A new approach for machine's management: from machine's signal acquisition to energy indexes

MATEC Web of Conferences

Data Management is one of the critical points of the tool industry since all applications are heavily based on the industrial data processing (mould, machine, etc..) as well as the business process that are enabled thanks to such data and their smart interpretation. This paper gives a brief overview of a software architecture for sensor platform for Data Management Services that could be implemented into the European Tool Making Industry.

MM Science Journal, 2022

Modern machining systems use horizontal and vertical data integration for the automatic monitoring and quality control of the machining operations. There are currently available some comprehensive papers focused on high-level system integration. Independently of that, there are also multiple publications focused on the automatic monitoring of specific machining operations. There is lack of presented connections between the high-level proposals and the operation-level methods. This paper proposes a framework for milling process monitoring that covers both mentioned levels, i.e., the system complexity as well as particular operation details. The data model consisting of five key objects (cutting tool management; machine tool; workpiece; machining operation and user) is presented within the framework.

MATEC Web of Conferences

One of the value drivers in Industry 4.0 is asset utilization that has capability to remote monitoring and controlling the assets. Some of the problems on the production floor, especially in modern manufacturing areas such as machining process in the Flexible Manufacturing System (FMS), requires the condition of the cutting tools. This paper will discuss a model of cutting tools online monitoring system that operated in web based application. The model involves three sensors and a human machine interface to show the cutting tools condition. The sensors consist of temperature sensor, accelerometer, and electrical power consumption sensor; while in the experiment, the data of those sensors will be compared with the flank wear and crater wear on the cutting tools. The model shows that the data of temperatures, vibration and power consumption are collected by the sensors and sent by internet to the monitoring systems.

ASME 2009 International Manufacturing Science and Engineering Conference, Volume 2, 2009

CNC machine tools have contributed in a very large extent, to the rise in highly ingenious manufacturing processes. The rise of the CNC machine tool coupled with the advent of sophisticated monitoring and control systems have ensured the tremendous improvements on the efficiency front over the past few decades. There are a number of technologies available in the market today, which meet the requirements of specific technical area. But the sine qua non of a successful industry lies in its ability to facilitate continuous technology change and development to maintain its edge in the competitive arena. The Smart Machine Program Initiative (SMPI) at TechSolve, Cincinnati aims at bringing together six distinct thrust areas viz. Machine Tool Metrology (MTM), On-Machine Probing (OMP), Tool Condition Monitoring (TCM), Health and Maintenance (H&M), Intelligent Process Planning (IPP) and Intelligent Machining Network (IMN) under the surveillance of an expert manufacturing system called the Supervisory System (SS), which acts like the brain of the entire manufacturing process.

Journal of Machine Engineering, 2021

Sensor integration into machining equipment has become an important factor for gaining deep process insights mainly driven by increasingly smaller and cheaper sensors and transmitters. Due to advances in microelectronics and communication technology, a broader field of applications in production processes and machine tools can be addressed using sensing devices and their implementation potentials. Ensuring a sensitive but robust data stream from close to the actual process allows not only reliable monitoring but also process and quality control based on sensor information. This paper provides an overview of the utilization of sensor data for the purpose of condition monitoring, model fitting and real-time control coping with stochastic effects. Examples of sensor integration in fields of injection molding, roll forming and heavy-duty milling comprise the state of the art of sensor implementation, data evaluation and possible feedback loops in the respective application scenarios.

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

The ever-increasing complexity in manufacturing systems caused by the fluctuating customer demands has highly affected the contemporary shop-floors. The selection of the appropriate cutting parameters is becoming more and more challenging due to the increasing complexity of products. Until now, the knowledge of the machine operators concerning the modification of the machining parameters and the monitoring information is not sufficiently exploited by the optimization systems. Web and Cloud technologies together with wireless sensor networks are required to capture the shop-floor data and enable the ubiquitous access from multiple IT tools. For addressing these challenges, this research work proposes a Cloud-based, knowledge-enriched framework for machining efficiency based on machine tool monitoring. More precisely, it focuses on the optimization of the machining parameters and moves through an event-driven optimization algorithm, utilizing the existing machining knowledge captured ...

Quality and Reliability Engineering International, 1995

A flexible, generically applicable and inexpensive data acquisition system (DAS), for machine tool condition monitoring, has been designed, constructed and installed as part of a European Union sponsored project. The DAS is more than just a data logger and an array of sensors. It also consists of a methodology for analysing data logging requirements and a relational database that supports this methodology. The database is held on a central 'maintenance management' computer. The monitoring to be carried out by the DAS is specified through this database, which contains detailed information about the DAS's facilities. This feature makes it simple to reconfigure the DAS to implement new monitoring requirements and to customize its operation to meet the needs of different machines. The information in the database is transformed into a look-up table that is read by the software that sets up and controls the data logging processes.

Expert Systems with Applications, 2020

Effective transition from raw industrial data to knowledge-based executive actions without human action requires developing new analytical tools, what also means new challenges for expert and intelligent systems. Studies must be conducted especially on developing effective analytical solutions for intelligent modules of Computerized Maintenance Management Systems, that take advantage of data analysis and decision support tools to predict and prevent the potential failure of machines or its elements. This is why the idea of a new classifier for condition assessment and Remaining Useful Life (RUL) prediction as an expert system tool for real-time monitoring of the manufacturing process was presented. Based on monitoring and current system check data, a new method enabling both early prediction of the machine tool's remaining useful life and its current condition classification was devised. Its failure and normal properties were distinguished as well. To this end, it was proposed that the remaining useful life prediction should be made via the combined use of the Support Vector Machine (SVM) as a classification tool and AutoRegressive and Integrated Moving Average (ARIMA) based identification. This would provide process engineers and machine operators with an expert system that is easy to implement and use at the operational level, thus allowing them confidently perform technological processes, according to the acceptable failure probability.

IFIP Advances in Information and Communication Technology, 2016

Real data from manufacturing processes are essential to create useful insights for decision-making. However, acquiring real manufacturing data can be expensive and time consuming. To address this issue, we implement a virtual milling machine model to generate machine monitoring data from process plans. MTConnect is used to report the monitoring data. This paper presents 1) the characteristics and specification of milling machine tools, 2) the architecture for implementing the virtual milling machine model, and 3) the integration with a simulation environment for extending to a virtual shop floor model. This paper also includes a case study to explain how to use the virtual milling machine model for predictive analytics modeling.

2014

Computer technologies, sensor technologies, fiber optics, high-performance network technologies, mobile devices, wireless sensor networks, and powerful data repositories have permeated all layers of industry and opened the door for unprecedented data analytic capabilities used to improve equipment operation, equipment design, process quality, and product quality. Powerful and inexpensive sensors can continuously collect data about industrial equipment operations and industrial processes. Human operators and maintenance workers also utilize mobile devices to carry out their work collecting and transmitting enormous amounts of data related to the industrial activity. This paper presents the concept of Industrial Analytics which provides a framework for analyzing industrial data to discover valuable knowledge of the industrial activity that can help make decisions related to equipment design, predictive maintenance, process optimization, remote services, and product quality. The paper ...