For the analysis of the wet pressing process, which was presented in the first part of this paper, a theoretical approach was chosen. This enabled the pre-definition of three quality-related priorities which now will be considered in detail in the second part. For further analysis, real process data, recorded in an early phase of the process implementation, are used. The challenge is that in this process status, the availability of data is limited or the data sets are incomplete. Supported by the theoretical approach, an easier interpretation of the process data, and in case of ambiguous issues, an accelerated decision making is expected. The objective is to show that this combination is suitable for the process analysis in an early production phase.

The wet pressing process represents a new production method for carbon fibre-reinforced plastics components. Due to the low cycle times, it is suitable for use in the automotive industry. Therefore, a sufficient degree of industrialisation needs to be achieved, which is characterised by a stable process. The knowledge about relevant process parameters, their interactions, and influence on the part quality builds the basis of an economic process. This is a major challenge, since in the early stage of process development the available amount of recorded process data is small and the data sets are not complete. As the implementation of time-, material-, and cost-intensive experiments represents no acceptable alternative, a theoretical approach is chosen. This article describes a theoretical procedure to define the critical factors of the wet pressing process with significantly less resource input.

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Factory shop floors of the future will see a significant increase in interconnected devices for monitoring and control. However, if a Service Orientated Architecture (SOA) is implemented on all such devices then this will result in a large number of permutations of services and composite services. These services combined with other business level components can pose a huge challenge to manage as it is often difficult to keep an overview of all the devices, equipment and services. This thesis proposes an SOA based novel assimilation architecture for integrating disparate industrial hardware based processes and business processes of an enterprise in particular the plastics machinery environment. The key benefits of the proposed architecture are the reduction of complexity when integrating disparate hardware platforms; managing the associated services as well as allowing the Micro Injection Moulding (µIM) process to be monitored on the web through service and data integration. An Enterprise Service Bus (ESB) based middleware layer integrates the Wireless Sensor Network (WSN) based environmental and simulated machine process systems with frontend Google Gadgets (GGs) based web visualisation applications. A business process framework is proposed to manage and orchestrate the resulting services from the architecture. Results from the analysis of the WSN kits in terms of their usability and reliability showed that the Jennic WSN was easy to setup and had a reliable communication link in the polymer industrial environment with the PER being below 0.5%. The prototype Jennic WSN based µIM process monitoring system had limitations when monitoring high-resolution machine data, therefore a novel hybrid integration architecture was proposed. The assimilation architecture was implemented on a distributed server based test bed. Results from test scenarios showed that the architecture was highly scalable and could potentially allow a large number of disparate sensor based hardware systems and services to be hosted, managed, visualised and linked to form a cohesive business process.

Metall- und Maschinenbauunternehmen müssen im Durchschnitt pro Jahr ca. zwei Prozent ihres Umsatzes für Strom und Erdgas ausgeben und die Unternehmer gehen von weiteren Preissteigerungen aus. Damit rückt das Thema Energieeinsparung stärker denn je in den Fokus und wird zu einem strategischen Faktor. Um Kosten zu sparen und Wettbewerbsvorteile zu sichern, ist es notwendig, zielgenaue Energieeinsparmaßnahmen einzuleiten. Die ersten Maßnahmen, welche die meisten Maschinenbauunternehmen umsetzen, sind die Erneuerung der Beleuchtungs-, Heizungs- und Lüftungsanlage, die Verbesserung der Drucklufterzeugung sowie die thematische Sensibilisierung der Mitarbeiter. Aber auch in Maschinen mit ihren dazugehörigen elektrischen Antrieben, Lüftern und Aggregaten verbirgt sich eine große Menge an Optimierungspotenzial. Allerdings ist es hier notwendig nicht die Verbraucher im Einzelnen, sondern die Maschine und deren Prozesse im Ganzen zu betrachten. Meist fehlen hierfür aber geeignete Schnittstellen, um die Messwerte von Sensoren (bspw. Temperatur-, Drucksensoren, etc.) und Antrieben auslesen zu können, was dazu führt, dass diese Potenziale nicht ausgeschöpft werden.

The average metal and mechanical engineering company must spend around two per cent of its annual turnover on electricity and natural gas, and companies are expecting further price increases. As a result, the issue of energy saving is becoming more of a strategic factor than ever before. In order to save costs and ensure competitive advantages, it is necessary to introduce precise energy-saving measures. The first steps taken by most mechanical engineering companies are to replace lighting, heating, and ventilation systems, improve compressed air generation and raise employee awareness. However, there is also a great potential for optimization in machines with their individual electrical drives, fans, and units. In this case, though, it is necessary to look at the machine and its processes as a whole rather than the individual electrical energy consumers. In most cases, however, there is a lack of suitable interfaces for analyzing the measured values from sensors (e.g. temperature, pressure sensors, etc.) and drives, which concludes that this potential is not fully exploited.

Capacitor discharge welding is an efficient, cost-effective and stable process. It is mostly used for projection welding. Real-time monitoring is desired to ensure quality. Until this point, measured process quantities were evaluated through expert systems. This method takes much time for developing, is strongly restricted to specific welding tasks and needs deep understanding of the process. Another possibility is quality prediction based on process data with machine learning. This method can overcome the downsides of expert systems. But it requires classified welding experiments to achieve a high prediction probability. In industrial manufacturing, it is rarely possible to generate big sets of this type of data. Therefore, semi-supervised learning will be investigated to enable model development on small data sets. Supervised learning is used to develop machine learning models on large amounts of data. These models are used as a comparison to the semi-supervised models. The time signals of the process parameters are evaluated in these investigations. A total of 389 classified weld tests were performed. With semi-supervised learning methods, the amount of training data necessary was reduced to 31 classified data sets.

Online quality assurance is crucial for elevating product quality and boosting process productivity in advanced manufacturing. However, the inherent complexity of advanced manufacturing, including nonlinear process dynamics, multiple process attributes, and low signal/noise ratio, poses severe challenges for both maintaining stable process operations and establishing efficacious online quality assurance schemes. To address these challenges, four different advanced manufacturing processes, namely, fused filament fabrication (FFF), binder jetting, chemical mechanical planarization (CMP), and the slicing process in wafer production, are investigated in this dissertation for applications of online quality assurance, with utilization of various sensors, such as thermocouples, infrared temperature sensors, accelerometers, etc. The overarching goal of this dissertation is to develop innovative integrated methodologies tailored for these individual manufacturing processes but addressing their common challenges to achieve satisfying performance in online quality assurance based on heterogeneous sensor data. Specifically, three new methodologies are created and validated using actual sensor data, namely, (1) Real-time process monitoring methods using Dirichlet process (DP) mixture model for timely detection of process changes and identification of different process states for FFF and CMP. The proposed methodology is capable of tackling non-Gaussian data from heterogeneous sensors in these advanced manufacturing processes for successful online quality assurance. (2) Spatial Dirichlet process (SDP) for modeling complex multimodal wafer thickness profiles and exploring their clustering effects. The SDP-based statistical control scheme can effectively detect out-of-control wafers and achieve wafer thickness quality assurance for the slicing process with high accuracy. (3) Augmented spatiotemporal log Gaussian Cox process (AST-LGCP) quantifying the spatiotemporal evolution of porosity in binder jetting parts, capable of predicting high-risk areas on consecutive layers. This work fills the long-standing research gap of lacking rigorous layer-wise porosity quantification for parts made by additive manufacturing (AM), and provides the basis for facilitating corrective actions for product quality improvements in a prognostic way. These developed methodologies surmount some common challenges of advanced manufacturing which paralyze traditional methods in online quality assurance, and embody key components for implementing effective online quality assurance with various sensor data. There is a promising potential to extend them to other manufacturing processes in the future. / Ph. D.

O desafio para a área de monitoramento de processos, em indústrias químicas, ainda é a etapa de detecção, com a necessidade de desenvolvimento de sistemas confiáveis. Pode-se resumir que um sistema é confiável, ao ser capaz de detectar as situações anormais, de modo precoce, e, ao mesmo tempo, de minimizar a geração de alarmes falsos. Ao se ter um sistema confiável, pode-se empregá-lo para auxiliar o operador, de fábricas, no processo de tomada de decisões. O objetivo deste estudo é apresentar uma metodologia, baseada na técnica, modelo oculto de Markov (HMM, acrônimo de ?Hidden Markov Model?), para se detectar situações anormais em caldeiras de recuperação química. As aplicações de maior sucesso de HMM são na área de reconhecimento de fala. Pode-se citar como aspectos positivos: o raciocínio probabilístico, a modelagem explícita, e a identificação a partir de dados históricos. Fez-se duas aplicações. O primeiro estudo de caso é no ?benchmark? de um sistema de evaporação múltiplo efeito de uma fábrica de produção de açúcar. Identificou-se um HMM, característico de operação normal, para se detectar cinco situações anormais no atuador responsável por regular o fluxo de xarope de açúcar para o primeiro evaporador. A detecção, para as três situações abruptas, é imediata, uma vez que o HMM foi capaz de detectar alterações, abruptas, no sinal da variável monitorada. Em relação às duas situações incipientes, foi possível detectá-las ainda em estágio inicial; ao ser o valor de f (vetor responsável por representar a intensidade de um evento anormal, com o tempo), no instante da detecção, próximo a zero, igual a 2,8% e 2,1%, respectivamente. O segundo estudo de caso é em uma caldeira de recuperação química, de uma fábrica de produção de celulose, no Brasil. O objetivo é monitorar o acúmulo de depósitos de cinzas sobre os equipamentos da sessão de transferência de calor convectivo, através de medições de perda de carga. Este é um dos principais desafios para se aumentar a eficiência operacional deste equipamento. Após a identificação de um HMM característico de perda de carga alta, pôde-se verificar a sua capacidade de informar o estado atual e, por consequência, a tendência do sistema, de modo similar à um preditor. Pôde-se demonstrar também a utilidade de se definir limites de controle, com o objetivo de se ter a informação sobre a distância entre o estado atual e os níveis de alarme de perda de carga. / The greatest challenge faced by the area of process monitoring in chemical industries still resides in the fault detection task, which aims at developing reliable systems. One may say that a system is reliable if it is able to perform early fault detection and, at the same time, to reduce the generation of false alarms. Once there is a reliable system available, it can be employed to help operators, in factories, in the decisionmaking process. The aim of this study is presenting a methodology, based on the Hidden Markov Model (HMM) technique, suggesting its use in the detection of abnormal situations in chemical recovery boilers. The most successful applications of HMM are in the area of speech recognition. Some of its advantages are: probabilistic reasoning, explicit modeling and the identification based on process history data. This study discusses two applications. The first one is on a benchmark of a multiple evaporation system in a sugar factory. A HMM representative of the normal operation was identified, in order to detect five abnormal situations at the actuator responsible for controlling the syrup flow to the first evaporator. The detection result for the three abrupt situations was immediate, since the HMM was capable of detecting the statistical changes on the signal of the monitored variable as soon as they occurred. Regarding to the two incipient situations, the detection was done at an early stage. For both events, the value of vector f (responsible for representing the strength of an abnormal event over time), at the time it occurred, was near zero, equal to 2.8 and 2.1%, respectively. The second case study deals with the application of HMM in a chemical recovery boiler, belonging to a cellulose mill, in Brazil. The aim is monitoring the accumulation of ash deposits over the equipments of the convective heat transfer section, through pressure drop measures. This is one of the main challenges to be overcome nowadays, bearing in mind the interest that exists in increasing the operational efficiency of this equipment. Initially, a HMM for high values of pressure drop was identified. With this model, it was possible to check its capacity to inform the current state, and consequently, the tendency of the system (similarly as a predictor). It was also possible to show the utility of defining control limits, in order to inform the operator the relative distance between the current state of the system and the alarm levels of pressure drop.

Factory shop floors of the future will see a significant increase in interconnected devices for monitoring and control. However, if a Service Orientated Architecture (SOA) is implemented on all such devices then this will result in a large number of permutations of services and composite services. These services combined with other business level components can pose a huge challenge to manage as it is often difficult to keep an overview of all the devices, equipment and services. This thesis proposes an SOA based novel assimilation architecture for integrating disparate industrial hardware based processes and business processes of an enterprise in particular the plastics machinery environment. The key benefits of the proposed architecture are the reduction of complexity when integrating disparate hardware platforms; managing the associated services as well as allowing the Micro Injection Moulding (µIM) process to be monitored on the web through service and data integration. An Enterprise Service Bus (ESB) based middleware layer integrates the Wireless Sensor Network (WSN) based environmental and simulated machine process systems with frontend Google Gadgets (GGs) based web visualisation applications. A business process framework is proposed to manage and orchestrate the resulting services from the architecture. Results from the analysis of the WSN kits in terms of their usability and reliability showed that the Jennic WSN was easy to setup and had a reliable communication link in the polymer industrial environment with the PER being below 0.5%. The prototype Jennic WSN based µIM process monitoring system had limitations when monitoring high-resolution machine data, therefore a novel hybrid integration architecture was proposed. The assimilation architecture was implemented on a distributed server based test bed. Results from test scenarios showed that the architecture was highly scalable and could potentially allow a large number of disparate sensor based hardware systems and services to be hosted, managed, visualised and linked to form a cohesive business process.