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This chapter presents a mechatronic system concept for highly productive and accurate machine tools. Using the example of an experimental machine called ‘MAX’, it will be demonstrated that the working precision of a machine can be increased whilst the effort required for machining and assembly of its mechanical components is kept to a minimum. Firstly, a novel machine structure is introduced, which allows a high reproducibility with low manufacturing effort and provides the necessary degrees of freedom for the correction of motion deviations as well as the additional actuators required for the compensation of dynamic excitations. The simplified requirements for production and assembly of the machine presented leads inevitably to large geometric and kinematic errors. These errors are modelled using rigid-body kinematics. Circularity tests and angular measurements are performed to verify the machine’s ability to correct its geometric-kinematic deviations. For a highly dynamic engraving process, the reduction of the dynamic excitation caused by the drive reaction forces is demonstrated using the principle of impulse compensation. Finally, an approach to the correction of elastic and thermo-elastic errors and the comprehensive modelling and simulation-based analysis of the experimental machine are outlined.

Gantry stages, which consist of two parallel acting servo drives, are commonly used in machine tools. One drawback of this concept is the crosstalk between both drives due to the structural coupling that can cause stability issues and therefore limits the bandwidth of the position control. This paper deals with the development of compliant joints to solve the coupling between the drives. When compared to solutions containing bearings, the advantages of such flexible elements are low friction and the absence of backlash. To adjust the properties of the joints, packages of spring-steel-sheets are used as compliant links. One design aspect of the flexible joints is a low stiffness relating to the rotation around one specific axis, but a high stiffness relating to the other degrees of freedom. With this method, the dynamic behaviour of the gantry stage is modified and the bandwidth of the controllers can be increased. Additionally, by releasing the mechanical coupling of the drives, the reaction forces the actuators have to provide can be reduced. Both systems with flexible and with rigid connecting elements, are analysed by measured frequency response functions.

It is a well-known problem of milling machines, that waste heat from motors, friction effects on guides and most importantly the milling process itself greatly affect positioning accuracy and thus production quality. An economic and energy-efficient method of correcting this thermo-elastic positioning error is to gather sensor data from the machine tool and the process and to use that information to predict and correct the resulting tool center point displacement using high dimensional characteristic diagrams. On the one hand, the selection of which and how many input variables to use in the characteristic diagrams is critical to their performance. On the other hand, however, there are often a great number of possible variable combinations available and testing them all is practically impossible. This paper will discuss the suitability of many different input variable types and present a new method of input variable selection which will be compared to existing methods and demonstrated on measurements performed on a machine tool.

The stability of micro-milling has an important influence on the machined surface, and choosing proper machining parameters can effectively avoid machining process instability and dynamic error. The frequency response function (FRF) of micro-milling tool point varies with tool overhang lengths, and it affects stability lobe diagram (SLD) of the machining system. During the micro-milling process, the cutting thickness was divided into the shearing zone and the plowing zone. In the shearing zone, the cutting thickness was calculated with considering cycloidal tool path and dynamic cutting thickness. After the micro-milling force model was established, the receptance coupling substructure analysis (RCSA) and experimental modal analysis (EMA) were used to obtain the FRFs at the micro-milling tool point. The 2 three-dimensional diagrams, SLD and surface location error (SLE), were developed comprehensively by means of frequency domain method, and the flank micro-milling experiments were carried out. The validity of the three-dimensional SLD containing tool overhang length and the prediction model of SLE was verified by the experimental results, and surface topography was characterized to show the actual machining state. The experimental results show that, compared with SLE, the change of tool overhang length had more significant influence on SLD, and the chatter remarks were not very obvious on the bottom of the workpiece no matter whether the machining process was stable or not. In unstable region, chatter caused by cutting tool left marks on the machined surface. Under the condition of stable milling, a machined surface with smaller SLE and better topography was obtained. The 2 three-dimensional diagrams can provide good references for the selection of optimal tool overhang length and machining parameters comprehensively.

Thermo-elastic deformations represent one of the main reasons for positioning errors in machine tools. Investigations of the thermo-mechanical behaviour of machine tools, especially during the design phase, rely mainly on thermo-elastic simulations. These require the knowledge of heat sources and sinks and assumptions on the heat dissipation via convection, conduction and radiation. Forced convection such as that caused by moving assemblies has both a large influence on the heat dissipation to the surrounding air. The most accurate way of taking convection into account is via computational fluid dynamics (CFD) simulations. These simulations compute heat transfer coefficients for every finite element on the machine tool surface, which can then be used as boundary conditions for accurate thermo-mechanical simulations. Transient thermo-mechanical simulations with moving assemblies thus require a CFD simulation during each time step, which is very time-consuming. This paper presents an alternative by using characteristic diagrams to interpolate the CFD simulations. The new method uses precomputed thermal coefficients of a small number of load cases as support points to estimate the convection of all relevant load cases (i.e. ambient conditions). It will be explained and demonstrated on a machine tool column.

This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures.

Kurzfassung Im Rahmen der Analyse und Bewertung des dynamischen Verformungsverhaltens von Werkzeugmaschinen kommt der modellgestützten Ermittlung des Eigenschwingungsverhaltens sowie der Nachgiebigkeitsfrequenzgänge eine zentrale Bedeutung zu. Hierzu erfordern die Modelle neben einer weitgehend gesicherten Parametrierung der Strukturbauteile auch geeignete Modellierungsansätze für die Fügeverbindungen zwischen den Bauteilen. Der vorliegende Beitrag stellt aktuelle Ergebnisse zur experimentellen Untersuchung realer Kontaktflächen und zur Analyse alternativer Modellierungsansätze innerhalb der Finite-Elemente-Methode vor.

Die Umformung von Karton ist eine Schlüsseltechnologie für die Herstellung nachhaltiger Verpackungslösungen auf der Basis vollständig rezyklier- und biologisch abbaubarer Fasermaterialien. Aus technologischer Sicht ist der Herstellungsprozess solcher Becher heute weitestgehend durchdrungen. Jedoch kann die vorhandene Anlagentechnik den Ansprüchen einer kommerziellen Massenfertigung aufgrund der stark schwankenden Materialeigenschaften des Kartons bislang nicht gerecht werden. Die Modularisierung der Produktionsanlage, eine Technologieregelung innerhalb der einzelnen Fertigungsmodule und die stoffliche als auch informationstechnische Vernetzung der Module zu einem Cyber-physischen Produktionssystem (CPPS) werden diese Lücke schließen.*)

Kurzfassung Im Rahmen der Analyse und Bewertung des dynamischen Verformungsverhaltens und der Prozessstabilität von Werkzeugmaschinen kommt der modellgestützten Ermittlung von Nachgiebigkeitsfrequenzgängen eine zentrale Bedeutung zu. Die Aussagekraft dieser Frequenzgänge wird dabei wesentlich von der treffenden Berücksichtigung der Dämpfung – in Ansatz und Parametrik – bestimmt. Einen wesentlichen Anteil an der Systemdämpfung leistet dabei die lokale Fugendämpfung in Pressverbindungen, deren Wirkung wiederum von der Flächenpressung und der Beanspruchung in den verschiedenen Schwingungsformen abhängt. Der vorliegende Beitrag stellt aktuelle Ergebnisse zur effizienten experimentellen Ermittlung dieser Dämpfung und zur Ableitung geeigneter Modellparameter vor.

From the economic point of view, rising the output rate of processing machines is a key requirement. Thereby, particular demands are to be met, i.e., product quality, process stability, energy consumption, impact of surroundings, or safety at work. Rising the output rate is limited, if only one of these demands can not longer be met. In this paper, a novel control approach is presented that allows to change the executed motion profile due to the actual output rate. Increasing the machine’s performance is evaluated on experimental results. Unused potentials of servo drives are opened up to generate highly dynamic multi-axis motions in processing machines.

The current trend in machine tools requires higher efforts for process setup and process monitoring. Hence, digital models gain importance in the field of feed drives. To constantly adjust them to the real counterpart, parameter identification methods, which are suitable to be utilized during operation of the machine, are of primary importance. One of their main drawbacks is that their results strongly depend on the present system excitation. The paper focusses on the accuracy and presents an identification method as well as approaches to reduce excitation dependency. Subsequently, one combination of modules is applied to the NC-axes of a turning-milling-centre.

This paper presents model-based methods and algorithms for describing and analyzing complex technological interactions in production process chains for the manufacturing of composite materials and components. The presented holistic approach includes the modeling of the process chain, the construction of suitable designs of experiments, the logging and the collection of experimental and production data, the data analysis, as well as the visualization of analysis results and the deduction of the room of maneuver for production settings. Supported by these methods, the development and the putting into operation of complex process chains will become much more efficient.

Thermo-elastic effects are one of the major reasons for positioning errors in machine tools. Next to friction and waste heat from drives, the heat exchange with the machine's surroundings influences the temperature field inside the machine tool significantly. The thermal parameters necessary to describe this heat transfer can be obtained through computational fluid dynamics (CFD) simulations. This paper presents a new method aimed at decoupling these CFD simulations from the thermo-elastic simulations in order to provide the heat transfer parameters quickly and efficiently for transient environmental conditions. This is done by defining a suitable set of load scenarios for the CFD simulations, clustering the resulting parameters with radial basis functions and interpolating them using characteristic diagrams.

The article describes a correction approach for thermally induced motion errors of machine tools that utilizes physically based models of the thermal and thermo‐elastic behavior of machine tools. After a classification and presentation of the functional principle of the model based approach functional core aspects, possible implementation variants and resulting requirements regarding the control are clarified. Examples are presented for which the functionality of the approach was proved and evaluated as well as compared to other model based thermal correction approaches.

New manufacturing processes and extended movability of modern machine tools, such as five-axis kinematics or hexapods, increase the demand for in-process measurement of spatial forces and torques in up to 6 degrees of freedom (DoF). The approach proposed in this paper is based on the idea of integrating 6 single-axis force sensors into the machine’s structure and converting these sensor forces to spatial forces and torques at the tool centre point (TCP) using a measurement model. This concept is advantageous to costs, ruggedness and available workspace when compared to state-of-the-art 6 DoF force/torque transducers. At the same time, the achievable measuring accuracy is similar and also significantly better than the accuracy of drive current based force evaluation. On the other hand, structure and machine influences have to be addressed by suitable measurement models. This article presents design, parametrization, verification, and characterisation of these measurement models on the example of four integration concepts, two in rigid bar frameworks and two in bar kinematics. Further, experimental results are shown which are classified in comparison to a 6 DoF F/T sensor and drive current based force measurement. Finally, other influences, such as structural design and deformations, as well as the integration of sensors and models into the machine’s control software are discussed.

We estimate the orientation of wood fibers in porous networks like paper, paperboard or fiberboard by computing digital thermal conductivity experiments on micro-computed tomography ( μ CT) images with artificial isotropic thermal conductivity parameters. The accuracy of mechanical and thermal constitutive models for porous wood fiber based materials crucially depends on knowing the wood fiber orientation. Unfortunately, due to the high porosity, the micro-heterogeneity of wood fibers, the high carbon content of organic materials and the unknown additives present in industrial paper, μ CT-scans often exhibit low contrast and strong artifacts. Conventional image processing approaches encounter difficulties, as they rely upon convex fiber cross sections. We propose a solution by circumventing the segmentation of single wood fibers in μ CT images, by performing thermal conductivity simulations on binarized wood fiber structures, where an artificial isotropic thermal conductivity is associated to the fibers and the pore space is considered as isolating. The local and global temperature fluxes are assembled into a fiber orientation tensor. This method overcomes the limitations of the mentioned local image processing approaches, as individual fibers need not be resolved and convergence for increasing resolution is a consequence of abstract mathematical theory. We use our novel method to analyze large three-dimensional μ CT-scans and a synchrotron scan of a paperboard sample, serving as the starting point of an accurate micromechanical modeling of the effective anisotropic mechanical behavior of paper and paperboard. These results are crucial for calculating the mechanical strength of deep-drawn paperboard, which will be accomplished in a subsequent article.

Digitization is the defining innovation driver for value creation in the modern global industrial society. At the forefront stand the increase in efficiency for flexibilization and improved resource utilization provided by the self-optimizing automation of processes. Digital technologies must become inherent components of the production system.

In machining up to 75% of the geometrical variations of work piece, features are caused by thermally induced deformations of machine components [1]. Since in dry cutting up to 80% of the thermal energy is stored in the chips [2], we expected a significant effect of these process-dependent heat sources on the machine accuracy. Based on preliminary simulation results, we systematically applied determined quantities of heated chips to a machine table to understand their impact on the temperature field. Temperature sensors where used to measure the temperature change on the tables surface and in the structure. Length measuring probes measured the corresponding deformations at 24 points distributed over the table. The measurements show a temperature change of 4 K at the surface and 3 K in the structure near the heat source after 6 minutes of exposure to 500°C chips. In this case, the impact on the temperature field is local but causes the bending of the table. We recorded 8 micron of thermo-elastic deformations. The results suggest that high-accuracy processes with large energy input, such as hard turning, require the heat induced into the machine structure by hot chips to be implemented into compensation methods and correction algorithms.

Milling structural components from aluminum under enormous requirements of productivity and quality from the aerospace perspective is still the subject of extensive research. The assessment strategies of the surface quality are a big challenge for the surface measurement technique, since only a fraction of the manifesting surface topography can be described with the help of two-dimensional measured variables. The aim of this experimental investigation is a description of several influences on the parameter of the surface roughness Ra and to analyze the prediction of these values. As a result, it can be shown that a more in-depth analysis is necessary to describe the surface quality and topography, which goes beyond the simple specification of roughness parameters.

Models are increasingly applied to improve the thermo-elastic behavior of machine tools. The most universal models are those with physically based approaches. They are used for analysis in the design process of the machines and for determining correction values in machine control during operation. In order to achieve sufficient accuracy, the models must be adjusted with metrological support. This is due to some model parameters, which have a high degree of uncertainty and overall effect. Because of the large number of parameters with very different characteristics, the determination of the parameters relevant for the adjustment is manually and computationally time-consuming. This article presents a systematic method of parameter selection that reduces this effort. The procedure is demonstrated exemplarily by the example of the thermo-elastic model of a hexapod strut axis.

Due to the advances in the digitalization process of the manufacturing industry and the resulting available data, there is tremendous progress and large interest in integrating machine learning and optimization methods on the shop floor in order to improve production processes. Additionally, a shortage of resources leads to increasing acceptance of new approaches, such as machine learning to save energy, time, and resources, and avoid waste. After describing possible occurring data types in the manufacturing world, this study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry. The review shows that there is hardly any correlation between the used data, the amount of data, the machine learning algorithms, the used optimizers, and the respective problem from the production. The detailed correlations between these criteria and the recent progress made in this area as well as the issues that are still unsolved are discussed in this paper.

Temperature is one of the most important key parameter to consider in measurement and mechanical engineering, because every measurement has to be conducted with reference to standard temperature conditions (20 °C, ISO 1). Strictly speaking, almost every measurement depends on the accuracy of the temperature measurement, which requires proper calibration. Therefore, standards list detailed criteria to fulfil temperature calibration with high precision. In fact, any calibration is only valid, if the whole measurement chain is taken into account. This would make recalibration necessary with each variation of the components in the measuring set-up (varying cable length, different measurement channel etc.), which is time-consuming or even impossible in practice. For that reason, this paper presents a practicable calibration strategy, which specifies each component individually and later combines the calibration results according to the composition of the measurement chain. This provides a fast and useful way to achieve the required accuracy of temperature measurement. The examined, exemplary measurement chain consists of an industrial platinum resistance thermometer (IPRT), cables with different lengths, an electrical amplifier and a reference temperature calibrator.

In open-die forging it is state of the art to use simulation tools for creating forging plans and setpoint values for the forging press and the automated part manipulator. These forging plans define required positions and forces. Therefore, the process can be fully automated. However, even small variations of not considered influence parameters lead to different forging results and thus to a discontinuous process. Influencing factors are, e.g. material parameter deviations, uncertainties in force measurements or variations in the part temperature due to varying environmental conditions. This paper presents an approach for a fully automatic open-die forging process with respect to actual conditions, based on a parallel measurement of the workpiece geometry and temperature and a “process-real-time” adaptation on the controller system. The focus of this work is the development of a measuring strategy and an according sensor setup for the combined temperature and geometry measurement of the workpiece. In addition, the structure, the characteristic features of the components and the beam path of the sensors scanning units are shown. Furthermore, first experimental results for the alignment of the beam path are presented. In the outline, the setup and calibration strategy of the measurement system are stated.

In this paper, we first present the continuous spatial Double Ballbar (DBB) measurement we purposed in our former work and then suggest two possible solutions to determine the correspondence of the actual machine poses to the DDB measuring values, so as to extend the scope of application of the continuous spatial DBB measuring approach to quantitative tasks such as calibration. For the machines, on which integrated capture of both data is not possible, it is more flexible to capture the data simultaneously in separate devices and adjust them thereafter. We address the issues of asynchrony and inhomogeneity arising therefrom and purpose an approach which could be automated and integrated as a preprocessing module into the data processing program for further use. The technical feasibility of the data capture concept, along with the purposed approach to synchronization and homogenization, was experimentally validated through measurement on a Stewart–Gough platform controlled using Beckoff TwinCAT.

Additive manufacturing (AM), often referred to as 3D printing, is a generic term describing the layered build-up of material in near net shape frequently attributed with a freedom of design that cannot be achieved otherwise. AM focuses basically on the fabrication of parts for different fields in complex high-tech applications. Examples include components for jet engines, turbines blades, and implants in the medical sector. This is often justified with tool cost savings, shorter lead-time, and overcoming the “design for manufacture” paradigm. On the other hand, a machining allowance is frequently required to counteract the inherent surface roughness and the widespread challenge of part distortion due to residual stresses. At this point, geometrical complexity and small batch sizes transform into strong cost drivers compared to conventional subtractive processing. In fact, these parts are simply hard-to-clamp and hard-to-probe. Moreover, iterative processing is frequently required due to remaining residual stresses in order to reach the target geometry; even the part envelope changes unintentionally. The current paper explores the novel approach of semiautonomous postprocessing of AM parts and components based on flexible clamping, geometry acquisition in the as-clamped position using cooperating laser profile sensors, and an adaptive milling path planning strategy to counteract unforeseen change of the part envelope.

Kurzfassung Die Verschleiß- und Reibungsminimierung in Verbrennungsmotoren ist einer der wesentlichen Bausteine zur verbrauchs- und emissionsarmen Mobilität. Ein Ansatz zur Optimierung hochbeanspruchter Gleitkontakte, wie z. B. des Nocken-Stößel-Tribosystems, ist eine definierte Strukturierung der Oberfläche. Dies wird mittels spanendem Verfahren mit bestimmter Schneide angestrebt. Dieser Beitrag stellt die prototypische Realisierung der kinematischen Anforderungen für die Mikrostrukturierung der Oberflächen einer variablen Nockengeometrie vor.

We study mechanical vibrations in milling with non-uniform pitch and variable helix tools. The process is modeled by a periodic delay differential equation with distributed delay, which takes into account, for example, the nonlinear cutting force behavior, the effect of runout, and the exact delay distribution due to the unequally spaced flutes. We present a new method for the identification of the chatter stability lobes from the linearized system that is based on the multifrequency solution. We give detailed remarks on the truncation of the resulting infinite dimensional matrices and the efficient numerical implementation of the method. Cutting tests for steel milling with a customary end mill with non-uniform pitch and variable helix angle and a conventional end mill with uniform pitch and constant helix angle are performed. The numerical and experimental results coincide well. They reveal a significant increase of the limiting depth of cut for the variable helix tool compared to the conventional tool. Moreover, we show that in contrast to conventional tools, for non-uniform pitch and variable helix tools, an exact model with time-varying coefficients, nonlinear cutting force behavior, and runout is necessary for an accurate prediction of the stability lobes.

Predicting mobile machine tool dynamics prior to moving the machine to a new part and/or location is essential to guide first-time-right in situ machining solutions. This paper considers such a priori prediction of assembled dynamics under varying base/part/contact characteristics by applying dynamic substructuring procedures. Assembled dynamics are predicted by substructural coupling of the machine's known free-free response with the known response of any base/part measured at location. Since obtaining the machine's free-free response remains non-trivial, we instead extract the machine's dynamics using substructure decoupling procedures. Substructuring is carried out using measured frequency response functions. Methods are tested for robustness, and are experimentally validated.

Kurzfassung Im Rahmen der Analyse und Bewertung des dynamischen Verformungsverhaltens und der Prozessstabilität von Werkzeugmaschinen kommt der modellgestützten Ermittlung vonNachgiebigkeitsfrequenzgängen eine zentrale Bedeutung zu. Die Aussagekraft dieser Frequenzgänge wird dabei wesentlich von der treffenden Berücksichtigung der Dämpfung – in Ansatz und Parametrik – bestimmt. Einen wesentlichen Anteil an der Systemdämpfung leistet dabei die lokale Fugendämpfung in verschraubten Flanschverbindungen, deren Wirkung von der Vorspannung und der Beanspruchung in den verschiedenen Schwingungsformen abhängt. Der vorliegende Beitrag stellt aktuelle Ergebnisse zur experimentellen Ermittlung dieser Dämpfung und zur Ableitung geeigneter Modellparameter vor.

A new interdisciplinary approach is discribed to identifying unknown parameters using an extended version of the known interval bisection method. This developed method is based on the use of finite elements for calibrating the simulation calculation. The resulting thermo-elastic deformations which occur in drilling processes with impaired cooling lubrication are to be used as correction values for tool positioning in the NC control. Based on the strong impact on workpiece temperature of machining, a simulation approach is presented for calculating the temperature fields and their thermo-elastic consequences. In addition, methods are presented to correct these effects. This paper particularly deals with the temperature fields of drilling operations. Special attention is paid to the technique employed for iterative numerical determination of the unknown heat flux ηw and heat transfer coefficient \(\bar {\gamma }\) values. Finally, the data obtained from experiments are compared with those achieved by numerical simulation in order to verify the efficiency of simulation and determination of parameters.

Kurzfassung Im Rahmen der Analyse und Bewertung des dynamischen Verformungsverhaltens und der Prozessstabilität von Werkzeugmaschinen kommt der modellgestützten Ermittlung von Nachgiebigkeitsfrequenzgängen eine zentrale Bedeutung zu. Die Aussagekraft dieser Frequenzgänge wird dabei wesentlich von der treffenden Berücksichtigung der Dämpfung – in Ansatz und Parametrik – bestimmt. Einen betragsmäßig meist sehr kleinen jedoch für die Systembetrachtung wesentlichen Anteil an der Dämpfungswirkung leistet dabei die Werkstoff- bzw. die Bauteildämpfung. Der vorliegende Beitrag stellt aktuelle Ergebnisse zur effizienten experimentellen Ermittlung dieser Dämpfung und zur Ableitung geeigneter Modellparameter vor. Dabei wird auch auf die Besonderheiten bei extrem schwacher Dämpfung eingegangen.

Based on the method of adaptive finite elements (FEM) a correction approach has been considered to identify the influence of thermo-elastic workpiece deformations during the production process milling. The paper presents a simulation-based tolerance variation calculation of cutting paths, which is caused by the heat input of the machine tool. Therefore a mathematical method is developed to numerically depict the progress of the miller with different curves A(t), B(t) and C(t). These curves are used to map the state of the milling path during the production process as well as to compare the current workpiece contour and the target workpiece contour. The tool center point (TCP) correction results from mapping of time-dependent deformation fields from the FE simulation. The aim is, on the one hand, to make statements before the production about keeping the tolerance, and on the other hand, to derive other correction approaches for the adaption of the cutting path coordinates.

In situ machining of large workpieces is made possible by moving mobile machines to workpiece locations. To guide and enhance on site machining performance, dynamics of the assembled system must be predicted beforehand. Beforehand prediction of assembled response requires dynamics of the mobile machine in its free-free configuration be coupled with response of base/workpiece measured at location. Since obtaining the free-free response of the machine is non-trivial, this paper presents a substructure decoupling scheme that instead extracts the machine's response from known dynamics of the machine mounted on a base, and from information of a substructural base system. Simulation driven investigations show the decoupling scheme to be robust even under the vagaries of measurement noise and changes in the residual substructural base system. Extracted dynamics of the machine can be subsequently synthesized with known dynamics of other bases/workpieces for a priori prediction of assembled dynamics to guide first-time-right on site machining.

The surface location error (SLE) is used to evaluate the machining error caused by forced vibration at the contact zone during the stable machining. When the workpiece was machined continuously, the convergent SLE would change with the dynamic response of the workpiece as well. In this paper, we first established the conservative stability lobe diagram (SLD) caused by regenerative effect, and excluded the instability induced by cumulative effect with continuous radial immersion. An assumption was made that multiple immersion-by-immersion was equivalent to one immersion with the same material removal, and then the in-process workpiece (IPW) dynamics was obtained by means of structural dynamic modification. Finally, we used the improved third-order full discretization method to obtain the predicted cumulative surface location error (CSLE) with considering the dynamics of milling tool and workpiece simultaneously. Based on the stable machining parameters, the cutting tests and measurement were performed. The experimental results showed that the proposed model could effectively predict the distribution of CSLE. Under the same spindle speed, the CSLE at different cutter locations were not the same and the difference was obvious, which indicated that the dynamics from the workpiece played an important role in the formation of CSLE during the continuous machining process and should be included in the predicted model.

Vibration measurements of tools are significant for machine modelling, error prone state identification and machine operation optimization. In the milling process, forced vibrations are the main factor resulting in shape deviations as well surface roughness of machined work pieces and depend on the spindle speed and tool properties. Numerical models for predicting the dynamic behavior of machine systems can help to reduce vibrations. For an optimization, the model parameters need to be evaluated by simultaneously measuring the vibration of the tool tip in force direction and its vertical direction under different rotational speeds with high spatio-temporal resolution. In this work, a non-contact, single-sensor system and a signal processing algorithm are presented for measuring the vibrational behavior inside of a CNC milling machine with a known force. It enables in situ, simultaneous bi-directional vibration measurements directly at the tool tip with measurement rates up to 50 kHz, a circumferential resolution below 230 μm and a displacement uncertainty down to 40 nm, at rotational speeds up to 300 Hz. The dynamic behavior parameters of the tool are evaluated depending on the measured vibration spectrum with relative uncertainties of 10 - 2 , enabling model optimizations. The measurements show a decrease of the natural frequency with increasing spindle speed. While the laser-based measurement principle does not bias the vibrational behavior it inherently guarantees orthogonality of the sensing axes, as well.

In this paper we present a systematic approach to generating a path for continuous Double Ball Bar(DBB) measurement. The generated path is feasible, free of collision, jerk-optimized and sensitive to the perturbation in kinematic parameters. The approach could be automated and be applied not only to parallel kinematic machines(PKM) but also to serial mechanism. We will discuss about the properties of continuous DBB measurement and conclude that the condition number of identification Jacobian is a proper index to describe the sensitivity of a continuous path to the perturbation in kinematic parameters. The sensitivity of the generated path will be experimentally validated on a Stewart-Gough platform with comparison to two otherwise designed paths. Measurement shows that the generated path is also suitable for time-demanding tasks.

Die Digitalisierung ist der bestimmende Innovationstreiber fur die Wertschopfung in der modernen globalen Industriegesellschaft. Im Vordergrund steht der Effizienzgewinn fur eine Flexibilisierung und bessere Ressourcennutzung in der Produktion durch die selbstoptimierende Automatisierung von Ablaufen. Digitale Technologien mussen inharenter Bestandteil des Produktionssystems werden. Ein Cyber-Physisches System reprasentiert die angestrebte Einheit von Realitat und digitalem Abbild und ist die Weiterentwicklung der Mechatronik zu einem symbiotischen Systemansatz auf Basis der informationstechnischen Vernetzung aller Komponenten. Die Informationstechnik und auch nichttechnische Disziplinen haben eine Vielfalt an Methoden, Techniken und Verfahren hervorgebracht, mit denen sensorische, aktorische und kognitive Funktionen so in technische Systeme integriert werden, dass diese Funktionalitaten aufweisen, die bislang nur von biologischen Systemen erfullt wurden. Auf diese Weise erwachst aus der Evolution durch die Industrie-4.0-Technologien tatsachlich ein disruptiver Paradigmenwechsel. Produktion, Ausruster und Produktentwickler treten in eine neue Qualitat eines Innovationsverbundes ein.

Kurzfassung Im Rahmen von Industrie 4.0 spielt die Wandlung von Produktions-systemen hin zu Cyber-physischen Produktionssysteme eine tragende Rolle. Nach einer Einordnung des Themas beleuchtet der Beitrag einen konkreten Anwendungsfall, die Temperierung von Werkzeugmaschinen mithilfe von CPS-Komponenten, genauer. Als wesentliche Aspekte werden die Modellierungsschritte (FEM, CFD, Systemmodell) vorgestellt sowie erste Aussagen hinsichtlich der Gestaltung und Komponentenstruktur getroffen.

This paper presents a linear-quadratic regulator (LQR) approach for solving inverse heat conduction problems (IHCPs) arising in production processes like chip removing or drilling. The inaccessibility of the processed area does not allow the measuring of the induced temperature. Hence the reconstruction of the heat source based on given measurements at accessible regions becomes necessary. Therefore, a short insight into the standard treatment of an IHCP and the related LQR design is provided. The main challenge in applying LQR control to the IHCP is to solve the differential Riccati equation. Here, a model order reduction approach is used in order to reduce the system dimension. The numerical results will show the accuracy of the approach for a problem based on data given by practical measurements.

Die Leistungsfähigkeit und Ressourceneffizienz moderner Produktionssysteme wird wesentlich durch ihr dynamisch-mechatronisches Verhalten bestimmt. Um quantitative Aussagen hinsichtlich auftretender Bahnabweichungen zu erhalten, hat sich die kombinierte Simulation von mechanischer Struktur und Antriebsregelung etabliert. Während der Verfahrbewegung ändert sich jedoch das mechanische Strukturverhalten durch die Verlagerung von Komponenten wesentlich. In der flexiblen Mehrkörpersimulation (MKS) existieren verschiedene Ansätze dynamische Bahnbewegungen abzubilden, welche jedoch komplexe, genauigkeitsreduzierende Reduktionen der FEM (Finite-Elemente-Methode)-Submodelle und/oder aufwendige, zeitsynchronisierte Co-Simulation in unterschiedlichen Simulationsumgebungen erfordern. Innerhalb geschlossener FEM-Umgebungen bieten transiente Rechnungen eine hohe Abbildungsgüte für die Zeitbereichssimulation, die Berechnungsdauer für große Modelle schließt jedoch Parameterstudien und die Integration der Regelung de facto aus. Es wird ein alternativer Ansatz unter Nutzung holistischer FEM-Modelle beschrieben, welcher innerhalb der Grenzen der linearen Modalanalyse eine effiziente Simulation des dynamischen Bahnverhaltens unter Berücksichtigung großer Verfahrbewegungen erlaubt. The capabilities of state-of-the art machine tools are significantly limited by their dynamic-mechatronic system properties. The combined simulation of the mechanical structure and control has been established to determine dynamic tool-path inaccuracies. While machining a work piece, the structural components of a machine tool change their position towards each other, which as a result leads to a different mechanical system. There exist different strategies to include that position dependent behavior within the coupled multi body simulation. However complex model-reduction methods for the flexible FEM substructures are necessary. Furthermore a time-synchronized parallel simulation between the simulation of the mechanical structure and the simulation of the control - typically carried out as digital block simulation - is necessary. On the other hand transient simulation approaches in the FEM environment of the overall structure are theoretically capable to represent realistic models but are by fare to complex and time-consuming to be implemented for time domain simulations including control. Based on these inadequatenesses the paper shows a new approach to consider the changing system behavior due to large movements of structural components based on holistic FEM-models. It allows an efficient integrated simulation inside the digital block simulation within the limits of a linear modal analysis.

Structural deformations of machine tool components that translate and/or rotate relative to each other to realize tool motion results in tool point dynamics varying along the tool path. These changing dynamics interact with the cutting process and the control loop of the drives to limit machine performance, making it necessary to virtually characterize these interactions such as to guide design decisions. To facilitate rapid evaluation of these varying dynamics, this paper describes a generalized frequency-based substructuring approach that combines the position-invariant component level receptances at the contacting interfaces between substructures to obtain the position-dependent tool point response. Receptances at the contacting interfaces are approximated by projecting them to a point to facilitate a multiple point receptance coupling formulation. Complete machine behavior is represented by just a few sets of receptances, making the model computationally more efficient than full-order finite element models and other dynamic substructuring methods. Position-dependent dynamic behavior for a representative three axis milling machine is simulated and numerically verified. Rapid investigations of the varying dynamics assist in virtually characterizing machine performance before eventual prototyping.

Designing processing machines or industrial products has become more and more complex. To handle the increasing complexity, many different stakeholders from various domains work together in interdisciplinary project teams. The present paper explores how blueprints can be applied to interdisciplinary product engineering processes including various disciplines with different motives and wishes. The aim of the presented blueprint variation is to chart the engineering design process using the example of a sales department in order to uncover operational weaknesses and highlight possible improvements. Opportunities and limitations of the presented adaptation are discussed.

It is a well-known problem of milling machines, that waste heat from motors, friction effects on guides and also the milling process itself greatly affect the positioning accuracy and thus the production quality. An economic and energy-efficient method of correcting this thermo-elastic positioning error is to gather sensor data from the machine tool and the process and to use that information to predict and correct the resulting tool center point displacement using high dimensional characteristic diagrams. The size of these characteristic diagrams depends on the number of input variables (sensors) and the fineness of the discretization of the grid. While the number of sensors can usually not be reduced without affecting the quality of the prediction, it is often possible to minimize the size of characteristic diagrams through the use of adaptive grid refinement. This ensures that the finest grid sections correspond with the load cases that have the largest local gradients. Through such adaptive refinement, it is possible to reduce storage capacity and computation time without significant loss of precision. The aim of this article is to examine, test and compare different methods of adaptive grid refinement. For this, simulation data from a machine tool is used.

Mobile machining solutions use autonomous machining units that can be transported to different part locations, making possible easy maintenance and repair of large industrial equipment. Every new part and location results in different boundary conditions for the mobile machine tool-part system; influencing the dynamics of the combined system and necessitating different strategies for part/machine referencing and clamping. To facilitate efficient mutability and modularity in mobile machining solutions, this paper presents a dynamic substructuring strategy that combines the response characteristics of the mobile machine unit with that of two different simulated base models under varying levels of contact stiffness and damping to obtain the synthesized mobile machine tool dynamic response. Numerical verification of the approach is provided. Framework presented can also combine measured response of parts for which models may not be available a priori. Methods presented provide experimental guidelines for establishing strategies for part/machine referencing, and planning of machining strategies based on the evaluated dynamics.

Kurzfassung Um das thermische Verhalten von Werkzeugmaschinen unter verschiedenen praxisnahen Bedingungen analysieren zu können, steht am Fraunhofer IWU eine Thermozelle für experimentelle Untersuchungen zur Verfügung. Der vorliegende Fachbeitrag beschreibt die messtechnischen Grundlagen und Vorgehensweisen bei thermischen Untersuchungen unter definierten Randbedingungen, u. a. als Grundlage für die TCP-Verlagerungskorrektur mit Hilfe hochdimensionaler Kennfelder.

Improved drive dynamics decisively boost productivity and accuracy of machine tools. Linear motors have advantages to overcome drawbacks of electromechanical drives, but are limited by their power density. Reaction forces of linear motors cause undesired excitation of the machine structure. Current research approaches focus on impulse decoupling and compensation as well as on force reduction. The paper presents a new design approach, characterised by two mechanically coupled and opposite driving linear motors. This arrangement improves the static and dynamic properties by force distribution which leads to an impulse-free feedback system. In order to have an effective use of the gained dynamics various control structures for coupled drives are investigated. Tuning methods as well as simulation and experimental results are discussed.

Machine learning, big data and deep learning are today’s catchphrases for how to improve reliability and productivity of your manufacturing equipment. Production companies implement a large number of sensors to record every activity within their production lines and learn as much as possible about their running processes in order to predict shifting product properties and to prevent stoppage due to failure. The successful application of machine learning algorithms to predict machine and process behavior depends on a reliable and balanced database. Since the foremost goal of every manufacturing business is to make sound parts and to avoid defects, there is a large amount of data available for smoothly running processes but only very little for failure production. One approach to solve this imbalance would be to link the production line data with simulation data. Simulation models allow for computing failure parts with no additional costs and therefore enable the exploration of the entire parameter space. We conducted press-hardening experiments with a variation of process parameters for a structural car body part on the press hardening line at Fraunhofer IWU. As an evaluation criterion, we measured the hardness of the final part at critical spots. In order to expand the experimental data, we applied FE simulations to the entire press hardening process chain. The paper explains limitations of the model and elaborates on its parameterization. As a final task, we applied a basic machine-learning algorithm to both experimental and numerical data as well as to their combination in order to evaluate the data space expansion through simulations. The results obtained through machine learning indicate significant differences in the prediction of part quality for solely experimental data and its combination with simulation data. This is especially true for press hardening because of non-linear system behavior and a large amount of uncertain and hard-to-identify parameters. We found that the most challenging parts of uniting measured and simulated data is not only to create simulations with appropriate accuracy, which allow for a meaningful extrapolation of the parameter space, but also to compare simulation and production data based on the same criterion and to have stable simulation models for the entire parameter range.