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Water-miscible cooling lubricants are used in approx. 90% of metalworking processes mainly for lubrication and cooling of the contact zone between tool and workpiece. The base oils of the widely used water-miscible emulsions are often of mineral origin and represent a significant share of the CO2-fingerprint in production systems in terms of their extraction, processing and the possibility of recycling. Cooling lubricants are furthermore negatively discussed regarding to their health compatibility. Those disadvantages of conventional cooling lubricants drive the research for replacing mineral base oils with vegetable base oils, which contribute to a safer work environment and greener production. In this work, both conventional cooling lubricants and several water-miscible emulsions with various edible and non-edible vegetable-based oils were prepared and characterized regarding their suitability as cooling lubricants for the milling of aircraft stainless steels. Comprehensive experimental investigations were carried out to determine the process forces and the resulting tool wear as technical performance indicators for the test evaluation.

The structure of factory systems has a long-term impact on the productivity and performance of production systems. With an increasing relevance of decentral renewable energy sources, factory planning becomes more complex due to an increase of decision criteria and possible solutions, but ecological impacts are also growingly depending on early structure decisions. However, decisions during planning processes in industry are still based on experience, qualitative assessments and discussions. Thus, planning results may not exploit the potential of the available solution space, nor do they allow quantitative traceability of layout configurations. Generative Design (GD) gained popularity in topology optimization and building construction and provides promising opportunities for complex design tasks. Not least with the increasing relevance of Building Information Modeling (BIM) in manufacturing, new methods and technologies for optimization become available to factory planners. Hence, the paper describes options for enhanced decisions in sustainable factory layout planning. Therefore, it provides a quick overview on optimization and GD in layout planning as well as the relation towards BIM and sustainability assessment. Based on this, possible extensions of optimization targets and boundaries towards energy and sustainability-related criteria is elaborated. Afterwards, necessary and potential data acquisition with the openBIM-related Industry Foundation Classes (IFC) is discussed. The paper concludes with an outlook towards further investigations on the overlap of layout design, BIM and sustainability.

In machining, tool wear is one of the main influence factors for the resulting quality of the product. Several wear mechanisms have to be addressed to prevent unwanted deterioration of the tool integrity. For aluminium alloys, adhesion of the workpiece material on the cutting tool is one of the most challenging wear mechanisms. Engineering surface microtopographies has been proved as a convenient strategy to tackle this issue. Particularly, the direct laser interference patterning (DLIP) technique enables the integration of periodic structures on the micrometer or sub-micrometer scale. In this study the structuring of tungsten carbide with different cobalt content is presented. The interference of two laser beams leads to periodic line-like structures with a spatial period of 5.5 µm. A maximum structure depth of 2.2 µm is reached by controlling the processing parameters. Moreover, the wettability of the structured samples was analyzed by contact angle measurements with selected cooling lubricants, revealing a hydrophilic behavior with a decreased contact angle of 10°. This work gives an insight into the possibilities of structuring tungsten carbide materials with a picosecond laser source in combination with an innovative beam shaping setup from the point of view of an analysis to explore the formation of structured surfaces and their wetting behavior with cooling lubricants.

Due to the increasing digitalization, modern solutions are needed to work effectively with rapidly growing amounts of data in the automotive production. This paper shows two potential fields of application for the practical use of data reduction methods. In particular, the combination of Fourier-related transformation methods (e.g. discrete Fourier transform) and surrogate models for the useful 3D visualization of complex interrelationships is shown using the example of press hardening and deep drawing.

Conventional machining of high-tech parts and components is often associated with complex processes, long lead times and small to medium batch sizes. Obviously, there is great interest to shorten process development periods and increase process reliability. The transformation of conventional machines into cyber-physical production systems (CPPS) promises significant improvements here. In fact, CPPS allows real-time data acquisition and processing, enabling to target integrated process improvement and quality assurance. In the present work, a methodical procedure for structured data aggregation is introduced for a Cyber-physical enhanced machining process while being explained from a high-tech application point of view. This includes data generation, extraction, transfer and storage, data-consolidation, linkage, visualization and interpretation. Finally, the paper illustrates these aspects in terms of the effects on the “digital product requirements” in order to achieve the presented “analytics-ready” data model using the example of a high-tech application.

Computer models, which simulate the thermo-elastic behavior of machine tools, are used for optimizing machine designs and more recently for controller-integrated online corrections. Sufficiently accurate corrections of errors caused by the thermal machine behavior require quick and precise identification of uncertain and irregularly distributed parameters based on measurements. The paper presents a systematic approach to conduct parameter identification during machine commissioning. In particular, the acquisition of required data for parameter adjustment and model optimization is addressed. Finally, the procedure is demonstrated on the example of a machine tool's feed axis.

This paper describes a method for creating a digital twin based on planning and process data for machining processes by using the example of components in the aerospace industry. This development is based on an information model that combines the properties and relationships of relevant information and data involved in the manufacturing process. This system integrates mathematical (technical-technological) models and algorithms for the determination of process parameters as well as planning-relevant information from the machining process. The technology of the digital twin makes it possible to recognize problems directly in the machining process, e.g. deviations from the planning specifications and to derive targeted measures for quality assurance and optimization of the machining process.

For continuous radial immersion milling operations, the dominant mode shape becomes difficult to determine when stiffness of the tool and workpiece are comparable, and this can pose a great challenge for ensuring machining processes stability. In this paper, we propose a rapid method to obtain time-varying modal parameters of the workpiece by combining experimental measurements with the receptance coupling method. Firstly, the contact parameters between the workpiece and vise were identified by a so-called dynamic coupling matrix. Then the mode shapes and the time-varying natural frequency of the workpiece were determined using the modal parameters of workpiece. Finally, the stability lobe diagrams (SLDs) were computed using the modal parameters and then were validated by undertaking immersion milling experiments. The experiments showed a more conservative and practical SLD for general workpiece under continuous radial immersion, where the workpiece mode had not always dominated the machining process. Based on the proposed method, we also explored two modifications in form of additional cylinder masses and passive support, to suppress chatter. Both modifications were effective in enhancing the minimum boundary of the conservative SLD, and the modification of passive support worked better. Although the modification of the workpiece could improve the stability boundary, it indirectly affected the dynamics of the milling tool through the interaction area between the workpiece and milling tool.

In machine tools, in-process force measurement is required by many manufacturing applications, where a particular demand for spatial measurements in up to 6 degrees of freedom (DoF) is growing. Beside expensive commercial 6 DoF force/torque sensors or vague drive current evaluation, sensor integration as part of machine components or joints has been discussed for a long time. The approach presented here, integrates 6 cost-efficient commercial 1 DoF force sensors in hexapod structures and kinematics, that are particularly suitable for sensor integration due to the absence of friction, the presence of mainly longitudinal forces and the availability of 6 DoF. These sensors can be placed at different positions, whereby this article focuses on a rigid hexapod-based end-effector. As the end-effector is not an independent measuring system, but part of a machine, that moves dynamically through the workspace and carries workpieces or tools, a suitable measurement model is necessary that addresses all those influences. After a brief literature overview and introduction to the approach, this work presents the dynamic measurement model including sensor and quasi-static error parameters, aspects about optimal framework design and several steps of validation and evaluation of the new measuring system. These include application of static loads, workspace analysis, dynamic transfer behaviour, rigid body dynamics compensation and, finally, process force measurement during a milling process.

As an unfavorable factor of machining process, chatter threatens the machined quality of workpiece, which determines the assembly and fatigue performance of the workpiece. During the interaction between machine tool and thin-walled workpiece, the process damping effect, multiple modes response and dynamic changes caused by the material removal of the in-process workpiece (IPW) will make the machining process more complicated and introduce great difficulties to dynamic modeling and performance prediction. In this paper, we considered the process damping determined by the indentation volume between flank face of milling tool and machined surface, and used multi-mode model to describe this behavior. In order to establish the assembled material removal model of the IPW dynamics with multiple modes, the structure dynamic modification (SDM) and finite element method (FEM) were combined together. The updated third-order full discretization method was applied to solve the dynamic equation in modal space. Then, the three-dimensional stability lobe diagrams (SLDs) with and without material removal along the tool path were obtained respectively by enveloping multiple modes of the IPW and milling tool together. Finally, the cutting tests were carried out. The experiments showed that the assembled model could predict the dynamics of IPW accurately, and the proposed stability analysis model was relatively close to experimental results. Besides, the modes of thin-walled workpiece with weak rigidity do not always play a dominant role in the process of machining. Although the material removal rate is limited by considering the multiple modes of the system, the processing quality can be ensured.

Thin sheets and thin-walled structures are of great economic importance in the aerospace industry due to the enormous efforts being made in lightweight construction. The challenge in machining is to use a reliable clamping device to take account of the sheet metal or component structure as well as to work productively and reliably. For this purpose, a hybrid clamping system has been developed that functions on the basis of vacuum and the adhesive properties of a carrier medium in order to form a complex joining. This method was verified by material tests and then developed to make it usable for the HSC and HPC milling of aluminum sheets. For this purpose, demonstrator parts were manufactured in order to test the critical loads of this clamping system on the one hand and to produce structures that cannot be realized economically with other machining methods on the other hand.

The value of data analytics is fundamental in cyber-physical production systems for tasks like optimization and predictive maintenance. The de facto standard for conducting data analytics in industrial applications is the CRISP-DM methodology. However, CRISP-DM does not specify a data acquisition phase within production scenarios. With this work, we present DMME as an extension to the CRISP-DM methodology specifically tailored for engineering applications. It provides a communication and planning foundation for data analytics within the production domain. We show the feasibility of our methodology for engineering applications within a case study in the field of work piece detection.

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.

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.

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.

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.

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.

Parallel kinematic machines exhibit strong position-dependent dynamic behavior, which changes the stability and the productive cutting conditions within the machine work volume. In this paper, position-dependency of a hybrid serial-parallel scissor kinematic machine is modeled by synthesizing its substructural reduced order finite element models. The model allows the efficient investigation of position-dependency as an alternative to using time consuming full order finite element models. The predicted position dependency of the machine with the reduced order model is experimentally validated. Stability maps are simulated as a function of machine positions to identify the productivity levels within the machine work volume.

This paper reviews the design, engineering principles and applications of machine tools specially developed for large parts. Large workshop machines are commonly used for manufacturing, where the impact of the general engineering principles differs substantially from those applied to conventional size machines. Portable machines are used during assembly and operation due to mobility, agility and energy constraints. Such large dimensions produce an amplification factor of any error source, so verification and calibration of such large or portable machines becomes even more critical than in conventional machines. The paper also includes future trends and unsolved challenges.

One possibility to increase the performance of machine tools is the achievement of higher accelerations and jerks of moved slides and stands. By this the energy consumption and machining-costs can be optimized. Nowadays machine tool structures are already optimized and further enhancements are not easily achieved. The paper deals with the mechatronic system optimization of a large-scale HSC- and HPC- portal milling centre for e.g. forming tools. This includes the identification of dynamic limiting structures and the following optimization by mechatronic system-monitoring. From process necessities a machining-time reduction by higher accelerations and improved process stability is intended. The system parameters where achieved by the implementation of a redundant axis and the usage of up-to-date lightweight design by reinforced plastics.All over the design phase the structure was constantly monitored regarding their dynamic behaviour. Furthermore a model was used – using a state space – to prejudge the machining time for a concrete example cycle. So even before any physical prototype exists the mechatronic system-behaviour of the machine tool was pre estimated using a virtual mechatronic model. This allows for a virtual product design with a new and improved quality of safety in achieving the pre-defined goals.

Machine tool performance, production accuracy and energy efficiency are interdependent design criteria. To solve this design conflict, bionics – this means technical interpretation of long-term optimized biological principles – could be an effective way to accelerate innovation loops in modern machine tool development processes. In the paper, these aspects will be discussed, and selected solutions will be presented in detail.

Drilling a stack made of fiber-reinforced plastic and metal layers is investigated and presented in this paper. Improvement of performance can be achieved if the process parameters will be adapted to the various drilled materials and drill position. Therefore, the true position of the drill should be precisely known. An algorithm for real time monitoring of drill position is suggested. Drill position is defined by analyzing acoustic emission signals from a sensor located near the drilling point. During drilling of CFRP and Al-stacks, it could be proved that material changeover can be identified ahead of time, especially when using stepped drills.

Complete processing in one machine calls for a configuration targeting major process stability as well as a structure geared towards accuracy and dynamism. The compromise between potential dynamism and machine size can be improved by using redundant drives. The advantageous use requires the specialization on partitioning of the complete motion. With the presented algorithm the reactive sectioning of a multidimensional motion into two components with different dynamic characteristics succeeds. This provides a useful basis for the application of the principle of dynamic sectioning for a suitable design of drive redundant machine structures. This paper describes models, simulation tools and the method of the development process.

The following paper introduces an approach, which allows the consideration of the kinematic as well as the dynamic properties of parallel kinematic machines. Based on the results of a preceding kinematic optimization, a FEM-model with arbitrary input parameters is designed. The full kinematic functionality of struts and joints used is ensured. By coupling the FEM-model to the GNU Octave numerical program system, a variety of movements including machining forces can be simulated. A Broyden-Fletcher-Goldfarb-Shanno optimization algorithm, using GNU Octave, was written and coupled to the FEM-system. Now, this algorithm is able to influence the model's arbitrary input parameters during the optimization process. Thus, the model is optimized automatically for a certain machining process and/or dynamic behavior. This procedure is demonstrated using the example of a delta robot structure originally designed by Raymond Clavel [7].

Parallel structures are characterized by closed kinematics chains. Compared with conventional structures, which have serially arranged axes and are thus characterized by open kinematics chains, parallel structures have a variety of advantages. These primarily include lower moved mass, high dynamics, the high number of possible repeating parts, and the advantageous integration of measures in the control system that are relevant to accuracy. The following presentation classifies parallel structures with six degrees of freedom according to the type of drive used. Based on free-form surface machining in die and mold making, kinematics requirements will be explained and various concepts regarding kinematics, structure. loading, and stiffness will also be discussed. Finally, a preferable solution will be provided in the summary.