二维条码:电子制造行业的制胜之道

项目背景 某电子制造厂是一家美资企业,坐落于江苏省南京市,主要为汽车、医疗、工控、和公共安全领域提供耐用电子产品,产品定位高端,所以对于产品的质量控制要求非常苛刻.在国内的新厂开启后,为了确保产品的品质始终优良,因此安装了一整套自动检测控制系统并同时部署条码阅读器.     

An empirical analysis of effective TQM implementation in the Hong Kong electronics manufacturing industry

The objective of this research is to develop a conceptual model of total quality management (TQM) implementation and test it empirically. The main research problem of this study is to investigate the effect of adopting TQM principles on quality achievement in manufacturing. The hypotheses specifying relationships between the core parts of the model including quality management infrastructure, core quality management practices, quality performance, and customer satisfaction are tested using survey research method. Data were collected from 50 managers of 25 manufacturing companies in Hong Kong. Correlation analysis on the ratings given by two different respondents from the same company shows that employee relations strongly facilitates the use of quality data and reporting, while training supports the function of product design, supplier quality management, and quality data and reporting. The results also indicate that employee relations and training have direct effects on quality performance which, in turn, relates to customer satisfaction. Practitioners are advised to build a supportive environment with a strong emphasis on human and organizational aspects to promote effective TQM implementation. © 1999 John Wiley & Sons, Inc.     

Prediction of effectiveness of concurrent engineering in electronics manufacturing in the U.S.

Based on a survey of 103 electronic component manufacturers in the U.S. using concurrent engineering, a theoretically based interdisciplinary quantitative predictive model was developed. In each company, data were collected from the manager of the concurrent engineering effort, the team leader, and the design, manufacturing, and marketing team members. The results from this 403-item response in each company indicate that the measures are reliable. The proposed model, which continues building on one originally proposed by Majchrzak, is presented and the organizational and human variables are shown to account for 50% of the variance. Proximity of team members is confirmed as a key to success. Future research directions are suggested in light of the tendency toward a more global economy, global manufacturing, and geographically distributed teams. The results of the present study suggest that the keys to success in the future will again go beyond the technical and into the human, organizational, and social aspects. © 1997 John Wiley & Sons, Inc.     

Integrating the grouping and layout problems in cellular manufacturing systems

In this paper, the simultaneous solution of the machine grouping and layout problems in cellular manufacturing systems is explored. A model for the combined problem is presented. Since the model is complex to solve using traditional optimization techniques, a suboptimal procedure involving the use of a simulated annealing based algorithm is suggested. Results with a numerical example are presented. An alternative formulation based on quandratic assignment problem is also presented.     

Flexible manufacturing systems—Manpower problems and policies

Flexible manufacturing systems can be seen as the latest stage in the automation of small batch production. The prevealing manpower approach to these systems is characterized by a hierarchical and highly specialized job structure. An alternative is to have a homogenous job structure on an advanced skill level. The latter approach increases problems and costs of implementation processes; however, it may ensure higher utilization times, more flexibility, and better quality in the long run. Trends in small batch and process manufacturing, the labor market, and union policies provide opportunities for the alternative approach.     

A simulation study of some tooling problems in automated manufacturing

A simulation model has been developed to analyze the effects of tool configuration, tool replenishing, and tool sharing. A feature of the model is its ability to keep track of tool usage (age). The application of the model is demonstrated using an example problem. Some findings are discussed.     

Multi-period part selection and loading problems in flexible manufacturing systems

We consider multi-period part selection and loading problems in flexible manufacturing systems with the objective of minimizing subcontracting costs. The part selection problem is to select sets of part types and to determine their quantities to be produced during the upcoming planning horizon while satisfying due dates of all orders for the parts, and the loading problem involves allocation of operations and required tools to machines. Production demands should be satisfied for periods through subcontracting if production demands cannot be satisfied by the system due to machine capacity or tool magazine capacity constraints. For the part selection and loading problems, we develop three iterative algorithms, called the forward algorithm, the backward algorithm and the capacity approximation algorithm, that solve the part selection and loading problems iteratively for each period. To compare the three algorithms, a series of computational experiments is done on randomly generated test problems.     

Two-machine group scheduling problems in discrete parts manufacturing with sequence-dependent setups

This paper focuses on minimizing the total completion time in two-machine group scheduling problems with sequence-dependent setups that are typically found in discrete parts manufacturing. As the problem is characterized as strongly NP-hard, three search algorithms based on tabu search are developed for solving industry-size scheduling problems. Four different lower bounding mechanisms are developed to identify a lower bound for all problems attempted, and the largest of the four is aptly used in the evaluation of the percentage deviation of the search algorithms to assess their efficacy. The problem sizes are classified as small, medium and large, and to accommodate the variability that might exist in the sequence-dependent setup times on both machines, three different scenarios are considered. Such finer levels of classification have resulted in the generation of nine different categories of problem instances, thus facilitating the performance of a very detailed statistical experimental design to assess the efficacy and efficiency of the three search algorithms. The search algorithm based on long-term memory with maximal frequencies either recorded a statistically better makespan or one that is indifferent when compared with the other two with all three scenarios and problem sizes. Hence, it is recommended for solving the research problem. Under the three scenarios, the average percentage deviation for all sizes of problem instances solved has been remarkably low. In particular, a mathematical programming based lower bounding mechanism, which focuses on converting (reducing) the original sequence-dependent group scheduling problem with several jobs in each group to a sequence-dependent job scheduling problem, has served well in identifying a high quality lower bound for the original problem, making it possible to evaluate a lower average percentage deviation for the search algorithm. Also, a 16–17-fold reduction in average computation time for solving a large problem instance with the recommended search algorithm compared with identifying just the lower bound of (not solving) the same instance by the mathematical programming based mechanism speaks strongly in favor of the search algorithm for solving industry-size group scheduling problems.     

Evolutionary techniques for optimization problems in integrated manufacturing system: State-of-the-art-survey

Integrated manufacturing system (IMS) is a novel manufacturing environment which has been developed for the next generation of manufacturing and processing technologies. It consists of engineering design, process planning, manufacturing, quality management, and storage and retrieval functions. Improving the decision quality in those fields give rise to complex combinatorial optimization problems, unfortunately, most of them fall into the class of NP-hard problems. Find a satisfactory solution in an acceptable time play important roles. Evolutionary techniques (ET) have turned out to be potent methods to solve such kind of optimization problems. How to adapt evolutionary technique to the IMS is very challenging but frustrating. Many efforts have been made in order to give an efficient implementation of ET to optimize the specific problems in IMS.In this paper, we address four crucial issues in IMS, including design, planning, manufacturing, and distribution. Furthermore, some hot topics in these issues are selected to demonstrate the efficiency of ET’s application, such as layout design (LD) problem, flexible job-shop scheduling problem (fJSP), multistage process planning (MPP) problem, and advanced planning and scheduling (APS) problem. First, we formulate a generalized mathematic models for all those problems; several evolutionary algorithms which adapt to the problems have been proposed; some test instances based on the practical problems demonstrate the effectiveness and efficiency of our proposed approach.     

CAD in electronics

CAD of electronic assemblies is discussed. The various language levels used to specify system architecture and to permit behavioural simulation is described, as is the development of circuit design. Future developments are suggested.     

Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In order to find an optimal solution to scheduling problems it gives rise to complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. In this paper, we focus on the design of multiobjective evolutionary algorithms (MOEAs) to solve a variety of scheduling problems. Firstly, we introduce fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and introduce evolutionary representations and hybrid evolutionary operations especially for the scheduling problems. Then we apply these EAs to the different types of scheduling problems, included job shop scheduling problem (JSP), flexible JSP, Automatic Guided Vehicle (AGV) dispatching in flexible manufacturing system (FMS), and integrated process planning and scheduling (IPPS). Through a variety of numerical experiments, we demonstrate the effectiveness of these Hybrid EAs (HEAs) in the widely applications of manufacturing scheduling problems. This paper also summarizes a classification of scheduling problems, and illustrates the design way of EAs for the different types of scheduling problems. It is useful to guide how to design an effective EA for the practical manufacturing scheduling problems. As known, these practical scheduling problems are very complex, and almost is a combination of different typical scheduling problems.     

Low-power electronics

Plans for a government sponsored research program call for the development of a new electronics technology base for hand-size information systems. This base will enable the design and implementation of semiconductor technologies that consume two orders of magnitude less power than conventional technology would have allowed. The strategy follows two coupled tracks, one developing advanced materials technologies and one developing advanced architectural technologies that are independent of implementation. We describe the preliminary program plans     

A genetic algorithm with proper parameters for manufacturing cell formation problems

One fundamental problem in cellular manufacturing is the formation of product families and machine cells. Many solution methods have been developed for the cell formation problem. Since efficient grouping is the prerequisite of a successful Cellular Manufacturing installation the research in this area will likely be continued. In this paper, we consider the problem of cell formation in cellular manufacturing systems with the objective of maximizing the grouping efficacy. We propose a Genetic Algorithm (GA) to obtain machine-cells and part-families. Developed GA has three different selection and crossover operators. The proper operators and parameters of the GA were determined by design of experiments. A set of 15 test problems with various sizes drawn from the literature is used to test the performance of the proposed algorithm. The corresponding results are compared to several well-known algorithms published. The comparative study shows that the proposed GA improves the grouping efficacy for 40% of the test problems.     

A laminate parametrization technique for discrete ply-angle problems with manufacturing constraints

In this paper we present a novel laminate parametrization technique for layered composite structures that can handle problems in which the ply angles are limited to a discrete set. In the proposed technique, the classical laminate stiffnesses are expressed as a linear combination of the discrete options and design-variable weights. An exact $\ell _{1}$ penalty function is employed to drive the solution toward discrete 0–1 designs. The proposed technique can be used as either an alternative or an enhancement to SIMP-type methods such as discrete material optimization (DMO). Unlike mixed-integer approaches, our laminate parametrization technique is well suited for gradient-based design optimization. The proposed laminate parametrization is demonstrated on the compliance design of laminated plates and the buckling design of a laminated stiffened panel. The results demonstrate that the approach is an effective alternative to DMO methods.     

Fuzzy multivariate mean square error in equispaced pareto frontiers considering manufacturing process optimization problems

This paper proposes a combined approach using the normal boundary intersection (NBI) and multivariate mean square error (MMSE) that is an alternative approach to outperform the traditional NBI driving to an equispaced Pareto Frontier in a low-dimension space with a considerable reduction in the number of iterations. The method participating in the evolutionary stage of creating a uniformly spread Pareto Frontier for a nonlinear multi-objective problem is the NBI using normalized objective functions allied to MMSE. In sequence, the fuzzy MMSE approach is utilized to determine the optimal point of the multi-objective optimization. For sake of comparison, the performance of arc homotopy length, global criterion method, and weighted sums were explored. To illustrate this proposal, a multivariate case of AISI H13 hardened steel-turning process is used. Experimental results indicate that the solution found by NBI-MMSE approach is a more appropriate Pareto frontier that surpassed all the competitors and also provides the best-compromised solution to set the machine input parameters. Further, this algorithm was also tested in benchmark functions to confirm the NBI-MMSE efficiency.