Component Placement Sequence Research Articles

An improved spider monkey optimization algorithm for multi-objective planning and scheduling problems of PCB assembly line

In recent years, the demand for printed circuit boards (PCBs) with various electronic components has tremendously increased due to high customization in smart electronic devices. To meet the market competition, efficient and realistic decisions with optimum planning and scheduling are required to enhance the productivity of PCB industries. Processing time of surface mounting technology (SMT) machines defines the pace of PCB production because of time consuming and critical process. In addition, it is observed that various planning and scheduling problems exist in PCB assembly line which are required to emphasize to increase the productivity. Therefore, in this study critical planning and scheduling problems i.e., component allocation problem (CAP) and component placement sequence problem (CPSP) are considered to optimize. Moreover, the periodic maintenance (PM) is also considered to maintain the efficiency of machines and make the problem more realistic. A mathematical model is formulated to minimize the maximum completion time, total energy consumption and the total maintenance time simultaneously. To solve this multi-objective NP-hard problem, an improved spider monkey optimization (ISMO) algorithm is developed. The efficiency and effectiveness of ISMO algorithm is improved by embedding few additional features. a) Two heuristics to ensure better initial solutions; b) evolutionary state judgment based on change in Pareto entropy to ensure the tradeoff between exploration and exploitation; c) an archive-based Q-Learning strategy to ensure the parametric adaptive tuning. Finally, computational experiments and comprehensive performance comparisons with other well-known multi-objective algorithms are performed with several size problem instances. The statistical results and analysis demonstrate that the proposed ISMO algorithm outperforms the other competitors.

Applied Research of Hierarchical Multi-objective Optimization Method in High Speed and High Precision Placement Machine

In practical engineering, placement optimization of multi-head in the high speed and high precision Surface Mount Technology (SMT) is a multi-objective optimization problem. It mainly includes the feeder distribution optimization, the component placement sequence optimization and the nozzle configuration optimization. Optimization problems are complicated and mutual coupling between them. In this paper, the hierarchical multi-objective optimization model is developed. In the hierarchical structure, the nozzle optimization is constructed as a 0-1 integer programming optimization model for the first master hierarchy. In the second hierarchical, component mounting sequence, feeder optimization and picking order are parallel optimized based on the first hierarchy optimization. The respective problems related algorithm strategies have been proposed. The engineering experiments show that this hierarchical multi-objective optimization method can significantly improve the overall performance of the multi-head placement machine with high speed and high precision.

Hybrid spider monkey optimisation algorithm for multi-level planning and scheduling problems of assembly lines

The production planning and scheduling problems of printed circuit board (PCB) assembly line robustly influence the production efficiency of PCB industries. The current study focuses on the optimisation of the multi-level planning and scheduling problem by minimising the cycle time of the PCB assembly lines. Two levels of planning problems i.e. component allocation problem (CAP) and component placement sequence problem (CPSP) are solved simultaneously using mixed integer linear programming model. In CAP, a model is formulated with the objective of balancing the workload among the surface mounted machines (SMM), while in CPSP, a model is formulated to find optimum sequencing for allocated components at each SMM. A novel hybrid spider monkey optimisation (HSMO) algorithm is proposed with the addition of new sorting food sources and genetic operators in the standard spider monkey optimisation (SMO) algorithm. The performance of the proposed HSMO algorithm is validated by comparing the solutions with well-known algorithms, i.e. genetic algorithm (GA), particle swarm optimisation (PSO), simulated annealing (SA) and artificial bee colony (ABC) algorithms. The proposed HSMO algorithm is tested on different problem set instances scaled based on the realistic production of PCB industries. The detailed analysis of results indicates that the proposed HSMO algorithm outperforms the compared algorithms in efficiency and effectiveness.

Printed circuit board assembly time minimisation using a novel Bees Algorithm

This paper presents a novel version of the Bees Algorithm customised to solve combinatorial optimisation problems. This version was created to minimise assembly time in the manufacturing of printed circuit boards using a machine of the moving-board-with-time-delay type, and optimising the feeder arrangement and machine component placement sequence. The local search procedure of the standard Bees Algorithm was modified to include five new operators for combinatorial optimisation. The customised Bees Algorithm was first tested on the related travelling salesman problem, where it excelled in terms of performance and efficiency compared to three state-of-the-art optimisation methods. It was then applied to a well-known moving-board-with-time-delay benchmark problem, where it performed favourably in comparison to the state-of-the-art in the literature, achieving fast and consistent solutions.

Application of an improved Spider Monkey Optimization algorithm for component assignment problem in PCB assembly

This paper presents a new optimization method for printed circuit board assembly (PCBA) process based on improved spider monkey optimization (SMO) algorithm, and a production model for PCBA is created. The proposed method proves to be suitable for product-service system(PSS). PCBA process mainly consists of three parts. First is the assignment of PCB, second is the assignment of machines, the last is the component assignment problem. In this research, the SMO algorithm is applied on optimization of component assignment which is divided into two stages: local leader phase (LLP) and global leader phase (GLP). In the first stage, the electronic components are assigned to the feeders by GLP. In the second stage, the component placement sequence, which is apparently known as a travelling salesman problem (TSP), is determined by LLP. Numerical experiments are performed to compare the performance of SMO algorithm with others under various experimental settings, the results show that the proposed method is more effective than other traditional methods. A production model is created with the proposed method, which is calimed to be able to increase the efficiency of the product services.

Multi-Level Planning and Scheduling for Parallel PCB Assembly Lines Using Hybrid Spider Monkey Optimization Approach

Printed circuit board (PCB) assembly lines are significant to produce a variety of electronic products. PCB manufacturing industries tend to move towards automated and complex manufacturing system due to an increase in the customer demand for more sophisticated products. PCB assembly process involves highly interrelated levels of planning and scheduling problems. Therefore, the current research investigates multi-level planning and scheduling of PCB assembly lines, which include line assignment to the PCB models, component allocations to machines, and component placement sequencing by machines on PCB boards. A mixed integer-programming model is developed to integrate the planning and scheduling problem of parallel PCB assembly lines to maximize the net profit. A hybrid spider monkey optimization (HSMO) algorithm is proposed to solve the multi-level planning and scheduling problem. The performance of the proposed HSMO algorithm is compared to artificial bee colonial (ABC), genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms. Moreover, the proposed HSMO algorithm is validated against ABC, GA, PSO, and SA algorithms with the case problem taken from well-reputed PCB manufacturing industry in China. The computation experiments indicate that the proposed HSMO algorithm can achieve good near-optimal solutions when compared with the other-mentioned four algorithms based on performance and efficiency for benchmark problems and real case problem.

Modeling of Order Oriented Planning for Printed Circuit Board Assembly Line

Printed circuit board (PCB) assembly planning problem has been given lot of attention to efficiently manage the orders of different PCB products. However, little effort has been paid to describe its comprehensive model which can demonstrate all the planning problems collectively considering in the surface mount technology (SMT) lines. A mixed integer model for PCB assembly line which includes the integration of different levels of the planning problems and considered component allocation problem (line balancing), PCB model sequencing problem (mixed model sequencing problem), feeder arrangement problem and component placement sequencing problem on different SMT machines collectively. Current research is significant to solve different planning problems of the PCB assembly line simultaneously to get the overall global solution of the problem in future.

PCB Assembly Process Optimization based on Constraint Model

The structure and assembly process of Chip Shooter (CS) machine are studied. The factors of engineering practice such as the component size and machine structure etc. are considered. The assembly optimization model of Printed Circuit Board (PCB) on CS machine is formulated. A hybrid genetic algorithm (HGA) which combine with Polychromatic Sets (PS) and Genetic Algorithm (GA) is presented. A binary piece-wise coding method is proposed. In the individual chromosome, feeder arrangement and component placement sequence is described simultaneously. Constraint model is used to consider the impact of component size on the assembly speed and the feeder arrangement of CS machine. Meanwhile the constraint model improved the quality of initial population, ensured the effectiveness of GA operation and narrowed the search space of GA obviously. Calculation example indicated that HGA optimize the component sequence and feeder arrangement simultaneously and achieve excellent practicability. The assembly efficiency of CS machine is improved obviously.

基于蚁群算法的PCB组装过程优化<br>PCB Assembly Optimization Based on Ant Colony Algorithm

在转塔式贴片机的印制电路板(PCB)组装过程中，元件贴装顺序和装载有不同类型元件的供料器在供料架上的布置是影响转塔式贴片机贴装时间的主要因素。在分析实际工程应用的基础上，建立了转塔式贴片机上PCB组装的集成优化模型，对蚁群算法进行了改进，提出了相互通信的最大–最小蚂蚁算法，用两种不同职能的蚂蚁相互协作，以元件组装顺序来驱动供料器布置，引导蚂蚁实现元件贴装顺序的优化，而执行蚂蚁根据引导蚂蚁选择元件的结果来实现供料器布置优化。算法可对元件贴装顺序和供料器的布置进行同时优化，从而提高转塔式贴片机上PCB组装的效率。最后通过实例验证了算法的有效性。 The component placement sequence and feeder arrangement are the critical factors determining assembly time of chip shooter (CS) machine. In addition, the different size of component and different arrangement strategy affect the feeder arrangement and component placement sequence. Based on the engineering analysis, an integrated optimization model of printed circuit board (PCB) assembly for CS machine is established. According to the parallel placement character of CS machine, “Max-Min Ant Colony Algorithm with Communication function” is designed based on traditional Ant Colony Algorithm. The idea that two ants with different duties collaborate to solve the optimization problem is presented. Guide ants optimize placement sequence while executant ants optimize feeder arrangement according to the components placement sequence. The component placement sequence and feeder arrangement are optimized simultaneously.

Optimizing the Performance of Chip Shooter Machine Based on Ant Colony Algorithm

The component placement sequence and feeder arrangement are two critical factors determining the assembly time of chip-shooter machine (CS). In addition, the different size of component and different arrangement strategy affect the feeder arrangement and component placement sequence. Based on the engineering analysis, an integrated optimization model of printed circuit board (PCB) assembly for CS machine is established. According to the parallel placement character of CS machine, "Max-Min Ant Colony Algorithm with Communication function (MMAC)" is designed based on traditional Ant Colony Algorithm. The idea that two ants with different duties collaborate to solve the optimization problem is presented. Guide ants optimize placement sequence while executant ants optimize feeder arrangement according to the components placement sequence. The component placement sequence and feeder arrangement are optimized simultaneously

An Efficient and Cost-Saving Component Scheduling Algorithm Using High Speed Turret Type Machines for a Board Containing Multiple PCBs

This paper considers a material constrained component scheduling problem during the high speed surface mount manufacturing stage in printed circuit board (PCB) assembly, where each piece of board contains an even number of identical PCBs. To accomplish the production, material requirements must be predetermined and incorporated as restraints into the scheduling problem, which has the objective of minimizing production completion time (makespan). A solution procedure is developed based on the following strategies: 1) Each machine is responsible for the same PCBs of each piece, 2) Components of the same types may use one or more feeder locations, 3) Component types are clustered based on their suitable placement speeds, 4) A heuristic using a bottom-up approach is applied to determine the component placement sequence and the feeder location assignment for all machines. Velocity estimate functions of the turret, XY table, and feeder carriage were derived based on empirical data. An experiment using Fuji CP732E machines was conducted on two real life instances. Experimental results indicate that our method performs 32.96% and 10.60% better than the Fuji-CP software for the two instances, in terms of the makespan per piece of board.

An aggregated optimization model for multi-head SMD placements

In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads, minimizing the number of nozzle exchanges, and improving handling class. The handling class which specifies the traveling speed of the robot arm, to the best of our knowledge, has been for the first time incorporated in an optimization model. While the MIP produces an optimal planning for batches of components, a new sequencing heuristics is developed in order to determine the final sequence of component placements based on the outputs of the MIP. This two-stage approach guarantees a good feasible solution to the multi-head SMD placement optimization problem. The computational performance is examined using real industrial data.

An Aggregated Optimization Model for Multi-Head SMD Placements

In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads, minimizing the number of nozzle exchanges, and improving handling class. The handling class which specifies the traveling speed of the robot arm, to the best of our knowledge, has been for the first time incorporated in an optimization model. While the MIP produces an optimal planning for batches of components, a new sequencing heuristics is developed in order to determine the final sequence of component placements based on the outputs of the MIP. This two-stage approach guarantees a good feasible solution to the multi-head SMD placement optimization problem. The computational performance is examined using real industrial data.

The optimisation of the single surface mount device placement machine in printed circuit board assembly: a survey

This article surveys research on the single surface mount device (SMD) placement machine optimisation problem. We classify the optimisation problem into five sub-problems: feeder setup, component placement sequencing, nozzle optimisation, component retrieval plan and motion control; and analyse issues relevant to each of these. One of the aims of this article is to provide guidance to other researchers and gain a deeper understanding of the various optimisation issues that arise in this domain. This could lead to the design of improved heuristics, which are more appropriate to the real-world scheduling problem of the SMD placement machine.

Optimizing PCB assembly for family setup strategy

PurposeThe purpose of this paper is to investigate the optimization for a placement machine in printed circuit board (PCB) assembly when family setup strategy is adopted.Design/methodology/approachA complete mathematical model is developed for the integrated problem to optimize feeder arrangement and component placement sequences so as to minimize the makespan for a set of PCB batches. Owing to the complexity of the problem, a specific genetic algorithm (GA) is proposed.FindingsThe established model is able to find the minimal makespan for a set of PCB batches through determining the feeder arrangement and placement sequences. However, exact solutions to the problem are not practical due to the complexity. Experimental tests show that the proposed GA can solve the problem both effectively and efficiently.Research limitations/implicationsWhen a placement machine is set up for production of a set of PCB batches, the feeder arrangement of the machine together with the component placement sequencing for each PCB type should be solved simultaneously so as to minimize the overall makespan.Practical implicationsThe paper investigates the optimization for PCB assembly with family setup strategy, which is adopted by many PCB manufacturers for reducing both setup costs and human errors.Originality/valueThe paper investigates the feeder arrangement and placement sequencing problems when family setup strategy is adopted, which has not been studied in the literature.

An integrated scheduling problem of PCB components on sequential pick-and-place machines: Mathematical models and heuristic solutions

This paper formulates several mathematical models for determining the optimal sequence of component placements and assignment of component types to feeders simultaneously or the integrated scheduling problem for a type of surface mount technology placement machines, called the sequential pick-and-place (PAP) machine. A PAP machine has multiple stationary feeders storing components, a stationary working table holding a printed circuit board (PCB), and a movable placement head to pick up components from feeders and place them to a board. The objective of integrated problem is to minimize the total distance traveled by the placement head. Two integer nonlinear programming models are formulated first. Then, each of them is equivalently converted into an integer linear type. The models for the integrated problem are verified by two commercial packages. In addition, a hybrid genetic algorithm previously developed by the authors is adopted to solve the models. The algorithm not only generates the optimal solutions quickly for small-sized problems, but also outperforms the genetic algorithms developed by other researchers in terms of total traveling distance.

Development of a genetic algorithm for component placement sequence optimization in printed circuit board assembly

This paper describes the development and evaluation of a custom application exploring the use of genetic algorithms (GA) to solve a component placement sequencing problem for printed circuit board (PCB) assembly. In the assembly of PCB’s, the component placement process is often the bottleneck, and the equipment to complete component placement is often the largest capital investment. The number of components placed on a PCB can range from few to hundreds. As a result, developing an application to determine an optimal or near-optimal placement sequence can translate into reduced cycle times for the overall assembly process and reduced assembly costs. A custom application was developed to evaluate the effectiveness of using GA’s to solve the component placement sequencing problem. A designed experiment was used to determine the best representation and crossover type, crossover rate, and mutation rate to use in solving a component sequencing problem for a PCB consisting of 10 components being placed on a single-headed placement machine. Three different representations (path, ordinal, and adjacency) and six appropriate crossover types (partially mapped, ordered, cycle, classical, alternating edges, and heuristic) were evaluated at three different mutation rates and at 11 crossover rates. Two algorithm response variables, the total distance traveled by the placement head and the algorithm solution efficiency (measured as number of generations and algorithm solution time) were used to evaluate the different GA applications. The combination of representation and crossover type along with mutation rate were found to be the most significant parameters in the algorithm design. In particular, path representation with order crossover was found to produce the best solution as measured by the total distance traveled as well as the solution generation efficiency. Increasing the mutation rate led to slightly improved solutions in terms of head travel, but also resulted in increased solution time.

Optimization of PCB component placements for the collect-and-place machines

This paper presents two hybrid genetic algorithms (HGAs) to optimize the component placement operation for the collect-and-place machines in printed circuit board (PCB) assembly. The component placement problem is to optimize (i) the assignment of components to a movable revolver head or assembly tour, (ii) the sequence of component placements on a stationary PCB in each tour, and (iii) the arrangement of component types to stationary feeders simultaneously. The objective of the problem is to minimize the total traveling time spent by the revolver head for assembling all components on the PCB. The major difference between the HGAs is that the initial solutions are generated randomly in HGA1. The Clarke and Wright saving method, the nearest neighbor heuristic, and the neighborhood frequency heuristic are incorporated into HGA2 for the initialization procedure. A computational study is carried out to compare the algorithms with different population sizes. It is proved that the performance of HGA2 is superior to HGA1 in terms of the total assembly time.

Modelling the operations of a component placement machine with rotational turret and stationary component magazine

Two major problems faced in the automated assembly of printed circuit boards are the determination of a component placement sequence and the internal arrangement of the component tapes in the feeder mechanism. The forms of these problems depend mainly on the physical design of the placement machine. The definition and solution of these optimization problems are attempted by many researchers and it is possible to find lots of relevant papers in the literature. However, most of these studies are concentrated on certain machine designs. In this study, the focus is on a particular machine type that has a rotational turret and a stationary component magazine. So far, this type of machine has received little attention among the researchers. The definition of the placement sequencing and feeder configuration problems are given and solution procedures are suggested. The performance of the suggested procedures are tested on real and randomly generated PCB assembly data.

A genetic-based algorithm for the operational sequence of a high speed chip placement machine

Surface mount component placement machines are widely used in electronic manufacturing industry for automated placement of components on printed circuit boards. In this paper, we propose a new approach to the component placement problem using high speed turret style chip shooter machine and investigate the case of one single machine and one board type case with the objective of minimizing the assembly (cycle) time per board. The proposed method first groups the component types that can be processed at the same machine speed. Then the minimum spanning tree technique is employed to perform feeder duplications, reducing the distance effect between components of each type. Finally, a genetic-based algorithm with 2-opt local search using feeder arrangement list as solution representation is applied to determine the component placement sequence. Our experimental results indicate that the algorithm produces satisfactory solutions when a lower bound on cycle time per board is used as the evaluation criterion. It is also shown that the overuse of feeder duplication will produce a negative effect on the cycle time. Finally, the algorithm can help management to make a production plan that takes both component inventory cost and cycle time into consideration.