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Abstract

The article describes a test stand with a spindle equipped with an active bearing preload system using piezoelectric actuators. The proper functioning of the spindle and the active system was associated with the correct alignment of the spindle shaft and the drive motor. The article presents two methods of shaft alignment. The use of commonly known shaft alignment methods with dial indicators is insufficient from the viewpoint of being able to control this preload. This work aims at making the readers aware that, for systems with active bearing preload, the latest measuring devices should be used to align the shaft. The use of commonly known methods of equalization with dial gauges is insufficient from the point of view of controlling this preload. Increasing the accuracy of shaft alignment from 0.1 to 0.01 mm made it possible to obtain a 50% reduction in the displacement of the outer bearing ring during spindle operation.

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Authors and Affiliations

Paweł Turek
1
Marek Stembalski
1

  1. Wrocław University of Science and Technology, Faculty of Mechanical Engineering, Wrocław, Poland.
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Abstract

In manufacturing industries, the selection of machine parameters is a very complicated task in a time-bound manner. The process parameters play a primary role in confirming the quality, low cost of manufacturing, high productivity, and provide the source for sustainable machining. This paper explores the milling behavior of MWCNT/epoxy nanocomposites to attain the parametric conditions having lower surface roughness (Ra) and higher materials removal rate (MRR). Milling is considered as an indispensable process employed to acquire highly accurate and precise slots. Particle swarm optimization (PSO) is very trendy among the nature-stimulated metaheuristic method used for the optimization of varying constraints. This article uses the non-dominated PSO algorithm to optimize the milling parameters, namely, MWCNT weight% (Wt.), spindle speed (N), feed rate (F), and depth of cut (D). The first setting confirmatory test demonstrates the value of Ra and MRR that are found as 1:62 μm and 5.69 mm3/min, respectively and for the second set, the obtained values of Ra and MRR are 3.74 μm and 22.83 mm3/min respectively. The Pareto set allows the manufacturer to determine the optimal setting depending on their application need. The outcomes of the proposed algorithm offer new criteria to control the milling parameters for high efficiency.

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Authors and Affiliations

Prakhar Kumar Kharwar
1
Rajesh Kumar Verma
1
Nirmal Kumar Mandal
2
Arpan Kumar Mondal
2

  1. Department of Mechanical Engineering, Madan Mohan Malaviya University of Technology Gorakhpur, India.
  2. Department of Mechanical Engineering, National Institute of Technical Teachers’ Training and Research, Kolkata, India.
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Abstract

This paper presents a new algorithm that approximates the forces that develop between a human hand and the handles of a climbing wall. A hand-to-handle model was developed using this algorithm for the Open Dynamics Engine physics solver, which can be plugged into a full-body climbing simulation to improve results. The model data are based on biomechanical measurements of the average population presented in previously published research. The main objective of this work was to identify maximum forces given hand orientation and force direction with respect to the climbing wall handles. Stated as a nonlinear programming problem, solution was achieved by applying a stochastic Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The algorithm for force approximation works consistently and provides reasonable results when gravity is neglected. However, including gravity results in a number of issues. Since the weight of the hand is small in relation to the hand-to-handle forces, neglecting gravity does not significantly affect the reliability and quality of the solution.

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Authors and Affiliations

Grzegorz Orzechowski
1 2
Perttu Hämäläinen
3
Aki Mikkola
1

  1. Department of Mechanical Engineering, LUT University, Lappeenranta, Finland.
  2. Mevea Ltd., Lappeenranta, Finland.
  3. Department of Computer Science, Aalto University, Espoo, Finland.

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