Numerical Control of Machining Parts from Aluminum Alloys with Sticking Minimization

To implement a lot of technological processes in the manufacturing of aircraft parts from aluminum alloys, it is necessary to move the tool at a high speed. High requirements for accuracy and manufacturing quality aircraft parts are provided with application of CNC systems with adaptive control algorithms, in which due the mutual influence of the process parameters provided by the parameters of quality and accuracy. Among a number of factors adversely affecting the quality of high speed machining of aluminum alloys in the paper questions influence Look-ahead system distortion predetermined (set up) cutting parameters on the formation conditions of the sticking material to the tool. The paper deals with high-speed machining without the use of a cooling lubricant (coolant). The paper provides a qualitative description of the heat generation variation in the cutting zone. It is concluded that it is necessary to cutting speed variation when feed variating. An experimental investigation of the cutting parameters for the appearance of the effect of the processed material sticking on the tool for alloy 1933 has been carried out. Areas of an unfavorable (negative) combination of parameters leading to the sticking of the material to the tool have been identified. The method of cutting modes adaptation to contour feed rate variation on curvilinear areas of a processing trajectory is proposed. The analysis of variation in cutting parameters with the partial deceleration of the spindle simultaneously with the deceleration of feed when moving along the path is carried out. The possibility of excluding the sticking of the processed material to the tool when variation the rotation speed by 20…30% is shown. The proposed method is implemented in the CNC system and used for manufacturing of “Antonov” company airplane parts

Controlling Contour Errors in CNC Machines

Ph.DDOCTOR OF PHILOSOPH

Digital Craft | In Search of a Method of Personal Expression Within the Digital

Our relationship with the digital has fundamentally changed within the past decade. A mesh of outside interests have been efficiently folding themselves into our lives. These exist as either a legion of hosted “free” web services touting the promise of a new-found collective intimacy, or a set of tightly coupled IOT(Internet of Things) applications that are slowly being pulled away from our fully capable hardware—all causing us to rely heavily on a virtual infrastructure that demands to host our work and place us at arm’s length of tools that we no longer own or control. This new bargain includes a view into our work and habits so that we can be better understood, tokenized, categorized, mapped, and finally monetized. While many today may be OK with this relationship, I’ll be frank, it unsettles me. I believe something fundamental is lost in this unravelling long-distance relationship. This thesis is a response. It pushes for a more intimate connection with technology within the backdrop of digital design and its many processes. In The Craftsman, Richard Sennett writes: “Making is Thinking,” and in his text he explores the close relationship between head and hand for a small set of traditional craftsmen: a cook, a musician and a glass blower. To elevate the digital within today’s architectural practice, I feel its use must also be seen as craft. But how might a relationship between head and hand manifest itself? Is there some similarity in thinking between Sennett’s craftsmen and the processes of successful digital design? I propose to investigate the mechanisms of digital Making, and hence digital Thinking through three design problems, inspired by the works of Neri Oxman, deskriptiv, Michael Hansmeyer, as well as the methods of D’Arcy Thompson, Shinichi Maruyama, Pina Bausch, and Frei Otto. By mindfully observing my exploration of these from a digital perspective, I believe it will be possible to get a sense of what makes craft possible within this realm

dsPIC-based signal processing techniques and an IT-enabled distributed system for intelligent process monitoring and management

dsPIC Technology employs a powerful 16-bit architecture into single-chip devices that seamlessly integrate the diverse attributes of a microcontroller with the computation and throughput capabilities of a digital signal processor in a single core. The key element of this dissertation is to explore how dsPIC has influenced the applicability of research in e-Monitoring systems. At the same time dsPIC has offered the opportunity to develop methodologies which were previously not even considered. The dsPIC devices are used, in this research, for front end data acquisition, signal processing and communication tools within a proposed monitoring architecture. In this work, novel digital signal processing (DSP) techniques are developed for the monitoring of an example application, namely the challenging one of tool breakage in milling operations. The monitoring regime is implemented on the dsPIC and its capabilities for real-time frequency analysis using overlap FFT and Multiband IIR Filters with dynamic coefficient selection techniques is explored. The developed systems are tested for various cutting conditions using existing machine tool signals and tool breakage is detected reliably in real-time. In attempting to enhance the accuracy of tool monitoring it is evident that the depth of cut (DOC) is an important parameter and achieving its on-line monitoring provides valuable information for condition monitoring. A systematic approach is adopted for the analysis and selection of ultrasonic sensors for distance measurement. A DOC monitoring system is developed using the dsPIC as the data acquisition and processing core. To achieve reliable results, various DSP algorithms are developed, implemented and verified for their effectiveness. The system integration stage combines the above elements for robust and reliable decision making and provides communication of the generated information to support management function using the internet and GSM connectivity. This integration enables an enhanced process management system which is capable of identifying all significant events, for offline analysis and subsequent diagnosis in addition to the real time diagnostic mode

Inverse Dynamics Problems

The inverse dynamics problem was developed in order to provide researchers with the state of the art in inverse problems for dynamic and vibrational systems. Contrasted with a forward problem, which solves for the system output in a straightforward manner, an inverse problem searches for the system input through a procedure contaminated with errors and uncertainties. An inverse problem, with a focus on structural dynamics, determines the changes made to the system and estimates the inputs, including forces and moments, to the system, utilizing measurements of structural vibration responses only. With its complex mathematical structure and need for more reliable input estimations, the inverse problem is still a fundamental subject of research among mathematicians and engineering scientists. This book contains 11 articles that touch upon various aspects of inverse dynamic problems

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment

To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural network into the EKF estimation procedure, the quality of performance can be improved

Prediction of Robot Execution Failures Using Neural Networks

In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution