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Published in Min Basadur, Michael Goldsby, Rob Mathews, Design-Centered Entrepreneurship, 2022
Min Basadur, Michael Goldsby, Rob Mathews
A basic understanding of machines may come in handy as you develop prototypes. The Naval Education and Training Program Development Center defines a machine as any device that helps you to do the work. We use machines to transform energy. Another use is to multiply force. Machines may also be used to multiply speed. There are only six simple machines: the lever, block, wheel, axle, inclined plane, screw, and gear. When you are familiar with the principles of these simple machines, you can work on better understanding the operation of complex machines. Complex machines are merely combinations of two or more simple machines.27A little understanding of technology, machinery, and power can provide you with a toolkit to better move an idea further. Of course, you will want to work with technical experts, and you will have a better interaction with them if you understand some basics about machinery.
Mechanical principles of dynamic engineering systems
Published in Alan Darbyshire, Charles Gibson, Mechanical Engineering, 2023
Alan Darbyshire, Charles Gibson
A simple machine is an arrangement of moving parts whose purpose is to transmit motion and force. The ones which we will consider are those in which a relatively small input force is used to raise a heavy load. They include lever systems, inclined planes, screw jacks, wheel and axle arrangements and gear trains (Figure 2.62).
Fundamentals of Electronics and Mechanics
Published in Ferat Sahin, Pushkin Kachroo, Practical and Experimental Robotics, 2017
Here Din is the distance traveled by the effort and Dout is the distance traveled by the load. The mechanical advantage (M.A.) of a simple machine is defined as the ratio of the load to effort force.
Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing
Published in Virtual and Physical Prototyping, 2023
Benjamin Bevans, Christopher Barrett, Thomas Spears, Aniruddha Gaikwad, Alex Riensche, Ziyad Smoqi, Harold (Scott) Halliday, Prahalada Rao
Gaikwad et al. (2022) monitored the meltpool using a system of coaxial high-speed cameras. They subsequently extracted physically intuitive shape, spatter and temperature distribution characteristics of the meltpool and correlated these sensor signatures to the type and severity of pore formation. They used simple machine learning approaches, such as k-nearest neighbours (KNN) and support vector machine (SVM) to make these signature-porosity correlations. They further compared the prediction fidelity of such simple machine learning techniques to complex black-box deep machine learning algorithms. Gaikwad et al. (2022) reported that a set of physically intuitive process signatures, when combined with simple machine learning models, were found to outperform complex deep learning models that directly used the sensor data without decomposing the sensor signals into process signatures. Similar results affirming the effectiveness of leveraging low-level, yet physically interpretable, process signatures with simple machine learning models are evident in recent works by other researchers (Gaikwad et al. 2020a; Smoqi et al. 2022).
Analytical solutions for two-dimensional piezoelectric quasicrystal composite wedges and spaces
Published in Mechanics of Advanced Materials and Structures, 2023
Xiang Mu, Zhiming Hu, Zhaowei Zhu, Jinming Zhang, Yang Li, Liangliang Zhang, Yang Gao
The wedge is a piece of material with V-shaped edges. As a simple machine, considerable research efforts have been devoted to the wedge. Hill et al. [21] obtained analytical solutions for the deformation of rigid frictionless wedge penetrating plastic materials. Liu and Chue [22] investigated singularities of the bi-material magnetoelectric elastic composite wedge under the anti-plane deformation. Xu and Rajapakse [23] investigated the singularity of piezoelectric composite wedges and junctions by extending Lekhnitskii's complex potential functions, and found the electric boundary conditions have an obvious effect on singularity orders. Hwu and Ting [24] discussed solutions for the general anisotropic wedge at critical wedge angles. Chen [25] explored stress singularities in anisotropic multi-material wedges and junctions, and got the singular orders by solving the eigen equation and demonstrated the effect of geometric structure and material properties on singular orders.
Energy absorption prediction and optimization of corrugation-reinforced multicell square tubes based on machine learning
Published in Mechanics of Advanced Materials and Structures, 2022
Zhixiang Li, Wen Ma, Huifen Zhu, Gongxun Deng, Lin Hou, Ping Xu, Shuguang Yao
In the present study, the criteria SEA, PCF and deformation mode were used to evaluate the energy absorption performance of the CMST. Machine learning techniques were used to predict these responses because they can deal with both numerical variables (i.e., SEA and PCF) and categorical variables (i.e., deformation mode), which cannot be achieved by traditional fitting methods. There are many machine learning models, including some simple models such as K-nearest neighbor (KNN) [29], random forest (RF) [30], etc., and some complex models such as Light Gradient Moosting Machine (LightGBM) [31] and eXtreme Gradient Boosting (XGBoost) [32], etc. The classification and regression problems in the present study were relatively simple engineering problems. In addition, our aim was to establish an easy to use method in engineering application. Therefore, some simple machine learning models were chosen. These machine learning methods were KNN, RF, support vector machine (SVM) [33] and artificial neural network (ANN) [34]. The four models involve less parameters, which reduces the difficulty of using the models. Moreover, the four machine learning algorithms are widely used for regression and classification, and therefore they can deal with both numerical and categorical responses [35].