Machines

5440 KiB

Open AccessArticle

A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages

by Gorka Urbikain, Alvaro Alvarez, Luis Norberto López de Lacalle, Mikel Arsuaga, Miguel A. Alonso and Fernando Veiga

Machines 2017, 5(2), 15; https://doi.org/10.3390/machines5020015 - 2 May 2017

Cited by 26 | Viewed by 6855

Abstract

The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy, and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on the equations describing the physical laws of the machining processes; however, additional efforts are needed to overcome the gap between scientific research and real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep-knowledge and models” that aid machine designers, production engineers, or machinists to get the most out of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system and process as input data, and generates results that help in the proper decision-making and machining plan. Direct benefits can be found in (a) the fixture/clamping optimal design; (b) the machine tool configuration; (c) the definition of chatter-free optimum cutting conditions and (d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper. Full article

(This article belongs to the Special Issue Advances in Process Machine Interactions)

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4247 KiB

Open AccessFeature PaperArticle

Automated Cable Preparation for Robotized Stator Cable Winding

by Erik Hultman and Mats Leijon

Machines 2017, 5(2), 14; https://doi.org/10.3390/machines5020014 - 18 Apr 2017

Cited by 7 | Viewed by 6641

Abstract

A method for robotized cable winding of the Uppsala University Wave Energy Converter generator stator has previously been presented and validated. The purpose of this study is to present and validate further developments to the method: automated stand-alone equipment for the preparation of the winding cables. The cable preparation consists of three parts: feeding the cable from a drum, forming the cable end and cutting the cable. Forming and cutting the cable was previously done manually and only small cable drums could be handled. Therefore the robot cell needed to be stopped frequently. The new equipment was tested in an experimental robot stator cable winding setup. Through the experiments, the equipment was validated to be able to perform fully automated and robust cable preparation. Suggestions are also given on how to further develop the equipment with regards to performance, robustness and quality. Hence, this work represents another important step towards demonstrating completely automated robotized stator cable winding. Full article

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2263 KiB

Open AccessArticle

Dynamic Simulation of the Harvester Boom Cylinder

by Rongfeng Shen, Xiaozhen Zhang and Chengjun Zhou

Machines 2017, 5(2), 13; https://doi.org/10.3390/machines5020013 - 17 Apr 2017

Cited by 4 | Viewed by 6049

Abstract

Based on the complete dynamic calculation method, the layout, force, and strength of harvester boom cylinders were designed and calculated. Closed simulations for the determination of the dynamic responses of the harvester boom during luffing motion considering the cylinder drive system and luffing angle position control have been realized. Using the ADAMS mechanical system dynamics analysis software, six different arm poses were selected and simulated based on the cylinder as the analysis object. A flexible model of the harvester boom luffing motion has been established. The movement of the oil cylinder under different conditions were analyzed, and the main operation dimensions of the harvester boom and the force condition of the oil cylinder were obtained. The calculation results show that the dynamic responses of the boom are more sensitive to the luffing acceleration, in comparison with the luffing velocity. It is seen that this method is very effective and convenient for boom luffing simulation. It is also reasonable to see that the extension of the distance of the bottom of the boom is shortened by adjusting the initial state of the boom in the working process, which can also effectively reduce the workload of the boom—thus improving the mechanical efficiency. Full article

(This article belongs to the Special Issue Mechatronics: Intelligent Machines)

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1979 KiB

Open AccessArticle

Concepts for 3D Printing-Based Self-Replicating Robot Command and Coordination Techniques

by Andrew Jones and Jeremy Straub

Machines 2017, 5(2), 12; https://doi.org/10.3390/machines5020012 - 6 Apr 2017

Cited by 11 | Viewed by 6386

Abstract

Self-replicating robots represent a new area for prospective advancement in robotics. A self-replicating robot can identify when additional robots are needed to solve a problem or meet user needs, and create them in response to this identified need. This allows robotic systems to respond to changing (or non-predicted) mission needs. Being able to modify the physical system component provides an additional tool for optimizing robotic system performance. This paper begins the process of developing a command and coordination system that makes decisions with the consideration of replication, repair, and retooling capabilities. A high-level algorithm is proposed and qualitatively assessed. Full article

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4917 KiB

Open AccessArticle

Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings

by Wahyu Caesarendra, Tegoeh Tjahjowidodo, Buyung Kosasih and Anh Kiet Tieu

Machines 2017, 5(2), 11; https://doi.org/10.3390/machines5020011 - 4 Apr 2017

Cited by 23 | Viewed by 6052

Abstract

This paper presents an application of multivariate state estimation technique (MSET), sequential probability ratio test (SPRT) and kernel regression for low speed slew bearing condition monitoring and prognosis. The method is applied in two steps. Step (1) is the detection of the incipient slew bearing defect. In this step, combined MSET and SPRT is used with circular-domain kurtosis, time-domain kurtosis, wavelet decomposition (WD) kurtosis, empirical mode decomposition (EMD) kurtosis and the largest Lyapunov exponent (LLE) feature. Step (2) is the prediction of the selected features’ trends and the estimation of the remaining useful life (RUL) of the slew bearing. In this step, kernel regression is used with time-domain kurtosis, WD kurtosis and the LLE feature. The application of the method is demonstrated with laboratory slew bearing acceleration data. Full article

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Machines, Volume 5, Issue 2 (June 2017) – 5 articles

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