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Moshe Shpitalni

Researcher at Technion – Israel Institute of Technology

Publications -  72
Citations -  4297

Moshe Shpitalni is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Product design & Constructive solid geometry. The author has an hindex of 26, co-authored 72 publications receiving 3865 citations. Previous affiliations of Moshe Shpitalni include University of Michigan.

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Design of reconfigurable manufacturing systems

TL;DR: The rationale for the development of reconfigurable manufacturing systems, which possess the advantages both of dedicated lines and of flexible systems, has been explained in this article, and a rigorous mathematical method is introduced for designing RMS with this recommended structure.
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Assembly system design and operations for product variety

TL;DR: A review of the state of the art research in the areas of assembly system design, planning and operations in the presence of product variety is presented in this article, where methods for assembly representation, sequence generation and assembly line balancing are reviewed and summarized.
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Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection

TL;DR: This contribution investigates a new paradigm from machine learning, namely deep machine learning by examining design configurations of deep Convolutional Neural Networks and the impact of different hyper-parameter settings towards the accuracy of defect detection results.
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Maintenance: Changing role in life cycle management

TL;DR: In this paper, the authors present a maintenance framework that shows management cycles of maintenance activities during the product life cycle, identifying technical issues of maintenance and discuss the advances of technologies supporting the change in the role of maintenance.
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Optimization-based reconstruction of a 3D object from a single freehand line drawing

TL;DR: This paper describes an optimization-based algorithm for reconstructing a 3D model from a single, inaccurate, 2D edge-vertex graph, which serves as input for the reconstruction process, and examples from a working implementation are given.