2,949 research outputs found

    A Cost-Based Allocation of Inspection Efforts in Quality Control of a Multistage Assembly Line: A Case Study of an Electronics Assembly Line

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    Firms within the electronics manufacturing industry is often a high-volume high-mix product manufacturing industry. This study presents a cost-based allocation of inspection efforts in quality control of a sequential multi-stage electronic assembly line, considering all relevant costs and proposes the optimum inspection strategy. A dynamic sampling plan is incorporated in the model to maintain the desired quality levels. Monte-Carlo simulation is used to obtain the solution of this complex model. The model is created based on an actual electronics packaging company. This approach provides the ability to minimize the costs and does not sacrifice the quality of the products. The input factors that significantly affect the costs are identified so that they can be optimized for performance improvement and decision-making

    Optimum Allocation of Inspection Stations in Multistage Manufacturing Processes by Using Max-Min Ant System

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    In multistage manufacturing processes it is common to locate inspection stations after some or all of the processing workstations. The purpose of the inspection is to reduce the total manufacturing cost, resulted from unidentified defective items being processed unnecessarily through subsequent manufacturing operations. This total cost is the sum of the costs of production, inspection and failures (during production and after shipment). Introducing inspection stations into a serial multistage manufacturing process, although constituting an additional cost, is expected to be a profitable course of action. Specifically, at some positions the associated inspection costs will be recovered from the benefits realised through the detection of defective items, before wasting additional cost by continuing to process them. In this research, a novel general cost modelling for allocating a limited number of inspection stations in serial multistage manufacturing processes is formulated. In allocation of inspection station (AOIS) problem, as the number of workstations increases, the number of inspection station allocation possibilities increases exponentially. To identify the appropriate approach for the AOIS problem, different optimisation methods are investigated. The MAX-MIN Ant System (MMAS) algorithm is proposed as a novel approach to explore AOIS in serial multistage manufacturing processes. MMAS is an ant colony optimisation algorithm that was designed originally to begin an explorative search phase and, subsequently, to make a slow transition to the intensive exploitation of the best solutions found during the search, by allowing only one ant to update the pheromone trails. Two novel heuristics information for the MMAS algorithm are created. The heuristic information for the MMAS algorithm is exploited as a novel means to guide ants to build reasonably good solutions from the very beginning of the search. To improve the performance of the MMAS algorithm, six local search methods which are well-known and suitable for the AOIS problem are used. Selecting relevant parameter values for the MMAS algorithm can have a great impact on the algorithm’s performance. As a result, a method for tuning the most influential parameter values for the MMAS algorithm is developed. The contribution of this research is, for the first time, a methodology using MMAS to solve the AOIS problem in serial multistage manufacturing processes has been developed. The methodology takes into account the constraints on inspection resources, in terms of a limited number of inspection stations. As a result, the total manufacturing cost of a product can be reduced, while maintaining the quality of the product. Four numerical experiments are conducted to assess the MMAS algorithm for the AOIS problem. The performance of the MMAS algorithm is compared with a number of other methods this includes the complete enumeration method (CEM), rule of thumb, a pure random search algorithm, particle swarm optimisation, simulated annealing and genetic algorithm. The experimental results show that the effectiveness of the MMAS algorithm lies in its considerably shorter execution time and robustness. Further, in certain conditions results obtained by the MMAS algorithm are identical to the CEM. In addition, the results show that applying local search to the MMAS algorithm has significantly improved the performance of the algorithm. Also the results demonstrate that it is essential to use heuristic information with the MMAS algorithm for the AOIS problem, in order to obtain a high quality solution. It was found that the main parameters of MMAS include the pheromone trail intensity, heuristic information and evaporation of pheromone are less sensitive within the specified range as the number of workstations is significantly increased

    Development of an optimization model to determine sampling levels

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    As the complexity of multi-component products increases the quality of these products becomes increasingly difficult to control. The first step to manufacturing a quality product is making sure that the components of the product meet specifications. Product quality can be controlled through sampling inspection of the components. Two models were developed in this research to determine the optimal sampling levels for incoming lots containing parts for production and assembly of multi-component systems. The main objective of the first model is to minimize the expected cost that is associated with a nonconforming item reaching assembly. In this model the time available for inspection is limited. The main objective in the second model is to minimize total cost, which includes the appraisal cost (inspection cost) and the cost associated with nonconformance reaching assembly. In this model the time available is not a constraint. The distribution of defects is assumed to follow the binomial distribution, and the distribution of accepting the lot with defects follows the hypergeometric distribution. In addition, the inspection is considered to be accurate and, if a nonconforming item is found in the inspected sample, the entire lot is rejected. An example is given with real world data and the results are discussed --Abstract, page iv

    A dynamic programming model for designing a quality control plan in a manufacturing process

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    This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Process quality planning should establish the quality control plan to achieve the desired quality level with the minimum quality cost (appraisal and failure costs) for the final product. This plan sets out the critical quality variables, the control stations in the process, and the control method at each control station. The quality costs associated with quality control and defective products can be greater than or less than ideal regarding the required quality level. The purpose of this paper is to provide a stochastic dynamic programming model for designing the quality control plan in a manufacturing process, which allows obtaining the desired level of control with the lowest cost. Inputs to the model are, in particular, control stations in the process, levels of quality, control methodologies (no control, statistical process control, 100% inspection), probabilities of changing the quality level and quality costs. The output of this model is the quality control plan that satisfies the desired level of quality at the lowest cost. This plan establishes the control stations, the methodology used in each control station, the desired quality level for the final product, and the estimated quality costs. Finally, an illustrative example based on a manufacturing process demonstrates the applicability of this approach and several considerations are reported about future research directions.FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2019

    Manufacturing variation models in multi-station machining systems

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    In product design and quality improvement fields, the development of reliable 3D machining variation models for multi-station machining processes is a key issue to estimate the resulting geometrical and dimensional quality of manufactured parts, generate robust process plans, eliminate downstream manufacturing problems, and reduce ramp-up times. In the literature, two main 3D machining variation models have been studied: the stream of variation model, oriented to product quality improvement (fault diagnosis, process planning evaluation and selection, etc.), and the model of the manufactured part, oriented to product and manufacturing design activities (manufacturing and product tolerance analysis and synthesis). This paper reviews the fundamentals of each model and describes step by step how to derive them using a simple case study. The paper analyzes both models and compares their main characteristics and applications. A discussion about the drawbacks and limitations of each model and some potential research lines in this field are also presented

    A particle swarm algorithm for inspection optimization in serial multi-stage processes

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    AbstractImplementing efficient inspection policies is much important for the organizations to reduce quality related costs. In this paper, a particle swarm optimization (PSO) algorithm is proposed to determine the optimal inspection policy in serial multi-stage processes. The policy consists of three decision parameters to be optimized; i.e. the stages in which inspection occurs, tolerance of inspection, and size of sample to inspect. Total inspection cost is adopted as the performance measure of the algorithm. A numerical example is investigated in two phases, i.e. fixed sample size and sample size as a decision parameter, to ensure the practicality and validity of the proposed PSO algorithm. It is shown that PSO gives better results in comparison with two other algorithms proposed by earlier works

    Production line: effect of different inspection station allocation under accepts reject inspection policy

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    Manufacturing system is one of the most important parts in any organization as it produces the output of the company which will generate the profit. It consists partly of the production line which plays the role as the centre of production to create the end product which could be half finished or the full product. It is a big problem for the company to determine which is the better arrangement and combination of the tools or machines available in this area of the organization as different combination will greatly impact the productivity of the production line together with the profit of the company. This research intend to analyze a new production line in a metal stamping company based on the complain from the company and try to explore the better layout or arrangement in the production line in reflect to the complained problem and constrain of the provided of accept the defect and repair inspection policy. The production line is first being analyzed in response to complain through computer simulation. After the problem had been identified, the researcher tried different alternatives in the attempt to seek for the better layout or arrangement in the production line. The effect of different inspection station allocation layout is then being evaluated in term of the production time. The research has resulted in the finding of the cause for the long production time in the factory which is the long inspection steps which consumed much of the production time. After a few alternatives have been explored in allocating the inspection station, it is obvious that the current approach of the production line is the better one. Even by reducing the number of inspection station, interesting enough, the production time does not seem to decrease but yet increased. This finding contradicts the normal thought of fewer stations means shorter time. This finding could be the founding basic in the future research regarding the allocation of the inspection station following certain provided policy. This is also very helpful in real life practice in company as to help them improve their production time. As for the time being, there is yet a research addressing this issue pertaining the given inspection policy

    Optimal Configuration of Inspection and Rework Stations in a Multistage Flexible Flowline

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    Inspection and rework are two important issues of quality control. In this research, an N-stage flowline is considered to make decisions on these two issues. When defective items are detected at the inspection station the items are either scrapped or reworked. A reworkable item may be repaired at the regular defect-creating workstation or at a dedicated off-line rework station. Two problems (end-of-line and multistage inspections) are considered here to deal with this situation. The end-of-line inspection (ELI) problem considers an inspection station located at the end of the line while the multistage inspection (MSI) problem deals with multiple in-line inspection stations that partition the flowline into multiple flexible lines. Models for unit cost of production are developed for both problems. The ELI problem is formulated for determining the best decision among alternative policies for dealing with defective items. For an MSI problem a unit cost function is developed for determining the number and locations of in-line inspection stations along with the alternative decisions on each type of defects. Both of the problems are formulated as fractional mixed-integer nonlinear programming (f-MINLP) to minimize the unit cost of production. After several transformations the f-MINLP becomes a mixed-integer linear programming (MILP) problem. A construction heuristic, coined as Inspection Station Assignment (ISA) heuristic is developed to determine a sub-optimal location of inspection and rework stations in order to achieve minimum unit cost of production. A hybrid of Ant-Colony Optimization-based metaheuristic (ACOR) and ISA is devised to efficiently solve large instances of MSI problems. Numerical examples are presented to show the solution procedure of ELI problems with branch and bound (B&B) method. Empirical studies on a production line with large number of workstations are presented to show the quality and efficiency of the solution processes involved in both ELI and MSI problems. Computational results present that the hybrid heuristic ISA+ACOR shows better performance in terms of solution quality and efficiency. These approaches are applicable to many discrete product manufacturing systems including garments industry

    A review on equipment protection and system protection relay in power system

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    Power system equipment is configured and connected together with multiple voltage levels in existing electrical power system. There are varieties of electrical equipment obtainable in the power system predominantly from generation side up to the distribution side. Consequently, appropriate protections must be apt to prevent inessential disturbances that lead to voltage instability, voltage collapse and sooner a total blackout took place in the power system. The understanding of each component on the system protection is critical. This is due to any abnormal condition and failure can be analyzed and solved effectively due to the rapid changing and development on the power system network. Therefore, the enhancement of power quality can be achieved by sheltering the equipment with protection relay in power system. Moreover, the design of a systematic network is crucial for the system protection itself. Several types of protective equipment and protection techniques are taken into consideration in this paper. Hence, the existing accessible types and methods of system protection in the power system network are reviewed
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