Catálogo de publicaciones - libros

Information and communication technology (ICT) applications are increasingly being applied in industry. Indeed, in the last years, initiatives like Industry 4.0, Factories of the Future and Industrial Internet of things, pushed by governments and industrial leaders, refer to ICT as the future of manufacturing. To meet this trend, machine tool industry needs to include all these advances. This chapter provides a state of the art of ICT applied in machine tool industry and serves as a reference of all the developments of twin-control project.

Twin-Control () is a new concept for machine tool and machining process performance optimization. It is based on a new simulation model that integrates the different aspects that affect machine tool and machining performance. This holistic approach allows a better estimation of machining performance than single-featured simulation packages, including lifecycle concepts like energy consumption and end-of-life components. This theoretical representation of the machine is complemented with real process data by the monitoring of the most important variables of the machining process and machine condition. This monitored information, combined with the developed models, is used at machine level to perform model-based control actions and/or warn about damaged components of the machine tool. In addition, a fleet-level data management system is used for a proper health management and optimizes the maintenance actions on the machine tools.

This section focusses on the dynamic modelling of the machine tool including its computer numeric control. To properly simulate modern machine tools in machining condition, which show close interaction between the dynamic behaviour of the mechanical structure, drives, and the CNC, we use an integrated methodology that combines control and MBS capabilities in a nonlinear FEA solver called SAMCEF Mecano. The capacity of such digital twin model to simulate machine tool dynamics is demonstrated considering several validation tests.

The Twin-Control product takes a holistic approach to modelling and simulation of machining processes, incorporating machine controller, machine structure, part program, part geometry, and process forces and dynamics into a single system. The focus of this chapter is on the process models used to simulate cutting forces, torques, form error, and tool dynamics throughout a part program. These predictions provide process planners with valuable information about an operation before it is executed on machine, allowing for potential issues to be identified and reduced or eliminated before the first part is produced.

Energy efficiency is an essential quality and cost feature of modern machine tools. The main measures to increase energy efficiency have been investigated many times and are therefore well known. Nevertheless, it must be noted that even with new machine tools, on many occasions only part of this potential is exploited. One reason for this is that the benefits of an energy efficiency measure are not transparent in the planning phase. This often results in a design according to the lowest investment costs. The energy efficiency module of Twin-Control is a simulation tool to support machine tool builders in choosing an optimal machine configuration regarding both the investment and the energy costs. An essential element is simulation models based on physical modelling. These can predict the energy demand of modules within machine tools without prior measurements and ensure that the technical requirements are maintained. During the automated compilation and evaluation of the configuration, the optimization algorithm of the platform, amongst other things, accesses a component database. Using the configuration platform allows the machine manufacturer to offer an individually customized machine tool with the lowest total costs to the machine user.

A new approach for the control of the life cycle of rolling bearings in machine tools is presented. The approach is based on a new simulation tool that has been developed using the well-known ISO 281 standard as a reference. This new tool improves the accuracy of the estimations in two fields: model-based and feature-based prognostics. The tool provides accurate end-of-life estimations, thanks to the determination of more realistic component loads for a defined manufacturing cycle. In addition, it can be embedded in monitoring devices using real machine tool usage data and calculate remaining useful life (RUL) of the analyzed component. Experimental tests confirm that the ISO 281 standard overestimates end-of-life of bearings under controlled conditions and presents vibration analysis as a key tool for an early detection of bearing failure. Indeed, the application of the module for remaining useful life calculation should be combined with vibration-based condition monitoring in order to detect unexpected component degradation.

Data monitoring is a key feature in current manufacturing industry. It provides the chance to know how the machine tool and the machining process are performing and opens further opportunities to improve process performance at various levels (maintenance, process control and even feedback to design). Twin-Control proposes a monitoring and data management architecture for machine tools. It consists of a modular local monitoring equipment that is flexible to cover most of the machine tool configurations. Monitored data at local level is uploaded to a cloud platform to perform fleet-level analysis. The proposed architecture has been working for almost two years, providing an excellent source of real data used as a basis for the rest of developments of Twin-Control.

Knowledge of machine tool behaviour is a key element to improve production, quality and availability as behaviour indicators represent or give an idea of the real state of the machine. Indeed, real conditions can be used to update simulation models’ parameters, detect or anticipate quality fault on workpiece, and to estimate damage state of machine components such as axis and spindle. Behaviour indicators extraction is based on the analysis of specific and known moves or actions of the machine tools. Two kinds of approaches to build these indicators are discussed in this chapter.

Collecting high-quality data points in the production process and using them to optimize the process are becoming increasingly important in the age of Industry 4.0. Measured energy and power data enable benchmarking and condition monitoring applications based on insightful energy performance indicators. As reliable measurement concepts require high investments and are accompanied by uncertain amortization periods, the development of new and more low-cost alternatives is essential. The following chapter will illustrate a cost-effective monitoring approach for industrial production machines on component level that can be applied to a wide range of machines. This is based on an algorithm that calculates the energy consumption from the aggregated power consumption and the control signals of the components. Cost and time-intensive sensor installations can thus be avoided. The data points obtained are then used to derive indicators that are able to identify energy wastage on component level. For this reason, the algorithm is implemented as a plug-in on an existing process control system. To ensure robustness against measurement uncertainties and distorting noise, a Kalman filter forms the basis of the algorithm. The capabilities of the proposed approach are demonstrated on a laboratory machine tool.

Workpiece quality is one of the essential goals of every production company, due to the fact that it is strongly related to customer satisfaction and therefore to sales revenue and business success. While in single-part production every product can be checked for quality issues, this is inefficient in higher volume series production. Hence, random sampling is a widespread method for quality control in high-volume production. The problem with this method is that it just uses samples. Defect workpieces in between the samples are not recognized and therefore processed further on. In this chapter, an approach for monitoring systems for higher volume series production in machine tools is introduced to implement a 100% quality monitoring. It is utilizing the already implemented sensor network that is delivering its data to the machine PLC of the machine tool to indirectly measure the quality.