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This paper, which will be published in two parts in consecutive issues of Soldering & Surface Mount Technology, reproduces a chapter of the recently published book ‘Handbook of Printed Circuit Technology: New Processes, New Technologies’, edited by G. Herrmann and K. Egerer and published by Electrochemical Publications Ltd, Port Erin, Isle of Man.

To determine the optimal chip/component placement for multi‐chip module (MCM) and printed circuit board (PCB) under thermal constraint.

The placement of power dissipating chips/component is carried out using genetic algorithms (GA) in order to achieve uniform thermal distribution on MCM and PCB. The thermal distribution on the MCM and PCB are predicted using 2D‐finite element method (FEM) analysis. Different number of chip/component and FEM meshing size is used to investigate the placement of chips/components.

The optimal placement of chip/component using GA is compared well to other placement techniques. The coarse meshing for FEM employed here is found adequate to carry out optimal placement of components by GA.

The analysis is valid for constant properties of MCM or PCB and steady state conditions. The chip/component size is limited to a single standard size.

The method is very useful for practical design of chip/component placement on MCM/PCB under thermal consideration.

FEM analyses of MCM and PCB can be easily implemented in the optimization procedure for obtaining the optimal chip/component placement based on thermal constraints.

The purpose of this paper is to propose an effective and efficient solution method for the component allocation problem (CAP) in printed circuit board (PCB) assembly, in order to achieve high‐throughput rates of the PCB assembly lines.

The investigated CAP is intertwined with the machine optimization problems for each machine in the line because the latter determine the process time of each machine. In order to solve the CAP, a solution method, which integrates a meta‐heuristic (genetic algorithm) and a regression model is proposed.

It is found that the established regression model can estimate the process time of each machine accurately and efficiently. Experimental tests show that the proposed solution method can solve the CAP both effectively and efficiently.

Although different regression models are required for different types of assembly machines, the proposed solution method can be adopted for solving the CAPs for assembly lines of any configuration, including a mixed‐vendor assembly line.

The solution method can ensure a high‐throughput rate of a PCB assembly line, and thus improve the production capacity without further investment on the expensive PCB assembly equipment.

The paper proposes an innovative solution method for the CAP in PCB assembly. The solution method integrates the meta‐heuristic method and the regression method, which has not been studied in the literature.

This paper aims to study the component pick-and-place (P&P) defect patterns for different root causes based on automated optical inspection data and develop a root cause identification model using machine learning.

This study conducts experiments to simulate the P&P machine errors including nozzle size and nozzle pick-up position. The component placement qualities with different errors are inspected. This study uses various machine learning methods to develop a root cause identification model based on the inspection result.

The experimental results revealed that the wrong nozzle size could increase the mean and the standard deviation of component placement offset and the probability of component drop during the transfer process. Moreover, nozzle pick-up position can affect the rotated component placement offset. These root causes of defects can be traced back using machine learning methods.

This study provides operators in surface mount technology assembly lines to understand the P&P machine error symptoms. The developed model can trace back the root causes of defects automatically in real line production.

The findings are expected to lead the regular preventive maintenance to data-driven predictive and reactive maintenance.

In order to handle high and rapid production demands, printed circuit board (PCB) manufacturers have employed high‐speed surface mount machines into their assembly lines. These machines have abilities of fast component placements, but provide challenges for process engineers to optimise the component placement sequences and feeder arrangements via effective planning. A computer program was developed based on operating concepts using genetic algorithm, to solve for various component placement sequencing planning of the high‐speed chipshooter. Genetic algorithms are a class of general purpose search methods based on the concepts of genetic evolution and survival of the fittest. The program provides information on component placement sequences and feeder arrangements for optimal assembly times. Initial tests have shown that the size of the parent space affects the convergence of the solutions during iterations. Finally, comparisons of results have shown improvement over those previously obtained by other researchers.

A printed circuit board (PCB) assembly consists of a circuit board and a variety of components in different shapes and sizes placed at specific locations on the board. They are used extensively in a variety of products by many industries today. These products include, for example, computer terminals, televisions, electronic instruments, and process controllers. There are many types and models of automatic machines available for placing components on the boards. The component placement operation consists of all steps necessary to remove a component from its packaging materials and mount it onto the PCB. The components are supplied either from a set of feeders, magazines or matrix trays. Because of the extreme accuracy required, automatic equipment is mandatory for all but the smallest production volumes.

This paper, which will be published in two parts in consecutive issues of Soldering & Surface Mount Technology, reproduces a chapter of the recently published book ‘Handbook of Printed Circuit Technology: New Processes, New Technologies’, edited by G. Herrmann and K. Egerer and published by Electrochemical Publications Ltd, Port Erin, Isle of Man.

Surface mount technology (SMT) is being increasingly used in printed circuit board (PCB) assembly. The reduced lead pitch of surface mount components coupled with their increased lead count and packing densities have made it imperative that automated placement methods be used. However, the SMT placement process is often a bottleneck in surface mount manufacturing. A reduction in placement time in SMT will enhance throughput and productivity. This paper describes the design and development of a prototype expert system based approach which identifies ‘near’ optimal placement sequences for surface mount PCBs in (almost) realtime. The software structure used integrates a knowledge based system with an optimisation module. PROLOG is the language used in this research. The system was rigorously validated and tested. Ideas for further research are also presented.

Little interest has been shown in pickup conditions and parameters and their effect on placement accuracy in the literature before. The purpose of this paper is to find out the possible link between pickup conditions and placement accuracy of typical discrete chip components.

A dedicated test board was developed and used to study the ultimate critical pickup conditions. Then the same board was used to find out the best parameters between ultimate conditions and perfect conditions in order to define working limits for good enough pickup that would work well in practice.

The link between pickup conditions and placement quality was found and converted into measurable controllable values. Additionally, a problem was surprisingly detected in the placement machine's vision performance resulting in inaccuracy, and parameters were re‐defined to avoid this problem in real‐world production. Based on all the findings, the best parameters were defined for component pickup.

This paper discusses the effect of component pickup conditions on accuracy which is seldom handled in the literature. Owing to smaller spacing between chip components in the future, pickup is becoming more important; components will simply have to be picked up more and more on the centre to avoid collision with components already been placed. This paper clearly shows the requirement for placement machine manufacturers to develop more accurate pickup tools for the future.

Printed wiring board placement optimization of high‐speed placement machines in a high volume surface mount line was studied based on more careful analysis of the board layout beforehand. In this method, the target is to get the board area to be assembled as small as possible in each successive placement machine, paying the most attention on component coordinates already during preliminary line balancing phase. Optimization and line balancing principles, that had showed promising results already in the first studies when compared to globally used well‐known commercial optimization systems, were now further developed. Results presented and illustrated in this paper show remarkable, comparable, improvement in both cycle time reduction and in balance of the whole surface mount line. The main difference between this method and the advanced commercial solutions is explained in detail. Board layout, feeder arrangement, sequence of fiducial registration, and XY‐table movement were taken into deeper consideration in the method.