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 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.

Printed circuit board assembly line scheduling (PCBALS) is a difficult task in the electronic industry for assembly lines using surface mounters, which is critical for production efficiency. This is a special type of line optimization problem that uses different allocation techniques, resulting in wide differences in assembly times between machines. This article proposes a hyper-heuristic optimizer embedded with a multifeature fusion ensemble estimator (HHO-MFEE) for PCBALS using linear-aligned-heads surface mounters. The objective and constraints of the problem are discussed, and a min-max integer model for small-scale problems is built. At the hyper-heuristic low level, seven data- and target-driven heuristics are presented for allocating components to different machines. Strategies for duplicated conditions with component types and placement points allocation are proposed to improve the applicability of the algorithm and the quality of the solution. An ensemble assembly time estimator that incorporates the coding of multifeatures, including estimated subobjectives, is proposed for evaluating the quality of the solution. Experimental results show that: 1) the gaps between the solution from HHO-MFEE and the optimal solution of the model are 3.44%~7.28% for small-scale data; 2) the proposed time estimator has higher accuracy than regression and heuristic-based ones, with mean absolute error of 2.01% and 3.43% for training and testing data, respectively; and 3) HHO-MFEE is better than other state-of-the-art algorithms, with average improvement of 7.21%~9.47%.

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.

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.

The development of autonomous laboratories has significantly advanced with the integration of computer vision, simultaneous localization and mapping, cloud computation technologies, etc. These advancements have enhanced the automation and efficiency of experimental processes. However, the optimal management of complex task scheduling within such environments remains underexplored, especially in the face of challenges such as managing numerous tasks, adhering to strict time and state constraints, and ensuring the sustainable stability and performance of the entire laboratory operation. This article introduces a novel model predictive control (MPC)-based strategy for the optimal management of task scheduling in autonomous laboratories. Our approach begins with the abstraction of the scheduling problem as a finite state machine, which lays the foundation for a systematic analysis. We then employ concepts of invariant sets and stability to ensure that the pro posed scheduling strategy is not only efficient but also resilient to operational uncertainties. The proposed approach ensures recursive feasibility, which guarantees the adaptability of the scheduling strategy over time. Through a series of simulations, we demonstrate the efficacy of our MPC-based management strategy in optimizing task scheduling, thereby significantly enhancing the laboratory's operational efficiency, stability, and sustainability. Our findings offer promising insights into the future of autonomous laboratory management, providing a robust framework for tackling the complexities of task scheduling in such environments.

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.

This article proposes a hierarchical multiobjective heuristic (HMOH) to optimize printed-circuit board assembly (PCBA) in a single beam-head surface mounter. The beam-head surface mounter is the core facility in a high-mix and low-volume PCBA line. However, as a large-scale, complex, and multiobjective combinatorial optimization problem, the PCBA optimization of the beam-head surface mounter is still a challenge. This article provides a framework for optimizing all the interrelated objectives, which has not been achieved in the existing studies. A novel decomposition strategy is applied. This helps to closely model the real-world problem as the head task assignment problem (HTAP) and the pickup-and-place sequencing problem (PAPSP). These two models consider all the factors affecting the assembly time, including the number of pickup-and-place (PAP) cycles, nozzle changes, simultaneous pickups, and the PAP distances. Specifically, HTAP consists of the nozzle assignment and component allocation, while PAPSP comprises place allocation, feeder set assignment, and place sequencing problems. Adhering strictly to the lexicographic method, the HMOH solves these subproblems in a descending order of importance of their involved objectives. Exploiting the expert knowledge, each subproblem is solved by an elaborately designed heuristic. Finally, the proposed HMOH realizes the complete and optimal PCBA decision making in real time. Using industrial PCB datasets, the superiority of HMOH is elucidated through comparison with the built-in optimizer of the widely used Samsung SM482.

Optimizing all the objectives of the printed circuit board assembly (PCBA) optimization in a multifunctional placer remains a formidable challenge till now. This article converts the original PCBA optimization problem to a newly defined component allocation problem, which decides the component-type handled by each head per pickup-and-place (PAP) cycle. The component allocation problem is a quadratic 3-D assignment problem (Q3AP) and effectively combines the optimization of all the main objectives. It is possible that one head stays idle, so the assigning 2-D locations are uncertain. We propose the cell division genetic algorithm (CDGA) to solve such a complex Q3AP. The CDGA allocates a component cell as the basic unit. Each of the first-generation component cells contains the mounting points of the same type. A cell chromosome decoding heuristic is designed to determine the next assigning head. By doing so, the problem dimension is reduced, so the conventional GA can be used for searching the optimal component allocation formed by the current-generation cells. When a better allocation can no longer be found by allocating the current cells, the cell division operation is performed to divide each cell into two new cells. The new cells are used in the next round of GA searching, which further optimizes the allocation from two perspectives: better balancing the minimization of nozzle changes and PAP cycles, more flexibly maximizing the simultaneous pickups with the uncertain locations. The CDGA works continuously until the current cells cannot bring any improvement. In simulations and experiments using the industrial samples, the proposed algorithm significantly reduces the PCBA time compared to two recent studies and the built-in optimizer of the widely used multifunctional placer, Hanwha SM482 PLUS, which demonstrates its effectiveness and superiority.