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During machining occurring losses conduct into the machine tool and lead to deformations that im-pair the machining accuracy. Thus, high effort is invested into the compensation of thermo-elastic er-rors. One new approach is to use the information extractable from part programs in combination with CNC controller systems to forecast occurring losses in terms of a look ahead function. The method al-lows the evaluation of part programs with respect to its thermal influence on the machine tool. There-fore, the method is applied on selected part programs, resulting loss distributions are discussed in terms of their thermal impact, and potential follow-up strategies are stated.

To control the thermal behavior of machine tools, numerous measures are used today. With the help of thermal machine models, well-founded predictions can be made for the selection and design of these measures. For creation and simulation of thermo-elastic models of structural components and a high effort is still necessary. In the paper, a procedure is presented to reduce this effort. A combination of existing methods and new approaches is used. This includes methods of mathematical model order reduction to reduce computing costs and robust mesh algorithms that process even slightly defective geometries.

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.

Interoperabilität ist der Kern von Industrie 4.0. Mit Standards wie der Verwaltungsschale und OPC UA lassen sich bereits Komponenten semantisch beschreiben und einheitlich steuern. Für selbstoptimierende Produktionssysteme muss zusätzliches Wissen aus weiteren Datenquellen maschinenlesbar abgebildet und flexibel durch intelligente, autonome Systeme abgefragt werden. Die Linked Factory liefert dafür eine passende Technologie und schafft semantische Interoperabilität auf dem Shopfloor. Interoperability is a core idea of Industry 4.0. For this purpose, components can already be semantically described and uniformly controlled via standards such as the Asset Administration Shell and OPC UA. To enable self-optimizing production systems, more knowledge from further data sources must be attached in a machine-readable form, and queried flexibly by intelligent, autonomous systems. The Linked Factory provides a suitable technology for this and establishes semantic interoperability on the shopfloor.

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.

Zusammenfassung Aktuelle Entwicklungen im Bausektor widmen sich dem Carbonbeton, der als neuer Baustoff den bisherigen Stahlbeton ergänzen soll, um insbesondere Ressourcen zu schonen. Bislang wurden umfangreiche werkstoffliche und fertigungstechnologische Grundlagenuntersuchungen angestellt, um das Know-how zum Materialdesign und zu Herstellverfahren zu erlangen. Gegenwärtige Arbeiten zielen auf das Erreichen der qualitätsgerechten Prozesssicherheit der Herstellverfahren – als Voraussetzung für das Ausschöpfen der Vorteile der neuartigen Baustoffe sowie deren wirtschaftliche Produktion. Um das dafür notwendige Verständnis über die Prozesszusammenhänge zu erlangen, sind aufgrund der vielfältigen Einflüsse auf die Eigenschaften der Produkte umfangreiche und komplexe Untersuchungen erforder- lich. Dieser Beitrag stellt eine für die Baubranche neuartige, ganzheitliche Vorgehensweise vor, die zunächst eine effizientere Verfahrensentwicklung ermöglicht und die schließlich als Grundlage für die Qualitätssicherung in industriellen Serienprozessen dienen kann. Da die Herstellung auf maschinellen Prozessen basiert, integriert die Vorgehensweise datengetriebene Methoden, um die Daten aus Maschinensteuerung und Qualitätssicherung nutzen zu können. Illustriert werden die datengetriebenen Methoden am Prozess zur Herstellung der textilen Bewehrung.

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.

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.

The determination of the thermal-elastic behavior is one of the main aspects in the design phase of new machine frames. Prototypically simulation models are used for preliminary investigations, which are based on finite element approaches and usually work with simplified material laws. By the manufacturing of machine frames of concrete steel reinforcements are used to ensure the operation reliability due to the high sensitivity of concrete to tensile stresses. Because of different thermal conductivity and specific heat capacity of steel and concrete the reinforcement has a not negligible influence on the total thermal behavior of the system, which cannot be covered with conventional material laws, e.g. from material libraries. Preliminary investigations show, that a volume fraction more than 1 % of the reinforcement of the total volume can cause a relative error up to ten percent in the temperature field. To reflect the real behavior of reinforced concrete for a machine bed, the influence should be exanimated for two different approaches. Next to the real illustration of the geometry of the reinforcement in the FE-model, decoupled simulation approaches are used on reduced models, which should approach numerically the material behavior of the reinforcement.

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.

Nowadays, feed axes are often equipped with multiple parallel-acting actuators in order to increase the dynamics of the machine tool. Also, additional actuators for active damping are widely used. Normally, the drives or actuators are controlled independently without consideration for the impact on each other. In contrast, by using the modal space control, the system can be decoupled and the modal control loops can be adjusted independently. This control approach is particularly suitable for motion systems, such as machine tools, which have more drives or actuators than degrees of freedom of movement. This paper deals with the pre-investigation of the modal-based vibration control for machine tools with additional actuators. The object of investigation is an elastic system with a movable saddle. The modal-based control is compared with a local control approach. The results obtained experimentally on the test rig are presented. The modal control is superior since, with the modal approach, each control loop corresponds to a specific vibration mode, and the control law for this loop is designed to provide the desired performance of the control system at the corresponding resonance frequency. The parameterisation of the control loops is simplified by modal control, since the modes can be controlled independently.

Growing power densities in the main spindle of machine tools caused by approaches to increase the productivity lead to deflections of the tool-center-point (TCP) and thus limit the achievable accuracy. In order to optimize the operating point of a spindle in the context of these contrary requirements, effective tools need to be developed. By modeling thermo-elastic effects using a FE approach, interactions of heat sources and sinks can be investigated deeply on the one hand and a model based correction of TCP deflections can be established on the other hand. For a practical use, short computing times as well as precise simulation results are core requirements. In the present paper, an online thermal simulation model is developed, parameterized and validated.

Within 200 years since the industrial revolution manufacturing systems have often changed their faces. Emerging nations, new markets, new inventions and the changing needs of the society forced them to adapt. Until today, the arising challenges are immensely diverse: highly individualized products, decreasing manufacturing time, limited resources and critical ecological footprints are only a few of them. Oftentimes solutions for these issues and other future requirements can be found by interrogating nature. Applying knowledge of biological principles to industrial manufacturing processes is recently referred to as „biological transformation of manufacturing systems“. Hereby three levels of a biological transformation are introduced, starting from inspiration over integration to the interaction of biological and technical systems. The paper illustrates the idea of biological transformation with specific examples for each level. On the inspiration-level the design of manufacturing systems with elements of natural ecosystems is described. Thus flexibility is increased, material cycles are closed and waste will be reduced. Furthermore the integration-level is illustrated by the use of microorganisms in cutting fluids. Finally, evolutionary computing within an automatic joining cell is shown as an example for the interaction-level.

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.

Nowadays, the thermo-energetic design of a machine tool also includes the thermal stabilization of its machine components. In the past, thermal stability was irrefutable connected to minimizing the temperature gradient of machine tools by air conditioning the entire machine or even the factory hall. Today, thermal stability also defines minimal inhomogeneities in the temperature field of the machine tool due to higher energy efficiency requirements. Fluidic tempering systems of machine components offer considerable potential concerning the minimization of thermo-elastic displacements with acceptable energy demand. Hence, intelligent algorithms are required to combine tolerable geometric deviations with minimal energy effort. The scope of this paper is the integration of a demand-oriented fluidic temperature control system into a machine bed. The resulting multi-input-multi-output (MIMO) systems and varying boundary conditions are challenges, which are addressed. Therefore the paper compares two control approaches, a decentralized single-loop control and a multi-loop control by decoupling the control loops and especially focus on the distribution of the jointly used actuating variable, combined with the variation of different boundary conditions.

Part complexity increase the ratio of manual tasks on machine tools, i.e. feeding, to maintain high productivity. Mobile robot cells automate these tasks but require cost-intensive retrofittings for additional interfaces. This paper focuses on a new mobile robot cell without any mechanical or electrical connection to the machine tool. Therefore, the solution uses a camera system for wireless machine tool localization and signal transmission.

In this paper, we use the blockchain technology to design a prototype to secure process data from a 3D-printer. Datastreams are gathered from various sources such as OPC UA servers and autonomous retrofit sensor nodes. This is followed by pre-processing for data reduction, storage in a data model, and the generation of a unique hash value over it. The hash values are stored in a blockchain using appropriate consensus methods, taking into account their temporal origin and production identification number. This also includes the context-related influence of sensor signals on the production process Restrictive access regulations using smart contracts make a partially or fully automated machine tool calibration possible. In this context, we show to realize a process partial or full automation through smart contracts. Physical machine tools and virtual simulations are integrated into the block-chain network to document the stability and performance.

The paper presents a framework for considering resilience as an integrated aspect in the design of manufacturing systems. The framework comprises methods for the assessment of resilience, supply chain and production planning, flexible execution and control as well as modular and skill-based methods for automation systems. A basic classification of risk categories and their impacts on manufacturing environments is given so that a concept of reconfigurable and robust production systems can be derived. Based on this, main characteristics and concepts of resilience are applied to manufacturing systems. As a lever of increased resilience on business and supply chain level, options for synchronized production planning are presented in a discrete event simulation. Furthermore, a concept to increase resilience on the level of business process execution is investigated, allowing manufacturing tasks to be rescheduled during runtime using a declarative approach to amend conventional business process models.

Combining fiber-reinforced composites with classic construction materials has tremendous potential for lightweight design but requires expensive equipment such as dies for injection molding. Additive manufacturing is cost-efficient for small volumes from fiber-reinforced materials. The MM3D (multi-material 3D printing) project analyses what technologies regarding interfaces, printer technology and identification of process parameters make economic fusion of 3D printing and established production processes possible.

Thermo-elastic errors in machine tools have a significant influence on the machining accuracy. The error at the tool center point can be determined with the structure model based correction, which uses physical based models like finite element models. The output of the structure model is the volumetric thermo-elastic error in the workspace of the machine tool. The correction values of the machine axes are determined based on the volumetric error with the help of a kinematic model. Therefore, three variants are presented in this article and their influence on the correction accuracy is estimated. The evaluation is based on typical thermo-elastic errors in the workspace of machine tools. The influence of the different variants on the correction accuracy is estimated with the Monte Carlo method based on randomly generated errors in the workspace of the machine.

In this paper, we present a measuring approach to assessing the volumetric accuracy of machine tools or kinematics with more than 3 interpolating feed axes. It is based on an inexpensive Double Ballbar and suitable for fast performance-tests in an operational state of the machine. The novelty is the recording of large amounts of data in a very short period while continuously moving along measuring paths with up to 6DoF. In order to facilitate this measuring approach, we developed a Double Ballbar with extended measuring range based on an optical sensor and a series of systematic methods, like kinematic error modeling, optimal design of 6D measuring paths as well as post-processing and evaluation of the captured data. We demonstrate that our approach is universally applicable to different types of kinematic structures such as 5-axis machine tools, industrial robots or even parallel kinematics. By experiments, we will also show that the measured data are essentially more informative than those of standardized circular DBB tests are. Moreover, the measuring procedure is automatable and to be carried out under production conditions in the workshop, what makes it practically applicable in various evaluation and decision scenarios.

In a global sales market with networked production steps and increasing complex machine tools, scaling service ecosystems for production provide an adequate solution for handling the generated data. The existing sensor equipment at current and the extension possibility by the System-of-Systems approach for existing machine tools can offer value-added services by the smart handling of production-related data. It is important to make these data validatable and exchangeable, taking into account to different protection goals. The trust of the individual actors in such a volatile value chain and the different (partly cross-border) value creation partners play an important role. The participation of a large number of these actors creates an attractive overall system (ecosystem) with lots of services and network effects. Concerning data security there are numerous aspects, which have not been adequately answered or taken into account in the use of a service ecosystem in the production environment. The paper discusses a distributed ecosystem for production on a distributed ledger-based service ecosystem, in which services can be mapped in the machine tool environment (e.g. calibration). This technology can be used for secure data exchange in order to discuss traceability and unchangeability of data while maintaining data sovereignty.

One of the main errors in the machining accuracy of machine tools is the displacement through thermal induced deformation. Modern design and construction methods aim to optimize the heat flow in the machine to achieve minimum displacement. To enable a further improvement it is essential to know the displacement state of the complete machine structure. However, most measurement methods that are used to capture the influence of a thermal load only measure the displacement of the TCP or individual axes. This paper presents a methodology to capture the complex spatial displacement condition of a state of the art machine tool in one measuring cycle using a multichannel laser interferometer. It describes the development of the measurement model as well as the measurement setup in the workspace of the machine. With measurements according to the presented procedure, it is possible to uncover weak points in the structure of a machine tool and to derive warm-up and cooling strategies.

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.

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.

With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

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.

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.

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.

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.

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.

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.

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.

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.