Printed Circuit Board Manufacturing Research Articles

Text mining approach for bottleneck detection and analysis in printed circuit board manufacturing

This paper proposes a production scheduling procedure for the production lines of a printed circuit board company. Linearity features of the manufacturing process of this company are characterized and exploited for developing an efficient three-phase scheduling procedure. The production line is formulated as a linear job shop that resembles a flow shop. First, the N-gram modelling approach is adopted to analyse the data sets to detect the machines that would be candidates of the bottleneck in the production lines. Second, according to the candidates, the bottleneck data are extracted from the original data sets and solved as flow shops by a mixed integer programming model. The optimal solutions of the bottleneck flow shop is next extended by incorporating upstream and down stream operations to form approximate solutions of the original problems. We propose three different strategies for forming the approximate solutions and the best one is designated as the final solution. The performance of the proposed heuristic algorithm is tested and compared with the well-known NEH algorithm through numerical instances from real production lines. Statistics indicate that for most instances including up to 80 jobs the proposed method delivers competitive solutions within a much shorter time than the NEH algorithm.

Cost-Sensitive Siamese Network for PCB Defect Classification.

After the production of printed circuit boards (PCB), PCB manufacturers need to remove defected boards by conducting rigorous testing, while manual inspection is time-consuming and laborious. Many PCB factories employ automatic optical inspection (AOI), but this pixel-based comparison method has a high false alarm rate, thus requiring intensive human inspection to determine whether alarms raised from it resemble true or pseudo defects. In this paper, we propose a new cost-sensitive deep learning model: cost-sensitive siamese network (CSS-Net) based on siamese network, transfer learning and threshold moving methods to distinguish between true and pseudo PCB defects as a cost-sensitive classification problem. We use optimization algorithms such as NSGA-II to determine the optimal cost-sensitive threshold. Results show that our model improves true defects prediction accuracy to 97.60%, and it maintains relatively high pseudo defect prediction accuracy, 61.24% in real-production scenario. Furthermore, our model also outperforms its state-of-the-art competitor models in other comprehensive cost-sensitive metrics, with an average of 33.32% shorter training time.

Framework and deployment of a cloud-based advanced planning and scheduling system

• This paper presented a cloud-based and advanced planning and scheduling (C-APS) system framework, it can be used as a reference for system developers to develop a cloud-based and object-oriented APS with simulation-based scheduling engine that can be applied in practice. • This paper proposed the service structure and approach of deploying the C-APS system in the public cloud infrastructure, platform, and service provider (e.g., AWS TM ) and hybrid cloud platforms. Readers may reference this approach to deploy a server-based enterprise information application system to a cloud-based system. • This paper presented the package diagram to collect the necessary data for building the case company's virtual factory model required by the C-APS system; this feature can significantly improve modeling efficiency and data stability. Besides, the C-APS's objected-oriented, simulation-based scheduling engine has the characteristics of quickly expanding and adjusting to the virtual plant model. • The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain; the scheduling quality is high; the computing time is short and acceptable for practical application. Many small and medium-sized manufacturing enterprises (SMEs) have already implemented enterprise resource planning (ERP) and manufacturing execution system (MES) and began to start the journey of cloud manufacturing; however, the high cost of hardware and software investment, implementation, and maintenance usually hinder SMEs from adopting an advanced planning and scheduling (APS) system. This paper aims to develop a cloud-based APS (C-APS) system framework, the service structure, and approach of deploying the C-APS system in a public cloud infrastructure platform and service provider or hybrid cloud platform. The package diagram is proposed for building the C-APS system's virtual factory model to improve modeling efficiency and data stability. The C-APS system is a cloud-based and object-oriented software; its simulation-based scheduling engine can generate the significant production and operations schedule, and has the characteristics of on-demand self-service, quickly expanding and adjusting to the virtual plant model. The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain. The scheduling quality is high; the computing time is short and acceptable for practical application.

Remediation of Soil Containing Sludge Generated by Printed Circuit Board Production and Electroplating

This paper examines the environmental conditions at the premises of printed circuit boards (PCBs) manufacturers, which have electroplating plants. It provides a brief overview of the key aspects of adverse environmental impacts of wastes generated by PCB manufacture and electroplating plants. The aim of this research was to improve the test method for evaluation of wastewater effect on the soil salinity at the premises of PCBs manufacturers. The object of research was the process of extraction and use of copper from wastewater generated by PCBs manufacture and electroplating. As an example, the process of sludge formation during PCBs etching has been reviewed. With the etching line capacity of 14 m2/h, one-shift manufacturing process will result in the production of up to 2,500 kg of sludge monthly. For enterprises with capacities of 2,000–4,000 m2 circuits, this means annual accumulation at their premises of up to 70 tons of wastes in the form of sludge. Estimates suggest that the upper half-meter layer of the aeration zone will be qualified as slightly saline in one year after accumulation of the sludge. In subsequent years, the salt content will increase and saline soil can be found at the depths of 1.5–2 m over ten years of storage. The authors of this paper propose to treat spent etching solutions applying regeneration technology in order to reduce the amount of sludge. With this technology, it is possible to use the extracted metal as a secondary raw material for copper production and re-use the regenerated solution in PCBs etching. This paper provides estimated hazard indices calculated for the storage of sludge at the manufacturer’s premises before and after the implementation of the proposed technology. With regards to findings of the study, it has been proposed to reuse copper recovered from wastes as a raw material for the industry.

Case Study: An Improvement of Inspection Approach of Printed Multilayer Circuit Products Related to Visual Based Inspection Technique

Product quality is an important issue in all manufacturing industry nowadays to meet the customers’ requirement. In this regard, most of the factory has a special department namely Quality Control (QC) Department which control the product quality. The studied factory is a flexible printed circuit board manufacturer which is located in Bayan Lepas, Pulau Pinang, Malaysia. QC Department of the studied factory has high amount of work-in-progress (WIP). Therefore, the products have to be sent out to subcontractors for quality inspection. However, this method is not economically effective. Hence, the objective of this study was to develop an improved inspection method which will reduce the overall inspection time of the current inspection method (CIM). By reducing the inspection time, productivity of the department will increase and subsequently reduce the amount of WIP. This study is focused on the improvement of one product only. Before the improved inspection method (IIM) of the selected product was developed, performance of the CIM was evaluated by using one of the graphical productivity analysis charting techniques which is flow process chart. It was found that the IIM was able to reduce the inspection time of whole inspection process by 85.06 sec for every 12 panels inspected.

Comparison of PCB winding topologies for axial‐flux permanent magnet synchronous machines

Although axial-flux permanent magnet machines have high torque densities, challenges regarding mass production of stators make them a less appealing choice. Printed circuit board (PCB) axial-flux machine is a type of machine with a stator that is made of layers of PCB. Given the precise, fast, and cheap mass production capabilities of PCB manufacturers, PCB axial-flux machines stand as a viable alternative for conventional round-wire winding machines. In this study, five different winding topologies are compared. Their induced phase voltages and torque are calculated using the developed magnetic scalar potential method and finite element analysis (FEA). Proposed windings are tested on a 16-pole, 2000-RPM, double rotor-single stator axial-flux permanent magnet synchronous machine. Results showed that the parallel winding had the smallest resistance and loss. Moreover, radial and concentric winding had the highest induced voltage and torque while the radial winding had 20% less phase resistance than concentric. Also, the induced voltage of radial winding had the smallest total harmonic distortion in comparison with other winding types. A novel unequal width parallel winding is proposed and it is compared with parallel winding separately. It is found that by simply increasing the cross-section area of wave windings, it is possible to decrease copper loss by 17%.

Defect detection of printed circuit board based on lightweight deep convolution network

With the rapid development of the electronic industry, the defect detection of printed circuit board (PCB) components is becoming more and more important. The types of PCB components are diverse and accompanied by complex character information, which is difficult to identify. The traditional detection method is inefficient, and it is unable to effectively perform the diversified category detection of PCB components and character recognition in complex scenes. The deep convolutional neural network has obvious advantages in object detection and character recognition, which can be used to implement a PCB component defect detection system. In this study, the authors have established a lightweight PCB type detection model called LD-PCB, which can perform real-time detection while improving detection accuracy. In addition, in the character detection of PCB, they have established a fast and robust character recognition model, called CR-PCB. This model can effectively improve the accuracy of irregular character recognition. Finally, they established and published a dataset of PCB components, and combined with LD-PCB and CR-PCB to realise the PCB defect detection system. This system can realise the functions of defect detection, wrong insertion, missing insertion, and character recognition in industrial PCB production. The results show that the method proposed in this study can effectively detect defects on PCB components.

Predicting the printed circuit board cycle time of surface-mount-technology production lines using a symbiotic organism search-based support vector regression ensemble

This paper presents a symbiotic organism search (SOS)-based support vector regression (SVR) ensemble for predicting the printed circuit board (PCB) cycle time of surface-mount-technology (SMT) production lines. Being able to predict the PCB cycle time accurately is essential for optimising the SMT production schedule. Although a machine simulator can be reliably used for single-type PCB production, it is time-consuming and often inaccurate for the simulator to be applied for highly mixed orders in multiple flexible SMT production lines. Due to the dynamic changes in both PCB orders and SMT production lines, there is a diverse set of samples, but the size of similar samples is relatively small. An SVR model is therefore used to estimate the PCB cycle time, and the SOS algorithm is employed to optimise the SVR parameters. We assume that uncertainties during the assembly process can be captured by the characteristics of PCB and SMT lines, which are utilised as features to train the SVR model. To enhance the performance of the prediction accuracy, an SOS-SVR ensemble is proposed. Experiments based on datasets collected from a leading global electronics manufacturer confirm the efficiency of the proposed approach compared to industrial solutions currently in place and other machine learning methods.

Reliability Evaluation of Production System With In-Line Stockers

A reliability evaluation method is proposed for production systems by applying the capacitated-flow production network (CFPN) model. In particular, this article considers in-line stockers to monitor and to avoid blockage and starvation in the CFPN. First, the minimal capacity that all cells (group of machines) should provide to meet demand is generated. Second, status of stocker is monitored via the proposed “stocker volume check table” to calculate the volume usage of a stocker, and the stocker nonutilization is then obtained. Finally, the system reliability is derived in terms of the minimal capacity vector and stocker nonutilization. An important contribution of this article is to avoid complicated dependency calculation when multiple stockers are considered. A practical case of printed circuit board production is further studied to illustrate the applicability of the proposed method.

A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0

Printed Circuit Board (PCB) manufacturing is a kind of energy-intensive and pollution-intensive industries. With the increment of individualized requirements, PCB manufacturers face massive customized orders with a variety of specifications. The individualized customization on orders results in large differentials of the profit, energy consumption, and environmental pollution. Making energy-efficient order acceptance decisions can reduce carbon consumption and improve material utilization during the whole manufacturing process. An order acceptance decision model is established based on a loosely-coupled integration of deep learning and reinforcement learning techniques. Firstly, different from a simple assumption of the linear cost function in a small-scale manufacturing system, the deep learning algorithm is presented for accurately predicting the production cost, makespan, and carbon consumption of incoming PCB orders in the large-scale manufacturing system. Secondly, these predicted cleaner production indicators are combined with original order features to perform a reinforcement learning-based order acceptance decision. The proposed loosely-coupled deep reinforcement learning approach is verified with a dataset built based on data collected from a PCB manufacturer in China. This research is expected to provide an environment-friendly order acceptance decision-making approach for sustainable manufacturing in the Industry 4.0 context.

Fabrication of Parts with Integrated Circuits by Selective Electroless Plating of Additively Manufactured Plastic Substrates

The use of printed circuit boards (PCBs) has become essential in manufacturing parts with electronic functions. However, the placement of PCBs inside a product requires special plates or fixtures that need to be separately designed, fabricated, and assembled. In addition, PCB manufacturing generally utilizes various toxic metals and etchants. Thus, there is a need to address the issues of space, cost, and environmental toxicity inherent in PCBs. This paper proposes a novel method of fabricating PCB-free part with electronic circuits in a set using the casing or exterior of a product. This process enables the manufacturing of a type of molded interconnect device without conventional molding technologies by directly forming conductive circuits on the side or back of 3D-printed substrates using selective electroless plating. For 3D printing of the product structure, this study uses substrates made of polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS), which are commonly used in the popular material-extrusion 3D printing technology. As ABS and PLA have different chemical properties, the region to be plated with the electrical circuit is fabricated using ABS, whereas the remaining substrate is fabricated using PLA. ABS was chemically treated to allow electroless plating. A process for forming 3D circuits was developed and evaluated by selective electroless plating of the areas of the substrate requiring circuitry. To verify the applicability of the proposed process, circuits and integrated parts used in the industry were redesigned into a single component and fabricated by applying the proposed process. The results demonstrate that the proposed process has good practical applicability. Furthermore, it has low environmental toxicity with low requirements for material consumption and processing energy.

Development of a dummy guided formulation and exact solution method for TSP

A traveling salesman problem (TSP) is a problem whereby the salesman starts from an origin node and returns to it in such a way that every node in the network of nodes is visited once and that the total distance travelled is minimized. An efficient algorithm for the TSP is believed not to exist. The TSP is classified as NP-hard and coming up with an efficient solution for it will imply NP = P . The paper presents a dummy guided formulation for the traveling salesman problem. To do this, all sub-tours in a traveling salesman problem (TSP) network are eliminated using the minimum number of constraints possible. Since a minimum of three nodes are required to form a sub-tour, the TSP network is partitioned by means of vertical and horizontal lines in such a way that there are no more than three nodes between either the vertical lines or horizontal lines. In this paper, a set of all nodes between any pair of vertical lines or horizontal lines is called a block. Dummy nodes are used to connect one block to the next one. The reconstructed TSP is then used to formulate the TSP as an integer linear programming problem (ILP). With branching related algorithms, there is no guarantee that numbers of sub-problems will not explode to unmanageable levels. Heuristics or approximating algorithms that are sometimes used to make quick decisions for practical TSP models have serious economic challenges. The difference between the exact solution and the approximated one in terms of money is very big for practical problems such as delivering household letters using a single vehicle in Beijing, Tokyo, Washington, etc. The TSP model has many industrial applications such as drilling of printed circuit boards (PCBs), overhauling of gas turbine engines, X-Ray crystallography, computer wiring, order-picking problem in warehouses, vehicle routing, mask plotting in PCB production, etc.

Anisotropic Copper Electrodeposition on Microelectrodes

Copper electrodeposition is critical to electronics manufacturing, ranging across lengthscales from nanometer damascene plating to 100s of micrometers for through-silicon-vias in 3D interconnect packaging and even millimeter-scale in printed circuit board manufacturing. Essential to this process is formation of void-free copper structures in high aspect ratio cavities, which is achieved with various additives that enable superconformal or bottom-up electrodeposition. When the additive package consists of only a suppressor (polyether) and chloride, the superconformal phenomenon is driven by S-shaped negative-differential resistance (S-NDR), which promotes spatial bifurcation of the electrode surface into active and passive depositing regions. All S-NDR systems involve kinetic suppression, polyether-chloride adsorption in this case, and kinetic activation, potential-driven disruption of the polyether-chloride adlayer. The term S-NDR is derived from the s-shape the i-V curve expresses when cyclic voltammetry (CV) is post-experimentally corrected for ohmic resistance, resulting in a system that expresses a multiplicity of reaction rates (current) for a given driving force (potential).Electroanalytical measurements using microelectrodes (UMEs) offer an avenue for exploring S-NDR systems without significant iR-drop in solution. In other reaction systems, the reduced electrode dimension of UMEs has shown the ability to frustrate active-passive bifurcation, resulting in a homogeneously reacting electrode surface. Nonetheless, potential-controlled measurements such as cyclic voltammetry restrict S-NDR behavior from fully expressing itself, as the potential is the control parameter throughout the experiment. Current-controlled techniques, however, allow the thermodynamic state (i.e., potential) to dynamically shift as the polyether-chloride suppressing layer is formed and disrupted. In this work, current-controlled measurements in the form of linear galvanodynamic sweeps (GDS) are utilized to explore S-NDR behavior and electrode bifurcation during copper electrodeposition. GDS and CV measurements on UMEs are compared across a range of additive concentrations and polyether types. Influence of the operating mode on pattern formation is evaluated by in-situ optical microscopy on UMEs ranging from 10 μm to 25 μm in diameter. Spatial bifurcation during GDS measurements is further exploited to grow anisotropic copper deposits.

Evaluating the environmental protection strategy of a printed circuit board manufacturer using a Tw fuzzy importance performance analysis with Google Trends

Printed circuit boards (PCBs) are very important materials in consumer electronic products. The process of making PCBs usually uses chemicals, and large quantities of water are needed. PCB manufacturers are devoted to improving the process in order to conform to environmental protection rules and regulations. However, evaluating the performance of environmental protection strategies by quantitative methods is very difficult. Therefore, this study attempts to evaluate the environmental protection strategy of a PCB manufacturer using the novel weakest t-norm (Tw) fuzzy importance-performance analysis with Google Trends (TFIPA-Google). The TFIPA-Google methodology obtain advantages of Tw operations, IPA, and Google Trends, which can handle uncertainty based on a fuzzy matrix of IPA, reduce fuzzy accumulation using the Tw operators, and analyze social media viewpoints using Google Trends. This empirical example based on the TFIPA-Google method shows that the recovered waste material management system should be a priority for PCB manufacturers. Moreover, the TFIPA-Google method can provide more creditable information, based on Tw operations and volume of Google Trends for decision-makers, than a conventional importance-performance analysis (IPA) model.

Estimation of the PCB Production Process Using a Neural Network

Printed Circuit Boards (PCB) are an integral part of all electronic products, and the production process for printed circuit boards is quite complex. As the life cycle of electronic products becomes shorter and shorter, and the precision and signal bandwidth of electronic products become higher and higher, the manufacturing process of printed circuit boards is further complicated. Therefore, how to pre-evaluate the production difficulty before starting the production will effectively increase the efficiency and quality of printed circuit board production.
 Gerber file is the most commonly used data format for the printed circuit board industry. This file contains most of the parameters required for the manufacture of printed circuit boards. Therefore, this study uses a neural network to evaluate new PCB products before they are produced through the production parameters that are more influential in the PCB manufacturing process. This makes it possible to evaluate the difficulty and the required production process before the new PCB product is produced. This will be very beneficial for the PCB production schedule, quality control, and cost.

Manufacturing profile-free copper foil using laser shock flattening

Copper foil is a key material of printed circuit boards and plays an important role in the conductance of electric circuits and interconnection of electronic components. When high-frequency signals were transmitted in rough copper foil wires, the conductor resistance, wire loss, and signal loss increased because of the skin effect. To reduce the negative influence of the skin effect and improve the quality of the copper foil, a laser shock flattening (LSF) method was proposed to manufacture profile-free copper foil with high performance. It was concluded that the better flattening effect for large-area profile-free copper foil could be achieved at a pulse energy of 0.25 J and an overlap rate of 25%, and its surface roughness decreased by 67.0% from 52.1 nm to 17.2 nm. Subsequently, to determine the mechanism for the flattened deformation of copper foil induced by LSF, the microstructures of the copper foil before and after flattening were characterised using transmission electron microscopy. A higher dislocation density and a few deformation twins were found in the profile-free copper foil. Ultimately, nano-indentation, micro-tensile, and electrochemical corrosion tests indicated that the mechanical properties and corrosion resistance of the copper foil were significantly improved by LSF. This technique would enable the successful fabrication of large-area profile-free copper foil with high performance for the emerging applications of ultra-high-frequency signal communication and printed circuit board manufacture.

Photopolymerization Using Metal Oxide Semiconducting Nanoparticles for Epoxy-Based Coatings and Patterned Films

Photopolymerization is fundamental to many applications such as printed circuit board manufacturing, dentistry, coating, and stereolithography 3D-printing. However, the current organic cationic ini...

Environmental sensitivity of printed circuit board manufacturing to Cu recycling rate, transportation and various energy sources

This study concentrates on environmental impacts of PCB manufacturing. The objective is to examine the effect of Cu recycling rate, transportation, energy sources and end-of-life (EoL) on global wa ...

Environmental sensitivity of printed circuit board manufacturing to Cu recycling rate, transportation and various energy sources

Environmental sensitivity of printed circuit board manufacturing to Cu recycling rate, transportation and various energy sources

An innovative method of recycling metals in printed circuit board (PCB) using solutions from PCB production

Waste printed circuit boards (WPCBs) have both a potentially high resource value and hazardous drawbacks. Meanwhile, large quantities of corrosive waste solutions are generated in PCB production. Existing methods for recycling metals in WPCBs produce high yields but unfortunately produce secondary pollution. In this study, to minimize these disadvantages, a win-win innovative recycling method for WPCBs was developed using waste solutions that are generated in PCB production. Both of the waste solutions - waste tin stripping solution (WTSS) and waste etching solution (WES) - had an enormous capacity to extract Pb, Sn, and Cu. It was suggested that 1 L of WTSS was potentially capable for dissolving solder from 3.6 to 7.2 kg of WPCBs under room temperature, while WES was capable for Cu leaching from 0.13-0.35 kg of WPCBs. Compared with conventional leaching solutions, it was demonstrated that approximately 1 kW h of electricity could be saved from the recycling process when WTSS and WES were used to recycle only 1 kg of WPCBs. The proposed approach can be expected to significantly reduce energy consumption for recycling metals from WPCBs, without additional waste solution generated, and to increase the potential value of WTSS and WES, as they can facilitate the recycling process.