Allocating solder-paste printing inspection in high-volume electronics manufacturing

The paper describes an optimization model for allocating solder-paste printing inspection that explicitly considers the economic tradeoff between board yield and inspection accuracy. The paper also shows that the use of a heuristic solution method for solving the post-printing inspection allocation model is effective and efficient for high-volume electronics manufacturing. The model has been developed using real production and visual inspection data provided by a high-volume electronics manufacturer located in Huntsville, AL. In addition, randomly generated problems are used to evaluate the performance of the proposed heuristic. Results from a large case study show that when compared with a full post-printing inspection, the heuristic approach provides a solution that can increase the total expected gains by 15%.     

Optimization of process plans using a constraint-based tabu search approach

A computer-aided process planning system should ideally generate and optimize process plans to ensure the application of good manufacturing practices and maintain the consistency of the desired functional specifications of a part during its production processes. Crucial processes, such as selecting machining resources, determining set-up plans and sequencing operations of a part should be considered simultaneously to achieve global optimal solutions. In this paper, these processes are integrated and modelled as a constraint-based optimization problem, and a tabu search-based approach is proposed to solve it effectively. In the optimization model, costs of the utilized machines and cutting tools, machine changes, tool changes, set-ups and departure from good manufacturing practices (penalty function) are the optimization evaluation criteria. Precedence constraints from the geometric and manufacturing interactions between features and their related operations in a part are defined and classified according to their effects on the plan feasibility and processing quality. A hybrid constraint-handling method is developed and embedded in the optimization algorithm to conduct the search efficiently in a large-size constraint-based space. Case studies, which are used for comparing this approach with the genetic algorithm and simulated annealing approaches, and the proposed constraint-handling method and other constraint methods, are discussed to highlight the performance of this approach in terms of the solution quality and computational efficiency of the algorithm.     

钢格板计算机辅助设计与优化排料系统研究与开发

钢格栅板广泛应用于建筑、化工、水利等领域。钢格板设计与生产流程普遍存在的诸多问题影响了企业的生产效率。系统通过集成自动布板、工作量统计与优化排料等功能对钢格板设计、生产流程进行全程优化,显著提高了钢格板的设计生产效率。结合某应用实例验证本系统的实用性和可靠性。     

MR-PRIM: Patient Rule Induction Method for Multiresponse Optimization

Most of the works in multiresponse surface methodology have been focusing mainly on the optimization issue, assuming that the data have been collected and suitable models have been built. Though crucial for optimization, a good empirical model is not easy to obtain from the manufacturing process data. This article proposes a new approach to solving the multiresponse problem directly without building a model—an approach called patient rule induction method for multiresponse optimization (MR-PRIM). MR-PRIM is an extension of PRIM to multiresponse problems. Three major characteristic features of MR-PRIM are discussed as the new approach is applied to the case of a steel manufacturing process.     

A neural network model-based open-loop optimization for the automated thermoplastic composite tow-placement system

This study demonstrates the use of on-line optimization algorithms to calculate optimum process set points for manufacturing processes such as the automated thermoplastic tow-placement (ATP) system. An in situ non-linear optimization technique based on artificial neural networks has been developed. This method is implemented in the ATP process and utilizes neural network-based process models to predict material quality as a function of process set points. The set points are computed by maximizing the throughput and maintaining a desired minimum quality. Process history can greatly affect the final part quality and, therefore, is an integral part of the optimization. The controller is validated for the highly non-linear ATP process and successfully predicts optimum processing parameters. The developed approach is applicable to many other manufacturing processes where process simulations exist and conventional control techniques are lacking.     

Fundamental Theories and Key Technologies for Smart and Optimal Manufacturing in the Process Industry

Given the significant requirements for transforming and promoting the process industry, we present the major limitations of current petrochemical enterprises, including limitations in decision-making, production operation, efficiency and security, information integration, and so forth. To promote a vision of the process industry with efficient, green, and smart production, modern information technology should be utilized throughout the entire optimization process for production, management, and marketing. To focus on smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of the manufacturing process, operating mode, and supply chain management, we put forward several key scientific problems in engineering in a demand-driven and application-oriented manner, namely: ① intelligent sensing and integration of all process information, including production and management information; ② collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; ③ cooperative control and optimization of plant-wide production processes via human-cyber-physical interaction; and ④ life-cycle assessments for safety and environmental footprint monitoring, in addition to tracing analysis and risk control. In order to solve these limitations and core scientific problems, we further present fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Although this paper discusses the process industry in China, the conclusions in this paper can be extended to the process industry around the world.     

Optimization of multiple responses using a fuzzy-rule based inference system

The optimization of multiple responses (or performance characteristics) has received increasing attention over the last few years in many manufacturing organizations. Many Taguchi practitioners have employed past experience and engineering knowledge or judgement when dealing with multiple responses. This approach brings an element of uncertainty to the decision-making process and therefore is not recommended for optimization of multiple responses. The approach presented in this paper takes advantage of both the Taguchi method and a fuzzy-rule based inference system, which forms a robust and practical methodology in tackling multiple response optimization problems. The paper also presents a case study to illustrate the potential of this powerful integrated approach for tackling multiple response optimization problems. The variance analysis is also an integral part of the study, which identifies the most critical and statistically significant parameters.     

Selecting the nozzle assortment for a Gantry-type placement machine

Electronics manufacturing systems employ increasingly multi-head gantry machines, where several vacuum nozzles are used simultaneously in pick-and-place operations to insert components on bare PCBs. Their use includes several options that have an impact on the overall manufacturing speed of the machine. In the present paper we address the problem of selecting the nozzles for this kind of a gantry machine, which is an important subproblem of the larger scheduling problem of multi-head gantry machines. Nozzles come in different types, and different types of components may require different types of nozzles in their placing. We address first a case where a single PCB type is manufactured and the only limitation on the number of nozzles is given by the capacity of the placement head. Then we discuss the case where there is a budget limitation on the total cost of the nozzles we can buy. We show that both of these problems can be solved optimally by the means of efficient greedy algorithms. We also discuss the case of selecting nozzles when manufacturing multiple different PCB types.     

A fault diagnosis prototype for a bioreactor for bioinsecticide production

The objective of this work is to develop an algorithm for fault diagnosis in a process of animal cell cultivation, for bioinsecticide production. Generally, these processes are batch processes. It is a fact that the diagnosis for a batch process involves a division of the process evolution (time horizon) into partial processes, which are defined as pseudocontinuous blocks. Therefore, a PCB represents the evolution of the system in a time interval where it has a qualitative behavior similar to a continuous one. Thus, each PCB, in which the process is divided, can be handled in a conventional way (like continuous processes). The process model, for each PCB, is a Signed Directed Graph (SDG). To achieve generality and to allow the computational implementation, the modular approach was used in the synthesis of the bioreactor digraph. After that, the SDGs were used to carry out qualitative simulations of faults. The achieved results are the fault patterns. A special fault symptom dictionary —SM—has been adopted as data base organization for fault patterns storage. An effective algorithm is presented for the searching process of fault patterns. The system studied, as a particular application, is a bioreactor for cell cultivation for bioinsecticide production. During this work, we concentrate on the SDG construction, and 3btaining real fault patterns by the elimination of spurious patterns. The algorithm has proved to be effective in both senses, resolution and accuracy, to diagnose different kinds of simulated faults.     

Hierarchical environmental impact evaluation of a process in printed circuit board manufacturing

Considering the growing concerns and importance of environmental issues, manufacturing industrialists, in particular those in electronics manufacturing, are seeking methods to evaluate the environmental performance of their manufacturing processes. These environmental evaluation tools should be capable of performing detailed analysis on the environmental performance of each individual process unit, identifying the environmental improvement opportunity and providing adequate decision support to environmental engineers for process modification and operational change. This paper modifies and improves the environmental impact evaluation model already developed and provides directions for decision-making at various stages of the analysis. The decision algorithm in this revised model adopts a hierarchical environmental impact evaluation approach and uses five impact categories related to ecological health and seven categories related to human health to form the base of an evaluation hierarchy. The algorithm, with its evaluation results presented in all levels of the hierarchy, is proposed as a means of tracking, controlling and improving the environmental performance of a process. A printed circuit board case study shows the effectiveness and applicability of the algorithm. The results indicate that the electroless copper process has a higher impact on ecological health than on human health, and that the phosphoric acid in the waste components is the identified major source of the impact on ecological health.     

The impact of environmental issues on the UK printed circuit boardindustry

The need for environmentally responsible manufacturing is forcing industry to change the way it produces its products and offers its services. This is evident in the electronics industry and especially so in the printed circuit board (PCB) manufacturing sector. PCB manufacture requires numerous chemical processes and materials, many of which contain chemicals that can be difficult to dispose of and harmful to the environment. With growing awareness of environmental issues and stricter legislation, the treatment and disposal of these materials can have a significant cost impact on the profitability of UK producers. This article discusses some of the environmental challenges facing the industry and highlights the fact that improvements in environmental performance can actually be used to commercial advantage     

An optimal cart moving policy for a flexible manufacturing system

A flexible manufacturing system is composed of many stations such as a load/unload station, a set of workstations, and a common buffer, that are linked together with a material handling system. Each workstation consists of a limited input buffer, a single machine and a limited output buffer. The material handling system consists of a single cart moving parts in the system according to the process paths required by the parts. A part is blocked when it is moved to a workstation but cannot enter the workstation. The function of the common buffer is to temporarily store blocked parts. A blocked part is treated in accordance with a flexible manufacturing system blocking mechanism. We model the flexible manufacturing system by a closed queueing network with the flexible manufacturing system blocking mechanism and a block-dependent static Markov part routing. An optimal cart moving policy that maximizes the expected system throughput is formulated as an undiscounted semi-Markov decision process. Several properties of the optimization problem are characterized. A loop approach is developed for finding an optimal policy. An example is given to illustrate the methodology, and investigate its convergence.     

用于KrF准分子激光光刻的三相移掩模

讨论了相移掩模提高光刻分辨力的基本原理,提出了一种抗蚀剂相移器制作衰减相移掩模的新方法,利用自行设计、建立的ＫｒＦ准分子激光投影光刻实验曝光系统进行了实验研究,给出了实验结果,并与传统光刻方法作了比较。     

Intelligent Ironmaking Optimization Service on a Cloud Computing Platform by Digital Twin

The shortage of computation methods and storage devices has largely limited the development of multi-objective optimization in industrial processes. To improve the operational levels of the process industries, we propose a multi-objective optimization framework based on cloud services and a cloud distribution system. Real-time data from manufacturing procedures are first temporarily stored in a local database, and then transferred to the relational database in the cloud. Next, a distribution system with elastic compute power is set up for the optimization framework. Finally, a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking process. With the application of this optimization service in a cloud factory, iron production was found to increase by 83.91 t∙d⁻¹, the coke ratio decreased 13.50 kg∙t⁻¹, and the silicon content decreased by an average of 0.047%.     

A genetic algorithm-based solution to optimal tolerance synthesis of mechanical assemblies with alternative manufacturing processes: focus on complex tolerancing problems

The product development process involves tolerance specification on the individual component dimensions. The impact of tolerance specification on manufacturing cost has drawn the attention of product designers towards economic tolerance synthesis using various optimization techniques. Simultaneous selection of manufacturing processes or machines from amongst the alternatives for producing a toleranced feature have also been considered. The solution surface for such a problem becomes a combinatorial and multi-modal function involving several local minima. Application of a genetic algorithm to the solution of this advanced tolerancing problem, together with benchmarking with the exact global solution obtained using Lagrange's multiplier-based exhaustive search method, has been reported in an earlier paper by the authors. The proposed algorithm is quite simple and straightforward, and automatically takes care of the process selection constraints. Application of the genetic algorithm has been demonstrated on complex tolerancing problems such as those involving interrelated dimension chains, complex stack-up conditions and complex cost functions, etc., where the use of traditional optimization techniques is not recommended.     

Optimum tooling design for resin transfer molding with virtual manufacturing and artificial intelligence

Resin transfer molding (RTM) is a promising fabrication method for low to medium volume, high-performance polymer composite structures. Yet there exist several technical issues which impede a wide application base. One of these issues is tooling design. In the RTM process, the arrangement of injection gates and vents of the mold has a significant impact on product quality and process efficiency. In this paper, a systematic approach for optimum design of RTM tooling is introduced. This approach is built upon an RTM virtual manufacturing (simulation) model coupled with a neural network–genetic algorithm optimization procedure. The simulation model is employed to predict resin flow patterns (i.e. potential quality problems) and processing efficiency (mold filling time). With the simulation results, a neural network is trained to create a rapid RTM process model. Genetic algorithms are applied to this rapid RTM process model to search for the optimum solution to RTM process design. This tooling design scheme enables the engineer to determine the optimum locations of injection gates and vents for the best processing performance, i.e. short filling time and high quality level (minimum defects). The approach is illustrated with an example.     

Impact of process flow conditions on particle morphology in metal powder production via gas atomization

Additive manufacturing processes as for instance selective laser melting or electron beam melting are becoming more common and just turning into standard manufacturing processes for metal components. Nevertheless, these processes are still new compared to classic powder metallurgy manufacturing routes such as pressing and sintering. Hence not all necessary requirements for the powders in use are fully known yet. This makes an increase in control of the powder properties a crucial task to achieve. To reach this goal one must understand the different influences on the powder production process from the beginning of the whole production route. In this work, the influence of the spray chamber flow on the particle morphology is examined. The nozzle system used to produce the metal powders is a close-coupled atomization system with a convergent-divergent gas nozzle configuration. The particle morphology as well as the particle size distribution have been analyzed to examine the influence of the atomization gas flow compared to an additional use of a coaxial gas flow. To review the changes of the flow patterns, computational fluid dynamic simulations have been performed. The particle trajectories were calculated to assess the change in particle behavior as well. Atomization experiments have been conducted with an AISI 52100 (1.3505) steel in a small batch atomization plant to evaluate the influence of the change in flow on the particle size distribution and circularity. The experimental results show that a use of additional coaxial gas leads to an increase in particle circularity up to 10% for relevant particle sizes. An approach for the quantification of satellite occurrence is given by examination of the shift of the particle size distribution to smaller diameters.     

泡沫金属的制造方法

概要叙述了泡沫金属的特性，制造方法及用途，并以泡沫铝为例，着重介绍了发泡法的原理及过程，指出了泡沫金属制造技术上应该解决的问题。     

A modified version of a T‐Cell Algorithm for constrained optimization problems

In this paper, we present a heuristic inspired on the T‐Cell model of the immune system (i.e. an artificial immune system). The proposed approach (called T‐Cell) is used for solving constrained (numerical) optimization problems, and is validated using several test functions taken from the specialized literature on evolutionary optimization. Additionally, several engineering optimization problems are also used for assessing the performance of the proposed approach. The results are compared with respect to approaches representative of the state‐of‐the‐art in constrained evolutionary optimization. Copyright © 2010 John Wiley & Sons, Ltd.     

Impact of plasma etching on fabrication technology of liquid crystal polymer printed circuit board

Liquid crystal polymer (LCP) has attracted great attention as a potential candidate for high performance micro-wave substrate material in high frequency printed circuit boards (PCB). This is attributed to its low loss behaviour, excellent thermal stability, and outstanding chemical resistance. The use of LCP in PCB, however, is hindered by its extreme chemical inertness, causing fabrication challenges in desmearing and metallization processes. To overcome the challenges, plasma etching is suggested for its capability of smear removal, adhesion improvement and surface activation of PCB material. However, previous experimental and theoretical studies of the effects of plasma etching on LCP PCB at different treatment conditions are lacking. This paper thus evaluates recent developments on plasma etching technologies, involving the plasma etching investigation under different process conditions for manufacturing LCP PCB. Distinct process approaches are developed and proposed based on the illustrations of the experimental outcomes. Finally, examples are shown to demonstrate the effectiveness of the proposed plasma etching approach for controlling the fabrication of LCP PCB.