This paper describes the development of neural networks for the prediction of (1) printed circuit card surface temperature during a wave soldering process, and (2) the quality level of the circuit card assembly soldered connections. Using a combination of production data and design of experiment data, a set of hierarchically connected neural networks were developed and validated. These networks predict thermal behavior of a printed circuit card assembly at various points in the solder process based on process settings and circuit card design data. Then these predictions are used as inputs, together with other parameters, to estimate the quality of solder connections. The system can be used to decrease the number of solder connection defects, reduce set-up and preparation time between lots, and lead to consistent, repeatable process settings without trial production runs or operator tuning efforts. For the wave soldering process studied, this is especially important since the batch size is quite small, quality demands are stringent, and process settings are changed frequently.
This paper focuses on optimization problems faced in automated assembly of Printed Circuit Board (PCB). In order to optimize the throughput rate of these automated machines, the time taken for the pick and place operation for each board has to be minimized. In this paper, the component placement sequence problem is modeled as a Traveling Salesman Problem (TSP) and is optimized by Genetic Algorithms (GAs). In this study, components are placed on PCB where the process of pick-up and placement occurs starting from an empty multi-headed placement machine moving to pick up the components from the feeder magazine. The number of components to be picked and placed can range from a minimum of one to a maximum of four, depending on its contribution to minimize tour distance. The difference in size of components is handled by the tool change process, which brings the optimization problem closer to real machine situation. The paper suggests GA as a better alternative to other heuristic solution approaches such as Variable Neighborhood Search (VNS) and local optimum search. GAs are more promising as a global and robust method of solution and it permits a simpler mathematical model to solve a component assembly problem. The tool change factor, which was not incorporated in previous studies have been included in the present paper for the first time.
In this paper, we present a multi-criteria optimization model of a disassembly-to-order system to determine the best combination of the number of each product type to be taken bark at the end-of-life and disassembled to meet the demand for items and materials retrieved from them under a variety of physical, financial and environmental constrains so as to achieve the preemptive goals of maximum total profit, maximum sales from materials, minimum number of disposed items, minimum number of stored items, minimum cost of disposal and minimum cost of preparation, in that order. When solved, the model provides the number of reused, recycled, stored and disposed items as well as the values of a host of other performance measures. A case example is presented to illustrate the model's implementation.
In the past few years, the electronics industry has undergone an explosion in new products and technologies. This fierce global competition has resulted in the decision by many companies to outsource manufacturing in order to concentrate on research and development, marketing, and sales. This has given rise to a number of companies who solely provide electronics manufacturing services. Exchange of information between product developers and contract manufacturers, especially information about product demand, is critical to the success of this business model. In this paper, we analyze forecast data obtained from a local contract manufacturer in order to better understand forecast variability and its reasons. We find that forecast variability tends to increase as the production period approaches, despite common belief that forecasts get better with time. We also discuss the impact that chronic material unavailability and product mix have on forecast variability. Finally, we see that unmet demand pushed in the next production period can distort forecasts and result in unrealistic expectations.
Several control problems of PCB assembly industry involve the estimation of the component placement times for various different placement robots. A standard approach, widely used in scientific literature, is to let the time be a linear function of the number of components. In this paper, we demonstrate that a more realistic model should include additional parameters (e.g. the number of different component types and the board size). Linear multiregression indicates that the coefficient of determination for the improved model is over 90 percent.
As the use of personal computers (PCs) increases, their short life cycle and the fact that they contain many hazardous materials have a significant influence on finding economical and environmentally benign means for their retirement and disposal. Many communities are mandating the recycling of these PCs to recover parts and materials and to minimize the amount of waste landfilled or incinerated. An industry to process end-of-life PCs is evolving to take advantage of this stream of materials. At present, PC recycling is not profitable unless the recycler receives a processing fee. In this paper, the factors that most influence the profitability of end of life processing of PCs are investigated and reported so that PC manufacturers and legislators may better develop products and policies to ensure that there is a viable PCs recycling infrastructure.
Unrelated parallel machine scheduling problems have been well-studied for several single objective cases under deterministic processing conditions. While these problems are adequate for some manufacturing environments, there are situations where multiple objectives need to be considered. We report on a case for a Japanese printed wiring board manufacturer's drilling operation, which is measured on five key performance objectives and which is subject to processing time variability and random equipment breakdowns (usually in the form of jams). This research presents and evaluates an optimization approach against three dispatching heuristics to determine how these different scheduling approaches are impacted by processing time variability and equipment breakdowns and to determine which perform best under various levels of uncertainty.
This paper presents a new batch machine dispatching policy that incorporates knowledge about future arrivals and the status of critical machines in subsequent (downstream) processing into the batch processing decision process. The intent is to create a methodology that balances the time lots spend waiting at a batch machine with the time spent in setup, thus improving the overall cycle time. Using discrete-event simulation, this heuristic is compared to existing heuristics that do not consider downstream operations to evaluate their impact on the cycle time both for a small three-machine system and a semiconductor manufacturing facility model. The results showed considerable improvement in cycle times for the small three-machine system. Results for the semiconductor-manufacturing model with downstream batching indicate that the new heuristic is robust but produces results consistent with the current standard heuristic; however, with further modifications, this heuristic may be capable of producing significantly better results.
This paper outlines a framework for flexible hoist scheduling that utilizes a knowledge-based simulation system to emulate multi-hoist operations in an electroplating line and derives hoist schedule to meet production demands. The selected hoist schedule is then used by the knowledge-based controller to control the material handling hoist operations in real-time. The paper describes the overall design of the knowledge-based simulator for material handling hoist operations that forms the fundamental part of the proposed framework. Most hoist schedules in industry are experimented on-line, which are costly and time consuming. The use of knowledge-based simulation will save both cost and time. The simulator aims at improving current manual approaches so that better schedules can be identified. We demonstrate the application of the simulator to bi-directional multi-hoist line configuration that is commonly found in industry today, where loading and unloading stations are on the same end of the line, and consider other more realistic system parameters than those considered in previous studies. Three new hoist scheduling heuristics axe proposed and their performances are compared with four previously studied rules. To this end, we also study the effect of several system variables such as hoist speed and inter-hoist distance on productivity. The simulation models developed are based on data from actual multi-hoist electroplating lines within several PCB manufacturing facilities.
In the manufacture of printed circuit boards, electronic components are attached to a blank board by one or more pick-and-place machines. Frequent machine setups, though time-consuming, can reduce overall processing time. We consider the Integrated Clustering and Machine Setup (ICMS) model, which incorporates this tradeoff between processing time and setup time and seeks to minimize the sum of the two. Solving this model to optimality is intractable for very large-scale instances. We show that ICMS is NP-hard and consequently propose and test a heuristic based on multi-exchange neighborhood search structures. Initial numerical results arevery encouraging.
This paper describes the structured methodology for formulating end-of-life strategies, using specific examples from consumer electronics products. The End-of-Life Design Advisor (ELDA), a web based tool, was developed to determine what end-of-life strategy is possible according to the products' technical characteristics. The classification of end-of-life strategies is compared with current industry practice, in order to evaluate and validate the method. ELDA succeeded in classifying end-of-life strategies in agreement with industry best practices for 86% of the products. This ability to classify end-of-life strategies enables designers to redesign products that move to end-of-life strategies with lower environmental impact. This paper also presents preliminary work on the calculation of the environmental impact of the end-of-life strategies, through a model called End-of-Life Strategy Environmental Impact Model (ELSEIM). By understanding better the end-of-life strategy appropriate for the product, the research results can help designers develop appropriate (and profitable) end-of-life strategies for their unique position, systematically.
In this paper, we address the problem of demand driven disassembly used to determine the optimal lot-sizes of end-of-life (EOL) products to disassemble so as to fulfil the demand of various components from a mix of different product types that have a number of components and/or modules in common. We discuss two approaches, viz., (1) the disassembly graph approach that is based on the study of the disassembly of mechanical products and (2) the component-disassembly optimization model that focuses on parts recovery by applying the reverse bill of materials. Although elegant, the main disadvantages of these two approaches are redundancy and nonlinearity respectively. To overcome these disadvantages, we propose a new method that combines the advantages of both approaches without their disadvantages. This is called the tree network model, which is a linear description of the demand driven, multiple product problem that includes commonality and multiplicity. Because of its simple structure, it can also be applied in dynamic situations, which is useful in problems that are related to production planning and inventory control in reverse logistics.
The paper presents a new mathematical programming approach for scheduling of printed wiring board assembly in SMT (Surface Mount Technology) lines. Various configurations of SMT lines encountered in the electronics industry are described and compared. An SMT line consists of several processing stages in series, separated by finite intermediate buffers, where each stage has one or more identical parallel machines. A board which has completed processing on a machine may remain there and block the machine until a downstream machine becomes available for processing. The objective is to determine an assembly schedule for a mix of board types, so as to complete the boards in minimum time. New mixed integer programming formulations are presented for blocking scheduling of various configurations of SMT lines that are found in the electronics industry. The proposed models can be used for optimization of assembly schedules by using commercially available software for discrete programming. Numerical examples are provided to illustrate the proposed approach. The influence of process time variability and machine breakdowns on an SMT line's performance is discussed.
Proper planning of multi-track feeders in the printed circuit board (PCB) assembly can increase the capacity of surface mount machines and reduce feeder changeovers. This paper describes a methodology for developing a process plan to minimize the feeder setups in multi-track feeder systems. Two problems are investigated: component to multi-track feeder assignment and PCB sequencing. The assignment problem is formulated as a multi-dimension symmetric assignment problem with an integer-programming model. The objective is to maximize the total similarity of the component assignment. The PCB sequencing problem is solved in three stages: component and PCB grouping, intra- and inter-group PCB sequencing, and feeder setup planning. The interrelationship between the assignment and the sequencing problems is also investigated.
The traditional electronic packaging hierarchies present ct bottleneck for increasing system speed and density. A revolutionary rethinking is therefore necessary which aims to integrate or eliminate the current packaging hierarchies towards single level hierarchy integration. In this paper, such a novel packaging scenario is introduced. Its technology concerns and electric performance are studied.
Neural networks have been applied within manufacturing domains, in particular electronics industries, to address the inherent complexity, the large number of interacting process features and the lack of robust analytical models of real industrial processes. The ability of neural systems to provide nonlinear mappings between process features and desired outputs has been the major driving force behind implementations. One of the major issues limiting the widespread industrial uptake of neural systems is the lack of detailed understanding of their design, implementation and operation. In many cases, network topologies and training parameters are systematically varied until satisfactory convergence is achieved. There is little discussion of the rationale behind the adopted training methods. A review of research into the functions that can be readily represented by neural networks are presented in this paper. The application focus is the control and monitoring of a discrete manufacturing process that is part of the manufacturing cycle of mixed technology surface mount printed circuit boards. Detailed knowledge of the process operation and functionality that can be represented by simple network topologies have been combined to develop a structured, partially interconnected neural network that provides optimised convergence performance. A comparison of the designed solution with standard approaches to neural network implementation is given. It has been demonstrated that if there is sufficient confidence in the operation of the process, input feature interaction within the network can be constrained to produce a robust control and monitoring system.
Polymer and polymer-based composites are widely used in the electronic packaging industry. There is a need to shorten the processing time for cost-effective reasons. Microwave radiation is recognized as an alternative to the conventional thermal treatment. This paper presents the fundamental concept of MW used as heating source for curing polymers. Upon literature survey, comparison between thermal and MW approaches was given. Various variables affecting MW applications were analyzed. Metal effect under microwave radiation was also discussed corresponding to the metal-filled electrically conductive adhesives. At last, a conclusion was made that microwave as the source of energy can offer higher processing rate than thermal treatment while no adverse effect is exerted on the properties of the processed materials; an even higher heating rate will require considerations of the influence of various variables; metal-filled electrically conductive adhesives could be heated with variable frequency microwave with no arcing being incurred.
There has been a great deal of interest in recent years relating to the use of direct liquid immersion cooling for heat dissipation from microelectronic components. The high levels of circuit integration place size constraints on any techniques used for heat removal. This paper presents results obtained using a novel heat sink which addresses the need for a mechanism to remove high heat fluxes, while at the same time dealing with space limitations, This heat sink consists of cavity structures etched into a silicon surface, utilizing manufacturing processes similar to those used in the production of the chips themselves. The enhanced surface features are in the form of pyramidal cavities with a mouth size as low as 12 mum, and cavity density as high as 420 cavities/cm(2). Tests were conducted using thin film heaters deposited on a silicon surface as the heat source. Data obtained from a bonded heat source-heat sink combination indicated very low values of overshoot at boiling incipience. The cooling characteristics of these etched silicon heat sinks were studied in two dielectric fluids; R-113 and FC-72. The boiling performance of an epoxy bonded heat sink was compared with that of a direct bonded heat sink.
Isotropic conductive adhesives (ICAs) have been developed as an alternative for traditional tin/lead (Sn/Pb) solders for electronics interconnect applications. Low processing temperature, elimination of lead, no-flux, no-clean process and the simple processing are some of the advantages of using ICAs. However, compared to the mature soldering technology, conductive adhesive technology is till in its infancy, and as such, there are some limitations for current commercial ICAs. Besides the conductivity fatigue and poor impact strength, electrically conductive adhesives tend to have lower current carrying capability and higher contact resistance in comparison to the conventional Sn/Pb solder. This is mainly due to the difference in the electrical conductive mechanisms between ICAs and Sn/Pb solders. This study focuses on current capacity measurements and other electrical properties, such as inductance, capacitance and resistance with a LCR meter and a four-point probe both in the direct current (DC) and alternating current (AC) ranges of the ICAs.
