Design of ultraprecision machine tools with application to manufacturing of miniature and micro components

Currently the underlying necessities for predictability, producibility and productivity remain big issues in ultraprecision machining of miniature/microproducts. The demand on rapid and economic fabrication of miniature/microproducts with complex shapes has also made new challenges for ultraprecision machine tool design. In this paper the design for an ultraprecision machine tool is introduced by describing its key machine elements and machine tool design procedures. The focus is on the review and assessment of the state-of-the-art ultraprecision machining tools. It also illustrates the application promise of miniature/microproducts. The trends on machine tool development, tooling, workpiece material and machining processes are pointed out

Using Remote Access for Sharing Experiences in a Machine Design Laboratory

A new Machine Design Laboratory at Marquette University has been created to foster student exploration and promote “hands-on” and “minds-on” learning. Laboratory experiments have been developed to give students practical experiences and expose them to physical hardware, actual tools, and design challenges. Students face a range of real-world tasks: identify and select components, measure parameters (dimensions, speed, force), distinguish between normal and used (worn) components and between proper and abnormal behavior, reverse engineer systems, and justify design choices. The experiments serve to motivate the theory, spark interest, and promote discovery learning in the subject of machine design. This paper presents details of the experiments in the Machine Design Laboratory and then explores the feasibility of sharing some of the experiences with students at other institutions through remote access technologies. The paper proposes steps towards achieving this goal and raises issues to be addressed for a pilot-study offering machine design experiences to students globally who have access to the internet

Discovery Learning Experiments in a New Machine Design Laboratory

A new Machine Design Laboratory at Marquette University has been created to foster student exploration with hardware and real-world systems. The Laboratory incorporates areas for teaching and training, and has been designed to promote “hands-on” and “minds-on” learning. It reflects the spirit of transformational learning that is a theme in the College of Engineering. The goal was to create discovery learning oriented experiments for a required junior-level “Design of Machine Elements” course in mechanical engineering that would give students practical experiences and expose them to physical hardware, actual tools, and real-world design challenges. In the experiments students face a range of real-world tasks: identify and select components, measure parameters (dimensions, speed, force), distinguish between normal and used (worn) components and between proper and abnormal behavior, reverse engineer systems, and justify design choices. The experiments serve to motivate the theory and spark interest in the subject of machine design. This paper presents details of the experiments and summarizes student reactions and our experiences in the Machine Design Laboratory. In addition, the paper provides some insights for others who may wish to develop similar types of experiments

Design Of Automatic Stamping Machine For Date And Dash Code Marking Using Pneumatic System And Plc Controller

The research is meant to design an automatic machine for stamping the date and dash code on the master carton for the packaging of the blister-pack. The quantity of existing manual process in producing the master carton does not meet with the demands, therefore, it needs a machine that improve the production process with the following characteristic: fast, easy to use and maintain, and also affordable in order to support the cost saving program. The design uses pneumatic system with PLC controller to automate the process

XNMT: The eXtensible Neural Machine Translation Toolkit

This paper describes XNMT, the eXtensible Neural Machine Translation toolkit. XNMT distin- guishes itself from other open-source NMT toolkits by its focus on modular code design, with the purpose of enabling fast iteration in research and replicable, reliable results. In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing. XNMT is available open-source at https://github.com/neulab/xnmtComment: To be presented at AMTA 2018 Open Source Software Showcas

Design of an electrochemical micromachining machine

Electrochemical micromachining (μECM) is a non-conventional machining process based on the phenomenon of electrolysis. μECM became an attractive area of research due to the fact that this process does not create any defective layer after machining and that there is a growing demand for better surface integrity on different micro applications including microfluidics systems, stress-free drilled holes in automotive and aerospace manufacturing with complex shapes, etc. This work presents the design of a next generation μECM machine for the automotive, aerospace, medical and metrology sectors. It has three axes of motion (X, Y, Z) and a spindle allowing the tool-electrode to rotate during machining. The linear slides for each axis use air bearings with linear DC brushless motors and 2-nm resolution encoders for ultra precise motion. The control system is based on the Power PMAC motion controller from Delta Tau. The electrolyte tank is located at the rear of the machine and allows the electrolyte to be changed quickly. This machine features two process control algorithms: fuzzy logic control and adaptive feed rate. A self-developed pulse generator has been mounted and interfaced with the machine and a wire ECM grinding device has been added. The pulse generator has the possibility to reverse the pulse polarity for on-line tool fabrication.The research reported in this paper is supported by the European Commission within the project “Minimizing Defects in Micro-Manufacturing Applications (MIDEMMA)” (FP7-2011-NMPICT- FoF-285614)

Mechanism design for decentralized online machine scheduling

Traditional optimization models assume a central decision maker who optimizes a global system performance measure. However, problem data is often distributed among several agents, and agents take autonomous decisions. This gives incentives for strategic behavior of agents, possibly leading to sub-optimal system performance. Furthermore, in dynamic environments, machines are locally dispersed and administratively independent. Examples are found both in business and engineering applications. We investigate such issues for a parallel machine scheduling model where jobs arrive online over time. Instead of centrally assigning jobs to machines, each machine implements a local sequencing rule and jobs decide for machines themselves. In this context, we introduce the concept of a myopic best response equilibrium, a concept weaker than the classical dominant strategy equilibrium, but appropriate for online problems. Our main result is a polynomial time, online mechanism that |assuming rational behavior of jobs| results in an equilibrium schedule that is 3.281-competitive with respect to the maximal social welfare. This is only lightly worse than state-of-the-art algorithms with central coordination