On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks.

Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detection in multi-sensor networks rely on models which are extracted from multi-sensor measurements and which characterize the anomaly-free reference situation. Therefore, significant deviations to these models indicate potential anomalies. In this paper, we propose a new approach which is based on causal relation networks (CRNs) that represent the inner causes and effects between sensor channels (or sensor nodes) in form of partial sub-relations, and evaluate its functionality and performance on two distinct production phases within a micro-fluidic chip manufacturing scenario. The partial relations are modeled by non-linear (fuzzy) regression models for characterizing the (local) degree of influences of the single causes on the effects. An advanced analysis of the multi-variate residual signals, obtained from the partial relations in the CRNs, is conducted. It employs independent component analysis (ICA) to characterize hidden structures in the fused residuals through independent components (latent variables) as obtained through the demixing matrix. A significant change in the energy content of latent variables, detected through automated control limits, indicates an anomaly. Suppression of possible noise content in residuals—to decrease the likelihood of false alarms—is achieved by performing the residual analysis solely on the dominant parts of the demixing matrix. Our approach could detect anomalies in the process which caused bad quality chips (with the occurrence of malfunctions) with negligible delay based on the process data recorded by multiple sensors in two production phases: injection molding and bonding, which are independently carried out with completely different process parameter settings and on different machines (hence, can be seen as two distinct use cases). Our approach furthermore i.) produced lower false alarm rates than several related and well-known state-of-the-art methods for (unsupervised) anomaly detection, and ii.) also caused much lower parametrization efforts (in fact, none at all). Both aspects are essential for the useability of an anomaly detection approach

An Overview of the Measurement of Permeability of Composite Reinforcements

Liquid composite molding (LCM) is a class of fast and cheap processes suitable for the fabrication of large parts with good geometrical and mechanical properties. One of the main steps in an LCM process is represented by the filling stage, during which a reinforcing fiber preform is impregnated with a low-viscosity resin. Darcy’s permeability is the key property for the filling stage, not usually available and depending on several factors. Permeability is also essential in computational modeling to reduce costly trial-and-error procedures during composite manufacturing. This review aims to present the most used and recent methods for permeability measurement. Several solutions, introduced to monitor resin flow within the preform and to calculate the in-plane and out-of-plane permeability, will be presented. Finally, the new trends toward reliable methods based mainly on non-invasive and possibly integrated sensors will be described

Fabrication and optimisation of a fused filament 3D-printed microfluidic platform

A 3D-printed microfluidic device was designed and manufactured using a low cost ($2000) consumer grade fusion deposition modelling (FDM) 3D printer. FDM printers are not typically used, or are capable, of producing the fine detailed structures required for microfluidic fabrication. However, in this work, the optical transparency of the device was improved through manufacture optimisation to such a point that optical colorimetric assays can be performed in a 50 µl device. A colorimetric enzymatic cascade assay was optimised using glucose oxidase and horseradish peroxidase for the oxidative coupling of aminoantipyrine and chromotropic acid to produce a blue quinoneimine dye with a broad absorbance peaking at 590 nm for the quantification of glucose in solution. For comparison the assay was run in standard 96 well plates with a commercial plate reader. The results show the accurate and reproducible quantification of 0–10 mM glucose solution using a 3D-printed microfluidic optical device with performance comparable to that of a plate reader assay

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an end-to-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem

Remanufacturing and Advanced Machining Processes for New Materials and Components

"Remanufacturing and Advanced Machining Processes for Materials and Components presents current and emerging techniques for machining of new materials and restoration of components, as well as surface engineering methods aimed at prolonging the life of industrial systems. It examines contemporary machining processes for new materials, methods of protection and restoration of components, and smart machining processes. • Details a variety of advanced machining processes, new materials joining techniques, and methods to increase machining accuracy • Presents innovative methods for protection and restoration of components primarily from the perspective of remanufacturing and protective surface engineering • Discusses smart machining processes, including computer-integrated manufacturing and rapid prototyping, and smart materials • Provides a comprehensive summary of state-of-the-art in every section and a description of manufacturing methods • Describes the applications in recovery and enhancing purposes and identifies contemporary trends in industrial practice, emphasizing resource savings and performance prolongation for components and engineering systems The book is aimed at a range of readers, including graduate-level students, researchers, and engineers in mechanical, materials, and manufacturing engineering, especially those focused on resource savings, renovation, and failure prevention of components in engineering systems.

A review of emerging technologies enabling improved solid oral dosage form manufacturing and processing

Tablets are the most widely utilized solid oral dosage forms because of the advantages of self-administration, stability, ease of handling, transportation, and good patient compliance. Over time, extensive advances have been made in tableting technology. This review aims to provide an insight about the advances in tablet excipients, manufacturing, analytical techniques and deployment of Quality by Design (QbD). Various excipients offering novel functionalities such as solubility enhancement, super-disintegration, taste masking and drug release modifications have been developed. Furthermore, co-processed multifunctional ready-to-use excipients, particularly for tablet dosage forms, have benefitted manufacturing with shorter processing times. Advances in granulation methods, including moist, thermal adhesion, steam, melt, freeze, foam, reverse wet and pneumatic dry granulation, have been proposed to improve product and process performance. Furthermore, methods for particle engineering including hot melt extrusion, extrusion-spheronization, injection molding, spray drying / congealing, co-precipitation and nanotechnology-based approaches have been employed to produce robust tablet formulations. A wide range of tableting technologies including rapidly disintegrating, matrix, tablet-in-tablet, tablet-in-capsule, multilayer tablets and multiparticulate systems have been developed to achieve customized formulation performance. In addition to conventional invasive characterization methods, novel techniques based on laser, tomography, fluorescence, spectroscopy and acoustic approaches have been developed to assess the physical-mechanical attributes of tablet formulations in a non- or minimally invasive manner. Conventional UV-Visible spectroscopy method has been improved (e.g., fiber-optic probes and UV imaging-based approaches) to efficiently record the dissolution profile of tablet formulations. Numerous modifications in tableting presses have also been made to aid machine product changeover, cleaning, and enhance efficiency and productivity. Various process analytical technologies have been employed to track the formulation properties and critical process parameters. These advances will contribute to a strategy for robust tablet dosage forms with excellent performance attributes

Design and Testing of a Composite Compressor Rotor

Additive manufacturing (AM) and molding are manufacturing methods known for building representations or replicas of conceptualized engine components, but was considered impractical for manufacturing operating engine components. More recent technology has rendered composite materials (combining high-temperature polymers and fiber reinforcement) capable of withstanding the temperature and structural requirements to compete with conventional turbomachinery metals. This study explores the application of several high-temperature polymers (ULTEM 9085, Onyx-Carbon fiber, and Epoxy-Carbon fiber) and their survivability in the operating conditions of a P400 Engine compressor. The tests conducted for this study determined their viability as compressor materials. This study required conducting tensile specimen testing, FEA modeling, and physical compressor spin testing. The results of each will be discussed