A new paradigm: cloud agile manufacturing

Cloud Agile Manufacturing is a new paradigm proposed in this article. The main objective of Cloud Agile Manufacturing is to offer industrial production systems as a service. Thus users can access any functionality available in the cloud of manufacturing (process design, production, management, business integration, factories virtualization, etc.) without knowledge — or at least without having to be experts — in managing the required resources. The proposal takes advantage of many of the benefits that can offer technologies and models like: Business Process Management (BPM), Cloud Computing, Service Oriented Architectures (SOA) and Ontologies. To develop the proposal has been taken as a starting point the Semantic Industrial Machinery as a Service (SIMaaS) proposed in previous work. This proposal facilitates the effective integration of industrial machinery in a computing environment, offering it as a network service. The work also includes an analysis of the benefits and disadvantages of the proposal.

Cloud agile manufacturing

This paper proposes a new manufacturing paradigm, we call Cloud Agile Manufacturing, and whose principal objective is to offer industrial production systems as a service. Thus users can access any functionality available in the cloud of manufacturing (process design, production, management, business integration, factories virtualization, etc.) without knowledge — or at least without having to be experts — in managing the required resources. The proposal takes advantage of many of the benefits that can offer technologies and models like: Business Process Management (BPM), Cloud Computing, Service Oriented Architectures (SOA) and Ontologies. To develop the proposal has been taken as a starting point the Semantic Industrial Machinery as a Service (SIMaaS) proposed in previous work. This proposal facilitates the effective integration of industrial machinery in a computing environment, offering it as a network service. The work also includes an analysis of the benefits and disadvantages of the proposal.

Dynamic investigation of an ancient masonry bell tower with operational modal analysis: a non-destructive experimental technique to obtain the dynamic characteristics of a structure

This paper shows the results of an experimental analysis on the bell tower of “Chiesa della Maddalena” (Mola di Bari, Italy), to better understand the structural behavior of slender masonry structures. The research aims to calibrate a numerical model by means of the Operational Modal Analysis (OMA) method. In this way realistic conclusions about the dynamic behavior of the structure are obtained. The choice of using an OMA derives from the necessity to know the modal parameters of a structure with a non-destructive testing, especially in case of cultural-historical value structures. Therefore by means of an easy and accurate process, it is possible to acquire in-situ environmental vibrations. The data collected are very important to estimate the mode shapes, the natural frequencies and the damping ratios of the structure. To analyze the data obtained from the monitoring, the Peak Picking method has been applied to the Fast Fourier Transforms (FFT) of the signals in order to identify the values of the effective natural frequencies and damping factors of the structure. The main frequencies and the damping ratios have been determined from measurements at some relevant locations. The responses have been then extrapolated and extended to the entire tower through a 3-D Finite Element Model. In this way, knowing the modes of vibration, it has been possible to understand the overall dynamic behavior of the structure.

3D-based reconstruction using growing neural gas landmark: application to rapid prototyping in shoe last manufacturing

Customizing shoe manufacturing is one of the great challenges in the footwear industry. It is a production model change where design adopts not only the main role, but also the main bottleneck. It is therefore necessary to accelerate this process by improving the accuracy of current methods. Rapid prototyping techniques are based on the reuse of manufactured footwear lasts so that they can be modified with CAD systems leading rapidly to new shoe models. In this work, we present a shoe last fast reconstruction method that fits current design and manufacturing processes. The method is based on the scanning of shoe last obtaining sections and establishing a fixed number of landmarks onto those sections to reconstruct the shoe last 3D surface. Automated landmark extraction is accomplished through the use of the self-organizing network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates up to 12 times the surface reconstruction and filtering processes used by the current shoe last design software. The proposed method offers higher accuracy compared with methods with similar efficiency as voxel grid.