Removed material volume calculations in CNC milling by exploiting CAD functionality

Material removal volume calculations in machining processes are important in a variety of milling simulation applications, including material removal rate estimation and machining force calculation. In this paper two different approaches are presented to this end, i.e., Z-maps and Boolean operations with solid models. The Z-map method is simple but results in large files and needs sophisticated routines to render acceptable accuracy. Boolean operations between accurate solid models of the tool and the workpiece is implemented on readily available CAD system application programming interface. Beside the computational load which is bound to the accuracy level, it requires a sufficient number of interpolated points through one revolution of the tool to be trustworthy. It is practical to use at particular points of interest along the toolpath

Internet of things and industrial applications for precision machining

The Internet of Things (IoT) can be regarded as an attempt to bring together the physical and the digital world by using devices for seamlessly exchanging and processing information that can be used anywhere, anytime. For industrial automation and manufacturing, the Industrial Internet of Things (IIoT) is regarded as the next step of industrial revolution that promises a step-change in productivity and operational efficiency. Precision machining is a field that has received a lot of research interest as it deals with phenomena and underlying mechanisms that are very complex and highly interacting. As the requirements and demand for products of high quality and tolerances that must be produced with shorter lead times are increasing, innovative approaches and methodologies need to be developed to compensate and IIoT offers an appropriate platform. This paper aims to present an overview of IIoT, investigate potential industrial applications for precision machining and predict future trends

Removed material volume calculations in CNC milling by exploiting CAD functionality

Material removal volume calculations in machining processes are important in a variety of milling simulation applications, including material removal rate estimation and machining force calculation. In this paper two different approaches are presented to this end, i.e., Z-maps and Boolean operations with solid models. The Z-map method is simple but results in large files and needs sophisticated routines to render acceptable accuracy. Boolean operations between accurate solid models of the tool and the workpiece is implemented on readily available CAD system application programming interface. Beside the computational load which is bound to the accuracy level, it requires a sufficient number of interpolated points through one revolution of the tool to be trustworthy. It is practical to use at particular points of interest along the toolpath

Assessing worker performance using dynamic cost functions in human robot collaborative tasks

The aim of this research is to develop a framework to allow efficient Human Robot, HR, collaboration on manufacturing assembly tasks based on cost functions that quantify capabilities and performance of each element in a system and enable their efficient evaluation. A proposed cost function format is developed along with initial development of two example cost function variables, completion time and fatigue, obtained as each worker is completing assembly tasks. The cost function format and example variables were tested with two example tasks utilizing an ABB YuMi Robot in addition to a simulated human worker under various levels of fatigue. The total costs produced clearly identified the best worker to complete each task with these costs also clearly indicating when a human worker is fatigued to a greater or lesser degree than expected

Online tool wear classification during dry machining using real time cutting force measurements and a CNN approach

The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%

Removed material volume calculations in CNC milling by exploiting CAD functionality

Material removal volume calculations in machining processes are important in a variety of milling simulation applications, including material removal rate estimation and machining force calculation. In this paper two different approaches are presented to this end, i.e., Z-maps and Boolean operations with solid models. The Z-map method is simple but results in large files and needs sophisticated routines to render acceptable accuracy. Boolean operations between accurate solid models of the tool and the workpiece is implemented on readily available CAD system application programming interface. Beside the computational load which is bound to the accuracy level, it requires a sufficient number of interpolated points through one revolution of the tool to be trustworthy. It is practical to use at particular points of interest along the toolpath

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators—average roughness, peak-to-valley roughness, and diameter deviation—are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model’s ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology

The influence of control vanes on pneumatic conveying of pulverised fuel at a trifurcator

© 2019 Elsevier B.V. Distribution of pulverised fuels in pneumatic conveying to conventional boilers is an ongoing research area with the intention of improving the performance of power-generating plants. One of the major challenges is the issue of obtaining an even fuel distribution across the fuel carrying lines to the burners or a customised distribution that satisfies the boiler load demand. A 1/3rd scale pneumatic conveying test rig was tested with inert cenosphere powder in a 3-way split configuration. Flow control vanes, similar to those applied in power plant pulverised fuel conveying lines were fitted into the junction and controlled using pneumatic proportional control actuators to alter the distribution of the powder in the three downstream branch pipes extending from the trifurcator. The measurement of the powder mass flux in each stream was carried out and the sensitivity of the particulate stream was assessed with respect to interference from the vane positions at the trifurcator

Flexible Robot Sealant Dispensing Cell using RGB-D sensor and off-line programming

In aerospace manufacture the accurate and robust application of sealant is an integral and challenging part of the manufacturing process that is still performed by human operator. Automation of this process is difficult and not cost effective due to the high variability in the parts to operate and also the difficulty associated with programming industrial robotic systems. This work tries to overcome these problems by presenting an AOLP (Automatic Off-Line Programming) system for sealant dispensing through the integration of the ABB’s proprietary OLP (Off-Line Programming) system RobotStudio with a relatively new RBG-D sensor technology based on structured light and the development of a RobotStudio add-on. The integration of the vision system in the generation of the robot program overcomes the current problems related to AOLP systems that rely on a known model of the work environment. This enables the ability to dynamically adapt the model according to sensor data, thus coping with environmental and parts variability during operation. Furthermore it exploits the advantages of an OLP system simplifying the robot programming allowing for faster automation of the process