A dynamic point specification approach to sequencing robot moves for PCB assembly

Electronics manufacturers use robots extensively in printed circuit board (PCB) assembly to attain high product quality at low production times. In robotic PCB assembly, fixed pick‐and‐place (FPP) causes unnecessary waste of time. A former paper explored the dynamic pick‐and‐place (DPP) approach to avoid robot waiting time and thus reduce robotic PCB assembly time. To further decrease robot moving or working space when the DPP approach is applied, this paper presents the rotary magazine‐based DPP approach. Experimental results show that the proposed approach not only reduces robot working space, but also reduces assembly time. The more insertions and component types, the more the assembly time is reduced.

The purpose of this paper is to study the temperature effect on the Print Circuit Board (PCB) and the solder joints in the vehicle under working condition of the engine. Thermal-stress test has been used to evaluate the reliability or fatigue life of the PCB using commercially available Finite Element Analysis software, ANSYS. The thermal deformation distribution of the PCB and stress of the solder joints can be obtained. The modal analysis was carried out under the thermal and normal condition. The frequency, mode shapes, deformation and stress were obtained. It is observed that the frequency of the overall PCB assembly was decreased under thermal condition as compared to that under normal condition. The position of maximum temperature was located around the top edge of solder joints, which was close to package. And the position of maximum deformation and stress was also located around the top edge of the solder joints, which was close to package. This paper obtains the thermal stress and thermal deformation of the PCB and solder joints in the vehicle and compare the frequency of the PCB assembly under the thermal and normal condition.

Printed Circuit Boards (PCBs) are widely used in most electronic devices. Typically, a PCB design has a set of components that needs to be assembled. In a broad sense, this assembly task involves placing PCB components at designated location on a PCB board; fixing PCB components; and testing the PCB after assembly operation to ensure that it is in proper working order. The stringent requirements of having a higher component density on PCBs, a shorter assembly time, and a more reliable product prompt manufacturers to automate the process of PCB assembly. Frequently, a few placement machines may work together to form an assembly line. Thus, the application of more than one machine for component placement on a PCB presents a line-balancing problem, which is basically concerned with balancing the workload of all the machines in an assembly line. This paper describes the application of a new artificial intelligence technique known as the immune algorithm to PCB component placement as well as the line balancing of PCB assembly line. It also includes an overview of PCB assembly and an outline of the assembly line balancing problem. Two case studies are used to validate the IA engine developed in this work. The details of IA, the IA engine and the case studies are presented.

Printed circuit board (PCB) assembly lines consist of a number of different machines for mounting electronic components onto PCBs. While high-speed placement machines are employed to assemble standard components, so-called fine-pitch placement machines are used to mount complex electronic components with high precision and by use of specific nozzles. In this paper, we investigate a typical mass production environment where a single type of PCB is assembled in a line comprising high-speed as well as high-precision placement machines. The PCB assembly line balancing problem consists of assigning component feeders, each holding a specific electronic component type, and the corresponding placement operations to machines in the line so as to minimize the assembly cycle time. To solve this problem, a two-stage solution procedure based on genetic algorithm (GA) is proposed. In the first stage, component feeders are assigned to the placement machines with the objective of balancing the workload within the assembly line. A number of candidate solutions are then transmitted to the second stage, where specific machine optimization algorithms are applied to determine the feeder-slot assignment in the component magazine of the machines and the placement sequence of the various components. As a result, fine-tuned placement operation times are achieved which reflect the individual operation mode and the actual component setup of the placement machines. Finally, from the candidate solutions the one which minimizes the actual PCB assembly time is selected.

Production planning and control of the printed circuit board (PCB) assembly includes several decisions dealing with, for example, grouping of PCB jobs, allocation of PCB batches to machine lines, sequencing of batches and load balancing of lines. The production time of a PCB job for a given placement machine is a key factor in this context and it must be quickly and accurately estimated, possibly millions of times in a single planning task, to avoid erroneous decisions. The commonly used nominal tact time-based estimators are very rough and the machine simulators too slow. Therefore, the purpose of this study is to give better machine-specific estimators that avoid the construction the actual machine control program. Two new estimators are proposed for gantry machines, one based on the information given by the manufacturer about the operations of the placement head, and the other on the regularised least-squares regression method trained with a set of PCB placement jobs. In practical evaluation with 95 PCB jobs, the mean absolute percentage error of the first and second methods are 3.75% and 6.52%, respectively, while that of the tact time-based approach is more than 17%. This indicates a great potential of the proposed methods as production time estimators.

A profile identification system for surface mount printed circuit board assembly

This paper investigates solving a group-scheduling problem in the assembly of printed circuit boards (PCBs). The type of setup operation to assemble a group of boards exactly matches with the real application. The kitting operation in the assembly of PCBs which have always been ignored in previous research is paid attention to by introducing the term “integration of external (kitting) and internal (machine) setups,” which eventually results in the development of a dynamic PCB assembly system incorporating the concepts of just-in-time manufacturing. The problem investigated is NP-hard implying the requirement of heuristic approaches for solving industry-size problems. A meta-heuristic algorithm called Tabu Search, which is known to be efficient in solving combinatorial optimization problems, is employed. However, apart from finding the best sequence of jobs, evaluation of the objective function associated with each sequence is still an issue and it cannot be guaranteed to be optimal without the help of a mathematical model. Thus, a decision tree-based approach enabling the search to evaluate each of the sequences is developed including several properties and a lower bound, all of which are demonstrated in this paper.

Recent technological innovations, namely the development of new solder pastes with increased tack life and the introduction of bypass conveyors, facilitate the use of mixed-model lines in printed circuit board (PCB) assembly. This paper presents a simulation study comparing PCB assembly lines operating in conventional batch and in mixed-model assembly mode. For the latter case, a novel group setup strategy is developed. In the first stage, component types and placement operations for each type of PCB are assigned to the machines in the assembly line with the objective of balancing the workload. In the second stage, PCB types with similar component requirements are clustered into setup families. In addition, the feeder-slot assignment for each setup family and the sequence of placement operations for each PCB type are determined. In the final stage, a cyclical mixed-model production schedule is established. The performance of the proposed solution approach is evaluated by means of a simulation study. Two alternative material flow systems are investigated: a serial conveyor which directly links one machine to the other and a flexible one that allows to bypass downstream machines in the line.

In this paper, we consider a repetitive routing problem which we find on a printed circuit board assembly line. There are m different printed circuit boards to be processed. As an automated manipulator embeds electronic parts in a printed circuit board from above, n identical pins from underneath protect it against overbending. A dedicated pin configuration is designed for each printed circuit board so that pins do not obstruct its own circuit. A single grasp-and-delivery robot transfers the pins one by one to arrange them from a configuration to another one. Given an initial configuration of pins and a permutable set of m required configurations, our repetitive routing problem asks to find a configuration sequence, i.e., a processing order of m printed circuit boards, and a transfer route of the grasp-and-delivery robot so that the route length over all m transitions is minimized. We first design a polynomial time approximation algorithm with factor four to a restricted version of the repetitive routing problem with a non-permutable set of configurations, i.e., with a fixed processing order of m printed circuit boards. Applying the procedure, we then show that the repetitive routing problem with a permutable set of configurations admits a polynomial time approximation algorithm with factor six.

It is indisputable that printed circuit boards (PCBs) play a vital role in our daily lives. With the ever-increasing applications of PCBs, one of the crucial ways to increase a PCB manufacturer’s competitiveness in terms of operation efficiency is to minimize the production time so that the products can be introduced to the market sooner. Optimal Production Planning for PCB Assembly is the first book to focus on the optimization of the PCB assembly lines’ efficiency. This is done by: • integrating the component sequencing and the feeder arrangement problems together for both the pick-and-place machine and the chip shooter machine; • constructing mathematical models and developing an efficient and effective heuristic solution approach for the integrated problems for both types of placement machines, the line assignment problem, and the component allocation problem; and • developing a prototype of the PCB assembly planning system. The techniques proposed in Optimal Production Planning for PCB Assembly will enable process planners in the electronics manufacturing industry to improve the assembly line’s efficiency in their companies. Graduate students in operations research can familiarise themselves with the techniques and the applications of mathematical modeling after reading this advanced introduction to optimal production planning for PCB assembly.

The process of manufacturing printed circuit boards and printed circuit board assemblies is very complex. There are numerous process variables with all levels of interactions. These interactions can be difficult to understand and control within a printed circuit board manufacturing facility or printed circuit assembly manufacturing facility. When the interactions span facilities and companies, it takes understanding and controls to prevent latent defects from occurring downstream. What happens when a defect appears that is new, unique and different enough from the typical defect that it is not readily apparent what caused the defect and whether it was caused during the printed circuit board manufacturing process, during the assembly process or, in fact, by an interaction between the two processes? A unique plated-through-hole solder void which did not meet IPC Class II printed circuit board Workmanship requirements as per IPC A610, Acceptability of Electronic Assemblies, occurred on a large number of assemblies.1 There are several questions which immediately arise: What caused the defect? Is the product acceptable for customer shipment? Can the defective product be reworked? Is this a seasonal defect (e.g. laminate voids caused by high levels of moisture in the laminate) or has a variable in the process shifted? A team of engineers from multiple companies worked together to answer these questions.

In recent years, the development of low cost consumer electronic products such as television, mobile, washing machine etc. make high demands on manufacturing of defect free Printed Circuit Board and Printed Circuit Board Assembly. Therefore, it is necessary to know the different types of defects and defect detection techniques of Printed Circuit Board in order to produce high quality zero defect printed circuit boards. This paper discusses various methods which have been used in detecting defects so as to understand the gaps which would be helpful for the researchers to do the future work related to defect detection.

In computer vision, shape recovery is a classic problem. The shape from shading problem aims to reconstruct a 3D shape from one or more intensity images. Inspection of solder joint is a critical step in the assembly of PCB (Printed Circuit Board). So, non-destructive inspection technology has been widely used in a production of PCB. In automated optical inspection (AOI) field 2D inspection appears to be more prevalent industry-wide than 3D inspection, but the limit of 2D inspection in some defects is apparent. This paper deals with the reconstruction of the solder joint's surface in PCB based on shape form shading technology. The advantages of SFS method are analyzed firstly. Then, theory and survey of SFS method is introduced. To overcome the disadvantages existing in conventional SFS methods, some improved algorithms are provided and proved by experiments. Experimental results reveal that the proposed method shows practical value in solder joint inspection.

Process planning is a critical function in any manufacturing domain. This is especially true in the electronics manufacturing area where substantial heuristic and experiential knowledge is commonly used during process plan development. While research in computer-aided process planning (CAPP) has tended to concentrate in the machining realm, few CAPP systems have been developed for electronics manufacturing applications and more specifically for the printed circuit board (PCB) assembly domain. Besides, the CAPP systems that have been developed for this area have dealt with either insertion mount or surface mount PCB assembly processes only.

A production scheduling problem for printed circuit board (PCB) assembly with multiple lines is addressed. The investigated problem is to schedule a set of PCB assembly jobs with different ready times and due dates on multiple assembly lines, with the objective to minimize the sum of total weighted tardiness of jobs and weighted makespan. The feature that each job can be completed by different lines with different process times makes the problem a special type of unrelated parallel-machine scheduling problem (PMSP) with special constraints and a unique objective. A mixed-integer linear programming model for the problem is established and verified through solving some small-sized problem instances to optimality. In order to solve the real-world problems, a steady-state genetic algorithm with an improved replacement strategy is proposed. Experimental tests and a case study are conducted to examine the usefulness and effectiveness of the approach.