-order filtering for continuous-time semi-Markov jump systems with imperfect measurements as well as randomly occurring uncertainties (ROUs). Specifically, the sojourn-time-dependent transition probability matrix (TPM) is presumed to be polytopic and a quantizer is introduced to quantize output signals aiming to reflect the reality. Both ROUs and sensor failures are generated by individual random variables belonging to be mutually independent Bernoulli-distributed white sequences. First, sufficient conditions for the existence of the event-triggered reduced-order filter are obtained by utilizing the dissipativity-based technique to ensure the asymptotical stability with a strictly dissipative performance of the filtering error system. The time-varying TPM is then fractionalized, which enhances the results as stated. Furthermore, the required reduced-order filter parameters are obtained by introducing slack symmetric matrix as well as cone complementarity linearization algorithm. The effectiveness of the suggested event-triggered reduced-order filter design method is shown through simulation results.

The optimization of printed circuit board assembly (PCBA) for a beam head placement machine is a multivariable and multiconstraint combinatorial problem. Current techniques falter in solving a variety of PCBA problems since heuristic algorithms lack theoretical guarantees of optimality, and mathematical modeling methods have high computational complexity for the whole problem. This article proposes a novel two-phase optimization for PCBA, integrating the advantages of mathematical modeling with heuristic algorithms. We divide the problem into the head task assignment and the placement route schedule. For the former, an effective integer linear programming model with component partition is proposed, encompassing key efficiency-influencing factors. A recursive heuristic-based initial solution speeds up the solving convergence, while the reduction strategies enhance model solvability. For the placement route schedule, a tailored greedy algorithm yields high-quality solutions, leveraging the results of the model, and an aggregated route relink heuristic does further optimization. In addition, we propose a selection criterion for the solution pool of the model to pre-evaluate the placement movement, which builds the connection between the two phases. Finally, we validate the performance of the two-phase optimization, which provides an average efficiency improvement of 8.66%–21.83% compared to other mainstream research.

Surface mount technology is essential to the development of the electronic manufacturing industry. This article studies optimizing the surface mount process for the beam-head placement machine. A mixed-integer programming (MIP) model is proposed for this problem, which is decomposed into three interconnected hierarchical parts: feeder allocation; component assignment; and pick-and-place (PAP) sequence problems. This article proposes an efficient hierarchical framework with three elaborately designed heuristics to solve the above problem. The design of the scan-based algorithms optimizes the subobjectives of feeder allocation and component assignment. First, the allocation heuristic arranges the feeders into slots as a prerequisite for other problems. Then, the component assignment heuristic determines the component type for each head with a variety of criteria and long short-term objectives. Finally, the PAP sequence problem is solved using a modified beam search algorithm. The proposed algorithm offers advantages in terms of effectiveness, efficiency, and extension, which can satisfy various customization demands. Experiments are conducted on our self-designed placement machine using industrial and randomly generated data. Computational experiments show that the scan-based heuristic algorithm obtains near-optimal solutions with a gap of 9.93% averagely compared with the proposed MIP model and provides efficiency improvement over the mainstream studies.

For multiple flexible manipulator systems under an undirected switching topology graph with actuator faults, dead zones, and external disturbances, the proposed distributed control technique aims to attain joint angle consensus and vibration suppression. Therefore, in boundary controller design, the Nussbaum function is utilized to address the issue of uncertain control direction caused by fault tolerance and dead zones. Then, under the condition that the switching topology is connected, by using the backstepping technique, a new Lyapunov function is introduced so that the multiple flexible manipulators system can ensure angle cooperative control and vibration suppression while not knowing the control direction. Moreover, the adaptive law is designed to handle compound disturbances consisting of the unknown dead zones, additive faults, and boundary disturbances. Furthermore, we show that the flexible manipulator system is uniformly ultimately bounded and stable according to the Lyapunov stability theory. Ultimately, through the numerical simulation, the control strategy’s efficacy is confirmed.

In this paper, a robust level set method is proposed for image segmentation. Traditional level set methods are sensitive to noise in images which greatly limits its application in real project. To overcome this shortcoming, the fractional order regularization and Markov random fields term are incorporated into the traditional level methods in this paper. The fractional order regularization can reveal more details of the image and the Markov random field (MRF) term takes the hole image into account. In additional to these two terms, a region term and a penalty term are added into the energy function. The comparison of the proposed method with the classical level set method is made and the results show that the proposed method is robust to noise in images in image segmentation application.