Total Assembly Time Research Articles

Research on complex products assembly sequence planning and its application based on PSO-GA

Abstract A PSO-GA-based method is proposed to improve the efficiency of complex product assembly sequence planning. We minimize total assembly time by optimizing the assembly sequence, considering the number of tool changes and assembly direction changes as evaluation criteria. A genetic algorithm (GA) is introduced into particle swarm optimization (PSO) to solve and verify assembly sequence planning. Finally, the effectiveness and rationality of the hybrid algorithm are verified by taking a certain type of gear pump as an example.

Design Improvements by Using the Design for Assembly (DFA) Method in Elevator Production

The purpose of this research is to show the benefits to be obtained by redesigning production processes for small and medium-sized enterprises, which aim to grow but cannot develop due to runaway production. In this study, an exemplary approach is presented using the principles of Design for Assembly (DFA) in an enterprise aiming to grow in the elevator industry. Within the scope of the research, the doorframe assembly process of the semi-automatic elevator door, which is the most produced in the elevator industry, was examined. According to this; Design improvement studies were carried out in the assembly processes of the side profiles, head plate, forehead plate, floor plate and their subparts used in the production of the case. In the design improvement, the B&D design efficiency measurement principles were used. As a result of the improvements; The semi-automatic door frame, which is 98 pieces in its current design, has been reduced to 55 pieces with the new design. With the reduction in the number of parts and design changes, the total assembly time was reduced by 33.8% in assembly time. As a result of the improvements, the Design Efficiency has been increased from 8.5% to 12.8%.

The Optimization of Vapor Compression Type for Desalination of Seawater Using the DFMA Method

The design of vapor compression type distillation using the design for manufacture and assembly (DFMA) method was discussed in this article. The efficacy of seawater desalination is the primary source of non-conventional fresh water in a large number of countries around the world. The distillation process can satisfy the demands for high-quality fresh water by utilizing seawater. DFMA is a technique for product development and improvement that could be used to simplify the manufacturing process and reduce assembly costs. This research focuses on creating an effective vapor compression-type desalination device. The DFMA method is used in this reverse engineering study. According to the findings of this study, the total assembly time of 22 components was 7932 seconds. Compared to the previous process, processing time efficiency increased by 4.1 %.

Solving the feeder assignment, component sequencing, and nozzle assignment problems for a multi-head gantry SMT machine using improved firefly algorithm and dynamic programming

This paper proposes a two-stage framework for developing hybrid approaches to solve the three printed circuit board assembly (PCBA) problems, component sequencing problem (CSP), feeder assignment problem (FAP), and nozzle assignment problem (NAP), simultaneously for a multi-head gantry SMT (surface-mounting technology) machine. The three essential PCBA problems affect the productivity of a multi-head gantry SMT machine considerably. However, due to the difficulty and complexity, the NAP (which assigns a given set of nozzles to the assembly heads of a machine) has been often neglected or roughly resolved in past research. The lack of considering the NAP leads to the underestimation of the total assembly time required for one printed circuit board (PCB) and eventually results in inaccurate production planning. The two-stage framework consists of metaheuristic and dynamic programming (DP) methods, which can deal with the three PCBA problems systematically. Various metaheuristics including firefly algorithm (FA), improved FA (IFA), genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA) have been respectively used in this framework and experiments have been conducted to investigate their effectiveness. The results show that the hybridization of IFA with DP outperforms the others in terms of total assembly time.

Research on Optimizing Sewing Processes for the Welt Pocket and Zipper of Jackets

This study uses the MTM time measurement method and the GSD predetermined time system to conduct the construction of the machining time norms for the welt pocket and zipper of jackets. The use of automatic welting machine has significantly saved (35.4%) total operations and 31.06% total assembly time. The programming machine that is used saves most of the time in processing the welt pockets and zipper up to 24.5%, especially integrating 2 stages of sewing the welt pocket and zipper in the same process with the machine. It saved 30% times of operations. Initial research results have provided data as the basis for solutions to increase labor productivity and quality of jacket production.

Using an Improved Differential Evolution for Scheduling Optimization of Dual-Gantry Multi-Head Surface-Mount Placement Machine

The difference between dual-gantry and single-gantry surface-mount placement (SMP) machines is that dual-gantry machines exhibit higher complexity and more problems due to their additional gantry robot, such as component allocation and collision. This paper presents algorithms to prescribe the assembly operations of a dual-gantry multi-head surface-mount placement machine. It considers five inter-related problems: (i) component allocation; (ii) automatic nozzle changer assignment; (iii) feeder arrangement; and (iv) pick-and-place sequence; it incorporates a practical restriction related to (v) component height. The paper proposes a solution to each problem: (i) equalizing “workloads” assigned to the gantries, (ii) using quantity ratio method, (iii) using two similarity measurement mechanisms in a modified differential evolution algorithm with a random-key encoding mapping method that addresses component height restriction, (iv) and a combination of nearest-neighbor search and 2-opt method to plan each placing operation. This study reports an experiment that involved the processing of 10 printed circuit boards and compared the performance of a modified differential evolution algorithm with well-known algorithms including differential evolution, particle swarm optimization, and genetic algorithm. The results reveal that the number of picks, moving distance of picking components, and total assembly time with the modified differential evolution algorithm are less than other algorithms.

Efficient hybrid group search optimizer for assembling printed circuit boards

AbstractAssembly optimization of printed circuit boards (PCBs) has received considerable research attention because of efforts to improve productivity. Researchers have simplified complexities associated with PCB assembly; however, they have overlooked hardware constraints, such as pick-and-place restrictions and simultaneous pickup restrictions. In this study, a hybrid group search optimizer (HGSO) was proposed. Assembly optimization of PCBs for a multihead placement machine is segmented into three problems: the (1) auto nozzle changer (ANC) assembly problem, (2) nozzle setup problem, and (3) component pick-and-place sequence problem. The proposed HGSO proportionally applies a modified group search optimizer (MGSO), random-key integer programming, and assigned number of nozzles to an ANC to solve the component picking problem and minimize the number of nozzle changes, and the place order is treated as a traveling salesman problem. Nearest neighbor search is used to generate an initial place order, which is then improved using a 2-opt method, where chaos local search and a population manager improve efficiency and population diversity to minimize total assembly time. To evaluate the performance of the proposed HGSO, real-time PCB data from a plant were examined and compared with data obtained by an onsite engineer and from other related studies. The results revealed that the proposed HGSO has the lowest total assembly time, and it can be widely employed in general multihead placement machines.

A Methodology for Assembly Sequence Optimization by Hybrid Cuckoo-Search Genetic Algorithm

In this paper, we have proposed and implemented a methodology for assembly sequence optimization by using a nature-inspired metaheuristic algorithm, known as hybrid cuckoo-search genetic algorithm (CSGA). The cost criteria for optimization in the present formulation takes into consideration the total assembly time and the number of reorientations during the assembly process. To demonstrate the application of the CSGA, an example assembly containing 19 parts has been presented and the results have been compared with those of another metaheuristic algorithm, Genetic Algorithm (GA). From the results, it has been observed that for the given problem, the CSGA not only produces optimal assembly sequences with costs comparable to that of GA, but the convergence of CSGA algorithm has been found to be faster than the GA algorithm.

Mechanical connections of the precast concrete columns with detachable metal plates

SummaryThe aim of this study is to develop laminated metal plates for a moment connection of the precast concrete columns. Metal plates were proposed as a time‐ and cost‐saving alternative to conventional monolithic cast‐in‐place joints for connecting precast concrete frames. This study investigated the possibility of using mechanical joints for the connections of both steel‐concrete composite precast frames and reinforced concrete precast frames. A full erection test of precast columns connected with laminated metal plates was performed to demonstrate the efficiency and simplicity of the proposed method. It was shown that the use of the novel mechanical joints with steel plates significantly reduces construction time compared with the conventional monolithic assembly. The total precast column assembly time was approximately 10–20 min, eliminating pour forms and curing times required for the conventional concrete frames. This study also presents an analytical modeling of the proposed connection for multibay precast frames to explore the strains and stress exerted on the joints.

Modified artificial bee colony algorithm for scheduling optimization for printed circuit board production

The production scheduling of placement machines strongly influences the efficiency of printed circuit board (PCB) production. This study focuses on the scheduling optimization for PCB production, that is, on maximizing throughput and minimizing total assembly time and head moving distance. Three problems—the automatic nozzle changer (ANC) assignment problem, the nozzle assignment problem, and the component pick-and-place sequence problem—are investigated using a modified artificial bee colony algorithm (MABCA) to enhance the production efficiency of placement machines.The proposed scheduling optimization algorithm comprises three phases. First, the proportional distribution method is applied to optimize ANC assignment. Second, considering the component height as well as the pick, simultaneous pickup, and component shape restrictions, the MABCA generates the nozzle assignment. Third, under the constraints of place restriction and the predetermined nozzle assignment, the MABCA and the 2-opt algorithm generates the place sequence with the minimal PCB assembly time. Finally, the proposed algorithm is experimentally demonstrated. The average number of pickups in the solutions generated by the proposed method, the onsite engineer, and two related studies are 159.3, 167.2, 208.6, and 210.6, respectively. The proposed approach yields the least PCB assembly time. The average total assembly time (Ttotal) improved between on-site engineer and the proposed MABCA is 4.08%.

Optimization of printed circuit board component placement using an efficient hybrid genetic algorithm

The hardware restrictions of surface mount placement machines, such as height, pick and place restrictions, and simultaneous pickup are often in printed circuit board (PCB)-related studies. This study proposes an efficient hybrid genetic algorithm (HGA) for solving the nozzle assignment problem and the component pick and place sequence problem. First, the proposed method obtains the sequence of the automatic nozzle changer (ANC) with the maximum number of simultaneous pickups and the minimum number of picks as the solution of the nozzle setup problem. Then, the proposed method uses the nearest neighbor search (NNS), 2-optimization, and a genetic algorithm (GA) with the known ANC sequences to obtain the PCB assembly time with the optimal component pick and place sequence. Experiments are conducted on the PCB of the EVEST EM-780 surface mount placement machine. Results show that the proposed HGA gives the lowest total number of picks, the shortest total head movement distance, and the minimum total PCB assembly time compared to those of other methods.

Including Material Exposure and Part Attributes in the Manual Assembly Line Balancing Problem

Assembly line balancing problem (ALBP) purpose is to assign assembly tasks to workstations optimizing a certain objective function. Despite this problem has been studied for decades, few contributions consider material exposure and part characteristics in ALBP. Furthermore, these features have relevant and practical implications for ALBP in the industrial context. This research proposes an innovative optimization model distinguished by different objective functions to properly balance assembly lines considering the aforementioned features. The model is tested and validated with a real industrial case study. The results demonstrates the importance of including material exposure and the optimal material storage within the assembly station in the assembly line balancing problem due to a strong influence of these on picking time, and so on the total assembly time.

Integrated assembly sequence planning and assembly line balancing with ant colony optimization approach

This paper proposes an approach for solving integrated assembly sequence planning and assembly line balancing with ant colony algorithm. In integrated assembly sequence planning and assembly line balancing, the assembly task priority diagram is built based on the assembly information, and the assembly sequences are evaluated in each workstation of the assembly line separately; for calculating the total assembly time in the workstation, besides the assembly task time, the time for changing assembly directions, changing assembly tools, and the time for moving the heavy parts in the workstation are also considered. The fitness function is built and the constraints are discussed; an ant colony algorithm is proposed to carry out assembly sequence planning and assembly line balancing simultaneously, based on the searching mechanism and the pheromone updating mechanism, to obtain the optimal solutions under the given cycle time. Finally, case studies are given to verify the proposed approach.

Steps within the Assembly of HIV Gag Virus Like Particles

HIV gag assembles at the plasma membrane and results of budding of HIV particles. We have monitored assembly of fluorescently labeled Gag molecules at the bottom surface of cells using TIRF microscopy with variable penetration depths. In this work we present quantitative analysis of intensity of vesicle like particles (VLPs) which form with fluorescently-tagged Gag on the cell membrane. To verify that these vesicles actually represent fully coated VLPs that separate from the host cell we have monitored the recruitment of VPS4-mCherry which is a critical member of the Endosomal Sorting Complexes Required for Transport (ESCRT) and its recruitment indicates fission from the host cell. Since the polymerization of Gag is a constant process, we expect the fluorescence intensities to increase continuously over time until it reaches a maximum, which signifies that the VLP is complete. However, we have found that the intensities of these VLPs increase in discrete steps. In order to further investigate the frequency and distribution of these pauses we have developed a mathematical model of Gag polymerization during virus assembly. This model is based on the spherical geometry of a VLP and how the total number of Gag proteins changes as the sphere forms. We have used least squares regression to determine the best fit of the model to the data, which we have then used to consider how these pauses have affected the total assembly time of the VLPs.

Integrated component scheduling models for chip shooter machines

This paper focuses on minimizing printed circuit board (PCB) assembly time for a chip shooter machine, which has a movable feeder carrier holding components, a movable X–Y table carrying a PCB, and a rotary turret with multiple assembly heads. The assembly time of the machine depends on two inter-related optimization problems: the component sequencing problem and the feeder arrangement problem. Nevertheless, they were often regarded as two individual problems and solved separately. This paper proposes two complete mathematical models for the integrated problem of the machine. The models are verified by two commercial packages. Finally, a hybrid genetic algorithm previously developed by the authors is presented to solve the model. 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 assembly time.

Assembly time minimization for an electronic component placement machine

In this study, a new algorithm for a particular component placement machine is proposed. Also, a pairwise exchange procedure is designed and applied after the proposed algorithm. In the analyzed machine, previously proposed algorithms were mounting the components from lightest to heaviest where we propose to mount the components from heaviest to lightest. This method brings new opportunities in terms of performance gain on typical Printed Circuit Boards produced in the industry. The new algorithm is compared with former approaches on synthetically generated instances. It outperforms the former approaches by 1.54 percent on printed circuit boards with 100 components to be placed. It gives better results in 96 out of 100 instances. Applying the designed pairwise exchange procedure after the proposed algorithm further improves the total assembly time. Moreover, by taking advantage of inherent design of the analyzed PCBs, other promising improvement procedures are also suggested for minimizing total assembly time. By applying all the suggested techniques to the problem instances, performance improvement rate reaches up to 4.5 percent when compared with previous studies. Since the architecture and working principles of widely used new technology placement machines are very similar to the one analyzed here, the improvement techniques developed here can easily be generalized to them.

A heuristic approach for component scheduling on a high-speed PCB assembly machine

The collect-and-place machine is one of the most widely used placement machines for assembling electronic components on the printed circuit boards (PCBs). Nevertheless, the number of researches concerning the optimisation of the machine performance is very few. This motivates us to study the component scheduling problem for this type of machine with the objective of minimising the total assembly time. The component scheduling problem is an integration of the component sequencing problem, that is, the sequencing of component placements; and the feeder arrangement problem, that is, the assignment of component types to feeders. To solve the component scheduling problem efficiently, a hybrid genetic algorithm is developed in this paper. A numerical example is used to compare the performance of the algorithm with different component grouping approaches and different population sizes.

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.

Enhancing optimal feeder assignment of the multi-head surface mounting machine using genetic algorithms

The problem of minimizing the time required to populate a printed circuit board using a multi-head surface mounting machine is considered in this paper. The multi-head surface mounting machine is becoming increasingly popular due to its merit of picking or placing multiple components simultaneously in one pick-and-place operation, which reduces much portion of the assembly time. The complexity of the optimization problem of minimizing the assembly time results in that acquiring its desired solution is difficult. The total assembly time depends on two optimization problems: feeder assignment problem and pick-and-place sequencing problem. Although these two problems are interrelated, they are solved, respectively. Feeder assignment problem is one crucial problem of affecting surface mounting machine's productivity directly. Optimal feeder assignment can decrease sum of time of moving along slots, moving from slot to PCB and moving from PCB to slot for placement heads griping components after predetermining pick-and-place sequence. For it is of a combinatorial nature and NP-hard, there is no exact algorithm for it. As an efficient and useful procedure for solving the combinatorial optimization problems, the genetic algorithm with specific crossover and mutation operators is proposed in this paper. The running results show that the proposed method performs better than the conventional methods.

Component scheduling for chip shooter machines: a hybrid genetic algorithm approach

A chip shooter machine for electronic component assembly has a movable feeder carrier, a movable X–Y table carrying a printed circuit board (PCB), and a rotary turret with multiple assembly heads. This paper presents a hybrid genetic algorithm (HGA) to optimize the sequence of component placements and the arrangement of component types to feeders simultaneously for a chip shooter machine, that is, the component scheduling problem. The objective of the problem is to minimize the total assembly time. The GA developed in the paper hybridizes different search heuristics including the nearest-neighbor heuristic, the 2-opt heuristic, and an iterated swap procedure, which is a new improved heuristic. Compared with the results obtained by other researchers, the performance of the HGA is superior in terms of the assembly time. Scope and purpose When assembling the surface mount components on a PCB, it is necessary to obtain the optimal sequence of component placements and the best arrangement of component types to feeders simultaneously in order to minimize the total assembly time. Since it is very difficult to obtain the optimality, a GA hybridized with several search heuristics is developed. The type of machines being studied is the chip shooter machine. This paper compares the algorithm with a simple GA. It shows that the performance of the algorithm is superior to that of the simple GA in terms of the total assembly time.