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Machines, Volume 6, Issue 1 (March 2018)

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Editorial

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Open AccessFeature PaperEditorial Acknowledgement to Reviewers of Machines in 2017
Machines 2018, 6(1), 3; doi:10.3390/machines6010003 (registering DOI)
Received: 16 January 2018 / Revised: 16 January 2018 / Accepted: 16 January 2018 / Published: 16 January 2018
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Abstract
The editors of Machines would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2017.[...] Full article

Research

Jump to: Editorial

Open AccessFeature PaperArticle A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements
Machines 2018, 6(1), 1; doi:10.3390/machines6010001
Received: 22 December 2017 / Revised: 2 January 2018 / Accepted: 4 January 2018 / Published: 5 January 2018
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Abstract
For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using
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For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using sensors are often related to the costs associated with them and the inaccessibility of critical locations within machines or processes. Manufacturing processes, as a large group of applications in which data analytics can bring significant value to, are the focus of this study. As the cost of instrumenting the machines in a manufacturing process is significant, an alternative solution which relies solely on product quality measurements is greatly desirable in the manufacturing industry. In this paper, a minimal-sensing framework for machine anomaly detection in multistage manufacturing processes based on product quality measurements is introduced. This framework, which relies on product quality data along with products’ manufacturing routes, allows the detection of variations in the quality of the products and is able to pinpoint the machine which is the cause of anomaly. A moving window is applied to the data, and a statistical metric is extracted by comparing the performance of a machine to its peers. This approach is expanded to work for multistage processes. The proposed method is validated using a dataset from a real-world manufacturing process and additional simulated datasets. Moreover, an alternative approach based on Bayesian Networks is provided and the performance of the two proposed methods is evaluated from an industrial implementation perspective. The results showed that the proposed similarity-based approach was able to successfully identify the root cause of the quality variations and pinpoint the machine that adversely impacted the product quality. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Open AccessArticle A Methodology for the Lightweight Design of Modern Transfer Machine Tools
Machines 2018, 6(1), 2; doi:10.3390/machines6010002
Received: 10 December 2017 / Revised: 7 January 2018 / Accepted: 11 January 2018 / Published: 14 January 2018
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Abstract
This paper deals with a modern design approach via finite elements in the definition of the main structural elements (rotary table and working unit) of an innovative family of transfer machine tools. Using the concepts of green design and manufacture, as well as
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This paper deals with a modern design approach via finite elements in the definition of the main structural elements (rotary table and working unit) of an innovative family of transfer machine tools. Using the concepts of green design and manufacture, as well as sustainable development thinking, the paper highlights the advantages derived from their application in this specific field (i.e., the clever use of lightweight materials to allow ruling out high-consumption hydraulic pump systems). The design is conceived in a modular way, so that the final solution can cover transfers from four to 15 working stations. Two versions of the machines are examined. The first one has a rotary table with nine divisions, which can be considered as a prototype: this machine has been studied in order to set up the numerical predictive model, then validated by experimental tests. The second one, equipped with a rotary table with 15 divisions, is the biggest of the range: this machine has been entirely designed with the aid of the previously developed numerical model. The loading input forces for the analyses have been evaluated experimentally via drilling operations carried out on a three-axis CNC unit. The definition of the design force made it possible to accurately assess both the rotary table and the working units installed in the machine. Full article
(This article belongs to the Special Issue Precision Machining)
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