A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

A predictive maintenance approach based in big data analysis

With the evolution of information systems, the data flow escalated into new boundaries, allowing enterprises to further develop their approach to important sectors, such as production, logistic, IT and especially maintenance. This last field accompanied industry developments hand in hand in each of the four iterations. More specifically, the fourth iteration (Industry 4.0) marked the capability to connect machines and further enhance data extraction, which allowed companies to use a new data-driven approach into their specific problems. Nevertheless, with a wider flow of data being generated, understanding data became a priority for maintenance-related decision-making processes. Therefore, the correct elaboration of a roadmap to apply predictive maintenance (PM) is a key step for companies. A roadmap can allow a safe approach, where resources may be placed strategically with a ratio of low risk and high reward. By analysing multiple approaches to PM, a generic model is proposed, which contains an array of guidelines. This combination aims to assist maintenance departments that wish to understand the feasibility of implementing a predictive maintenance solution in their company. To understand the utility of the developed artefact, a practical application was conducted to a production line of HFA, a Portuguese Small and Medium Enterprise.Através da evolução dos sistemas de informação (SI), o fluxo de dados atingiu novos limites, permitindo assim às empresas desenvolver diferentes focos e aplicar novas perspetivas nos departamentos fulcrais à sua atividade, tais como produção, logística e, mais especificamente, a manutenção. Esta última componente evolui paralelamente à indústria, evidenciando novos desenvolvimentos em cada iteração da mesma. Particularmente, a quarta revolução industrial destacou-se pela capacidade de conectar máquinas entre si e pela evolução posterior do processo de extração de dados. Assim, surgiu uma nova perspetiva focada na utilização dos dados extraídos para resolução de problemas. Consequentemente, esta inovação fomentou uma redefinição das prioridades nas decisões tomadas relativas à manutenção, dando primazia à compreensão dos dados gerados. Por conseguinte, a correta elaboração de um plano de implementação de manutenção preditiva (MP) destaca-se como um passo fulcral para as empresas. Este plano tem como objetivo permitir uma abordagem mais segura, possibilitando assim alocar os recursos estrategicamente, reduzindo o risco e potenciando a recompensa. Mediante a análise de múltiplas abordagens de MP, é proposto um modelo genérico que reúne um conjunto diretrizes. Este tem intuito de auxiliar os departamentos de manutenção que pretendem compreender a viabilidade da instalação de uma solução de MP na empresa. A fim de perceber a utilidade dos artefactos desenvolvidos, foi realizada uma aplicação prática do modelo numa pequena e média empresa (PME)

JTEC Panel report on electronic manufacturing and packaging in Japan

This report summarizes the status of electronic manufacturing and packaging technology in Japan in comparison to that in the United States, and its impact on competition in electronic manufacturing in general. In addition to electronic manufacturing technologies, the report covers technology and manufacturing infrastructure, electronics manufacturing and assembly, quality assurance and reliability in the Japanese electronics industry, and successful product realization strategies. The panel found that Japan leads the United States in almost every electronics packaging technology. Japan clearly has achieved a strategic advantage in electronics production and process technologies. Panel members believe that Japanese competitors could be leading U.S. firms by as much as a decade in some electronics process technologies

The durability of solder joints under thermo-mechanical loading; application to Sn-37Pb and Sn-3.8Ag-0.7Cu lead-free replacement alloy

Solder joints in electronic packages provide mechanical, electrical and thermal connections. Hence, their reliability is also a major concern to the electronic packaging industry. Ball Grid Arrays (BGAs) are a very common type of surface mount technology for electronic packaging. This work primarily addresses the thermo-mechanical durability of BGAs and is applied to the exemplar alloys; traditional leaded solder and a popular lead-free solder. Isothermal mechanical fatigue tests were carried out on 4-ball test specimens of the lead-free (Sn-3.8Ag-0.7Cu) and leaded (Sn-37Pb) solder under load control at room temperature, 35°C and 75°C. As well as this, a set of combined thermal and mechanical cycling tests were carried out, again under load control with the thermal cycles either at a different frequency from the mechanical cycles (not-in-phase) or at the same frequency (both in phase and out-of-phase). The microstructural evaluation of both alloys was investigated by carrying out a series of simulated ageing tests, coupled with detailed metallurgical analysis and hardness testing. The results were treated to produce stress-life, cyclic behaviour and creep curves for each of the test conditions. Careful calibration allowed the effects of substrate and grips to be accounted for and so a set of strain-life curves to be produced. These results were compared with other results from the literature taking into account the observations on microstructure made in the ageing tests. It is generally concluded that the TMF performance is better for the Sn-Ag-Cu alloy than for the Sn-Pb alloy, when expressed as stress-life curves. There is also a significant effect on temperature and phase for each of the alloys, the Sn-Ag-Cu being less susceptible to these effects. When expressed as strain life, the effects of temperature, phase and alloy type are much diminished. Many of these conclusions coincided with only parts of the literature and reasons for the remaining differences are advanced

The development of a process for the production of textiles with fully embedded electronics

Many attempts to combine Electronics and Textiles have been realised for many years now. At the beginning with the introduction of conductive wires, then with the introduction of sensors and more complex circuits onto an everyday garment. The next step of evolution of combining these seemingly different fields is to integrate the electronics inside a textile structure, so that it will provide a seamless implementation of both worlds into everyday life. The microelectronics, mechanical, electrical, computing and chemical engineering advances of the last years, can ensure that, nowadays, this is feasible. Because of the minuscule dimensions of the electronic components, so that can be integrated inside the thin-by-nature yarn, and the necessity of a flexible and bendable structure overall, the task required is not of a small scale and has no prerequisite. This Thesis provides the backbone of an innovative technique to achieve the above goal in an automated or semi-automated, accurate, repeatable, reliable and time-cost effective way, combining all the required procedures, outlining the issues and proposing solutions on a plethora of them. This research's outcome, after both manual and automated implementation of the microelectronic component encapsulation concept, proves that automation of the process is feasible with more research and funding in the future. Because this is an innovative and challenging in its implementation, as far as the tiny dimensions of the electronic components are concerned, more testing and physical implementation must be conducted with the contribution of a team of people from different disciplines, in order to finalise it and produce the first linear and continuous version of the machine that can automatically produce electronic yarns, i.e. yarn with electronic components inside its core. The importance of this Thesis is that it sets the foundations, guidelines and requirements for the development of an all-new manufacturing procedure and the creation of a new machine, i.e. the Electronic Yarn Machine -EYM- in the future

Characterisation of lead-free solder pastes and their correlation with the stencil printing process performance

Solder pastes are complex materials whose properties are governed by many factors. Variations exhibited in solder paste characteristics make it increasingly difficult to understand the correlations between solder paste properties and their printing process performance. The recent EU directives on RoHS (Restriction of Hazardous Substances – enacted by UK regulations) and WEEE (Waste from Electrical and Electronic Equipment) has led to the use of lead-free soldering in the SMA (surface mount assembly) process, and an urgent need for better understanding of the characteristics and printing performance of new solder paste formulations. Equally, as the miniaturisation of hand-held and consumer electronic products continues apace, the solder paste printing process remains a real challenge to the electronics assembly industry. This is because the successful assembly of electronic devices at the ultra-fine pitch and flip-chip geometry requires the deposition of small and consistent paste deposits from pad to pad and from board to board. The paste printing process at this chip-scale geometry depends on conditions such as good paste roll, complete aperture filling and paste release from the apertures onto the substrate pads. This means that the paste flow and deformation behaviour, i.e. the paste rheology, is very important in defining the printing performance of any solder paste. Rheological measurements can be used as a tool to study the deformation or flow experienced by the pastes during the stencil printing process. In addition, the rheological measurements can also be used as a quality control tool in the paste production process for identifying batch-to-batch variation, and to reduce the associated printing defects in the paste printing process. The work reported here on the characterisation of lead-free solder pastes and their correlation with the stencil printing process is divided into five main parts. The first part concerns the study of the effect of variations in flux and particle size distribution (PSD) on the creep recovery performance of lead-free solder pastes used for flip-chip assembly. For this study, a novel technique was calculating the extent of paste recovery and hence characterising the slumping tendency in solder pastes. The second part of the study concerns the influence of long-term ageing on the rheology and print quality of lead-free solder pastes used for flip-chip assembly, and the main focus of the work was to develop methodologies for benchmarking new formulations in terms of shelf life, rheological deterioration and print performance. The third part of the work deals with a rheological simulation study of the effect of variation in applied temperature on the slumping behaviour of lead-free solder pastes, and the fourth part considers the rheological correlation between print performance and abandon time for lead-free solder paste used for flip-chip assembly. The final part of the study concerns the influence of applied stress, application time and recurrence on the rheological creep recovery behaviour of lead-free solder pastes. The research work was funded through the PRIME Faraday EPSRC CASE Studentship grant, and was carried out in collaboration with Henkel Technologies, Hemel Hempstead, UK. The extensive set of results from the experimental programme, in particular relating to the aspect of key paste performance indicators, has been adapted by the industrial partner for implementation as part of a quality assurance (QA) tool in its production plant, and the results have also been disseminated widely through journal publications and presentations at international conferences

Automating Fault Detection and Quality Control in PCBs: A Machine Learning Approach to Handle Imbalanced Data

Printed Circuit Boards (PCBs) are fundamental to the operation of a wide array of electronic devices, from consumer electronics to sophisticated industrial machinery. Given this pivotal role, quality control and fault detection are especially significant, as they are essential for ensuring the devices' long-term reliability and efficiency. To address this, the thesis explores advancements in fault detection and quality control methods for PCBs, with a focus on Machine Learning (ML) and Deep Learning (DL) techniques. The study begins with an in-depth review of traditional approaches like visual and X-ray inspections, then delves into modern, data-driven methods, such as automated anomaly detection in PCB manufacturing using tabular datasets. The core of the thesis is divided into three specific tasks: firstly, applying ML and DL models for anomaly detection in PCBs, particularly focusing on solder-pasting issues and the challenges posed by imbalanced datasets; secondly, predicting human inspection labels through specially designed tabular models like TabNet; and thirdly, implementing multi-classification methods to automate repair labeling on PCBs. The study is structured to offer a comprehensive view, beginning with background information, followed by the methodology and results of each task, and concluding with a summary and directions for future research. Through this systematic approach, the research not only provides new insights into the capabilities and limitations of existing fault detection techniques but also sets the stage for more intelligent and efficient systems in PCB manufacturing and quality control

Prognostics and health management of light emitting diodes

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed