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Automation in Manufacturing
Published in Edward Y. Uechi, Business Automation and Its Effect on the Labor Force, 2023
Additive manufacturing (also known as 3D printing) is an innovative process in which an object can be made layer by layer with such high degree of precision that allows the object to conform to hitherto new geometric shapes. The material used to make the object can be plastic, metal, or concrete. In the past, wood or plastic models were made into molds. The molds were then made into metal castings from which the finished products were produced. Additive manufacturing removes the steps to produce molds, going directly from conceptual design to finished product. The process would start with a computer-aided design (CAD) drawing of the product. The CAD drawing would then be sent to the 3D printer, which outputs the object layer by layer according to the specifications in the CAD drawing. Then the 3D printer would be able to replicate production by printing a number of copies of the object.
General Components of Machine Tool
Published in Ajay M. Sidpara, Ganesh Malayath, Micro Electro Discharge Machining, 2019
Ajay M. Sidpara, Ganesh Malayath
The precision of a machine tool depends on the positioning of the cutting tool/spark/energy beam responsible for material removal. One of the revolutionary discoveries that led to the development of precision machine tools is the invention of computer numerical control (CNC) system. In the CNC system, the computer associated with the machine tool reads, interprets, and executes a specific operation using a program written in alphanumerical codes. This includes the movement control of workpiece/tool and sending ON–OFF signals to various machine tool subsystems (e.g. coolant fluid delivery system). The CNC system must be capable of moving the X, Y, Z and rotary stages with high resolution and minimum inertial, hysteresis, and backlash errors. Precision movements in machine tools are achieved by using a servomechanism for motion control. The most popular servo control system in machine tools consists of a servomotor, a lead screw, a precise guideway, and a linear encoder for position feedback. Rotary motion of the motor is converted to linear motion with the help of the lead screw and nut mechanism. To reduce the errors due to backlash in the lead screw mechanism, a ball screw arrangement is often used in the positioning system. Precise guideways will ensure the smooth motion of the stage without any sideway tilting. The optical encoder will give the feedback to the servo controller unit to correct the position of the stage.
Review of Numerical Methods
Published in Yongjie Jessica Zhang, Geometric Modeling and Mesh Generation from Scanned Images, 2018
Numerical computation introduces numerical errors, which are generally defined based on accuracy and precision. Accuracy refers to how closely a computed or measured value agrees with the true value, while precision refers to how closely individual computed or measured values agree with each other. Given the true value or analytical solution, we can compute the true relative error () ϵt=truevalue−approximationtruevalue×100%.
The Research of Manufacturing for HL-2M Vacuum Vessel
Published in Fusion Science and Technology, 2023
Hong Ran, Jilai Hou, Binbin Song, Yuncong Huang, Dangshen Zhang, Qinwei Yang, Le Tang, Xiaoqiang Wu, Zen Cao, Lijun Cai
The VV of a tokamak device is generally a torus-shaped structure consisting of shells, ribs, ports, and supports.5–9 For such a complex VV structure, there are many defects that can occur during manufacturing; especially, VV issues stem from a more common industrial fabrication difficulty, i.e., the distortion that invariably occurs in welding processes, compounded by the overall complexity of the VV. At present, the International Thermonuclear Experimental Reactor (ITER) VV is facing such a problem: In the case of the three VV sectors that had already been delivered, deviations during the welding process led to dimensional “non-conformities” on the outer shells, affecting the geometry of the field joints where the sectors are to be welded together, thus delaying follow-up manufacturing processes. So, manufacturing process research is extremely important. In order to achieve good quality and reach tight profile tolerances, technical difficulties about high-precision manufacturing need to be overcome.
Research challenges and future directions towards medical data processing
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Anusha Ampavathi, Vijaya Saradhi T
The performance measures considered for the analysis of medical storage systems from existing studies are given in Table 1. Diverse measures have been used for the evaluating the performance. Here, accuracy is defined as the closeness of the measurements to a particular value, whereas precision is described as the closeness of the measurements to each other. The time taken for considering a functional unit or model for reaction to a specified input is called as response time. F1- score is also used for testing the accuracy and is the mean of recall and precision. Entropy measures the spatial observations in terms of local vs global entropy. Signal-to-Noise Ratio (SNR) is used for characterising the image quality, which is also said as relative to signal. The number of bit errors per unit time is considered as Bit Error Rate (BER), while the probabilities of receiving a symbol and bit in error are indicated as Symbol Error Rate (SER).
Non-contact surface roughness evaluation of milling surface using CNN-deep learning models
Published in International Journal of Computer Integrated Manufacturing, 2022
With the advent of Industry 4.0, traditional manufacturing and industrial practices are transforming to cope with modern smart technologies. Many precision machined parts are often produced using Computer Numeric Control (CNC) machine. Precision machined parts using CNC machines can be a complex structure, with very tight tolerance in machining accuracy and surface roughness. The term ‘smart’ generally means that the system is intelligent and fully integrated to work collaboratively and adjust in real-time with the demand and conditions without the need for human intervention. Thus, Industry 4.0 is the trend toward automation using cyber-physical systems, the Internet of things (IoT), cloud computing, and artificial intelligence (Lasi et al. 2014). Machine learning (ML) and Deep Learning (DL) are a subset of artificial intelligence. ML and DL algorithms build a mathematical model based on training data without being explicitly programmed. The ML algorithms allow learning data patterns while DL structures algorithms in layers, which can be used to predict future results with high accuracy.