X-­ray nanotomography is an imaging technique used to study three­-dimensional structures at submicrometer length scale. The samples to be studied should be only 10s of micrometers in diameter, and ideally cylindrical. Focused ion beam milling is the most common technique used to prepare samples for nanotomography experiments, and the current technique used at the NanoMAX beamline of the MAX IV synchrotron facility. It is a time­-consuming process as preparing one sample can take hours. With the aim of offering a faster, alternative sample preparation method, a CNC milling setup was developed, and is presented here. The CNC setup is based on two spindles placed on precision linear stages - one for the sample and one for the milling tool. The sample is rotated while being trimmed gently by the milling tool, resulting in a small sample cylinder. A Python script generating G­-code commands controls the procedure. The setup was used to trim copper samples down to 18.5 micrometers. Further work is needed to optimize milling parameters in order to reach similar diameters for other sample materials. The developed setup offers a time-­efficient, repeatable and low­-cost sample preparation method for X-­ray nanotomography. / Nanotomografi med röntgenstrålning är en metod som används för att urskilja och studera tre­-dimensionella strukturer i material av storleksordningar mindre än 100 nanometer. Proverna behöver vara små; endast tiotals mikrometer i diameter. Det ideala provet för tomografi är cylindriskt. Fokuserad jonstrålefräsning är en vanlig metod för att preparera prover för nanotomografi, och är den metod som idag används på NanoMAX vid synkrotronljusanläggningen MAX IV. Det är en tidskrävande process; att förbereda ett enda prov tar flera timmar. Med avsikt att erbjuda en snabbare, alternativ metod för provpreparering så utvecklades ett CNC-styrt frässystem, som presenteras här. Systemet är baserat på två spolar placerade på precisionsplattformar - en för själva provet och en för fräsverktyget. Ett Python-program utvecklades som skickar kommandon i G-­kod och därigenom kontrollerar precisionsplattformarnas rörelser. Provet trimmas till en viss diameter, och slutresultatet är ett nästintill cylindriskt prov som kan användas för nanotomografi. Den minsta diameter som nåddes var 18.5 mikrometer, för ett kopparprov. Ytterligare arbete krävs för att optimera parametrar för fräsproceduren för andra typer av material, för att lika små diametrar ska kunna nås. Systemet som utvecklats erbjuder en snabb, billig och repeterbar metod för att förbereda prover för nanotomografi med röntgenstrålning.

This diploma thesis focuses on the design of new technology for the production of a given part by company Poppe + Potthoff s.r.o. The thesis is divided into three parts. The first part is a theoretical analysis of individual operations that are necessary for the production of the given part. The second part deals with the analysis of the current technological process of production. In the third part, a new technological process is proposed. In the end, there is a technical and economic evaluation performed, where both variants are compared.

The trends of rapid manufacturing (RM) have influenced numerous developments of technologies mainly in additive processes. However, the material compatibility and accuracy problems of additive techniques have limited the ability to manufacture end-user products. More established manufacturing methods such as Computer Numerical Controlled (CNC) machining can be adapted for RM under some circumstances. The use of a 3-axis CNC milling machine with an indexing device increases tool accessibility and overcomes most of the process constraints. However, more work is required to enhance the application of CNC for RM, and this thesis focuses on the improvement of roughing and finishing operations and the integration of cutting tools in CNC machining to make it viable for RM applications. The purpose of this research is to further adapt CNC machining to rapid manufacturing, and it is believed that implementing the suggested approaches will speed up production, enhance part quality and make the process more suitable for RM. A feasible approach to improving roughing operations is investigated through the adoption of different cutting orientations. Simulation analyses are performed to manipulate the values of the orientations and to generate estimated cutting times. An orientations set with minimum machining time is selected to execute roughing processes. Further development is carried out to integrate different tool geometries; flat and ball nose end mill in the finishing processes. A surface classification method is formulated to assist the integration and to define the cutting regions. To realise a rapid machining system, the advancement of Computer Aided Manufacturing (CAM) is exploited. This allows CNC process planning to be handled through customised programming codes. The findings from simulation studies are supported by the machining experiment results. First, roughing through four independent orientations minimized the cutting time and prevents any susceptibility to tool failure. Secondly, the integration of end mill tools improves surface quality of the machined parts. Lastly, the process planning programs manage to control the simulation analyses and construct machining operations effectively.

No description available.

In recent decades there has been a rapid development of technology in manufacturing industries, in particular through the increasing use of ever more powerful and sophisticated Computer Numerical Controlled (CNC) machines to manufacture complex parts. These machines are supported by a chain of computer based software solutions amongst which manufacturing information is exchanged. With the need for information exchange, interoperability between various computer-aided systems (CAx) has become an important research area. In CNC part programing, innovations by various hardware manufacturers and their reflection in their software have led to the necessity for the existence of different part programs for each machine. Creating these is a time consuming and economically inefficient activity. Implementing genuine interoperability between CNC machines is a way of eliminating this deficiency but, to achieve this, CNC programmers must be able to write a CNC program for a specific machine and effortlessly convert that program to work for other machines. The aim of this research was to enable the exchange of CNC programmes across machines with different technologies and demonstrate this between a C-axis CNC turn-mill machine and a 4-axis CNC machining centre. This has been achieved by designing a cross-technology interoperability framework that is capable of supporting systems that can work with the different types of CNC machines. This framework is the core contribution to knowledge from this PhD research. In order to fully identify the context for the research, this thesis presents a review of existing literature on machining of turn-mill parts and interoperability for CNC manufacturing. This is followed by the specification and realisation of a novel framework for cross-technology interoperability for CNC manufacturing. The demonstration is conducted using test components that can be manufactured using different CNC technologies.

Virtual manufacturing has gained considerable importance in the last decade. To obtain reliable predictions in a virtual environment, the factors that influence the outcome of a manufacturing operation need to be carefully modeled and integrated in a simulation platform. The dynamic behavior of the Computer Numerical Control (CNC) system, which has a profound influence on the final part geometry and tolerance integrity, is among these factors. Classical CNC drive identification techniques are usually time consuming and need to be performed by an engineer qualified in dynamics and control theory. These techniques require the servo loop or the trajectory interpolator to be disconnected in order to inject the necessary identification signals, causing downtime to the machine. Hence, these techniques are usually not practical for constructing virtual models of existing CNC machine tools in a manufacturing environment. This thesis presents an alternative strategy for constructing virtual drive models with minimal intervention and downtime to the machinery. The proposed technique, named “rapid identification”, consists of executing a short G-code experiment and collecting input/output data using the motion capture feature available on most CNC controllers. The data is then processed to reverse engineer the equivalent tracking and disturbance transfer functions and friction characteristics of the machine. It is shown that virtual drive models constructed this way can be used to predict the real machine’s contouring performance for large class of drive systems, controlled with different control techniques. In the proposed scheme, the excitation is delivered by smoothly interpolated motion commands. Hence, convergence of parameters to their true values is not guaranteed. When the real system contains pole-zero cancellations, namely due to feedforward control action, this also results in a loss of identifiability. In order to guarantee the stability of the identified drive models, the pole locations are constrained with frequency and damping ratio limits. Hence, the rapid identification task is cast as a constrained minimization problem. Two solution strategies have been developed. In the first approach, Lagrange Multipliers (LM) technique is applied, which yields successful estimation results. However, implementation of LM is computationally intensive and requires the use of a dedicated symbolic solver. This limits the portability for industrial implementation. In the second approach, a Genetic Algorithm (GA) search technique is developed, which is a more practical but slightly approximate alternative. The GA allows parameter bounds to be incorporated in a natural manner and converges to 2-3% vicinity of the LM solution in one-tenth of the computation time. The GA solution can be easily ported to different computation platforms. Both LM and GA identification techniques were validated in simulations and experiments conducted on virtual and real machine tool drives. It is shown that although the parameters estimated using the rapid identification scheme do not always match their true values, the key tracking and disturbance rejection characteristics of the drives are successfully captured in the frequency range of the CNC motion commands. Therefore, the drive models constructed with rapid identification can be used to predict the contouring accuracy of real machine tools in a virtual process planning environment. This thesis presents an alternative strategy for constructing virtual drive models with minimal intervention and downtime to the machinery. The proposed technique, named “rapid identification”, consists of executing a short G-code experiment and collecting input/output data using the motion capture feature available on most CNC controllers. The data is then processed to reverse engineer the equivalent tracking and disturbance transfer functions and friction characteristics of the machine. It is shown that virtual drive models constructed this way can be used to predict the real machine’s contouring performance for large class of drive systems, controlled with different control techniques. In the proposed scheme, the excitation is delivered by smoothly interpolated motion commands. Hence, convergence of parameters to their true values is not guaranteed. When the real system contains pole-zero cancellations, namely due to feedforward control action, this also results in a loss of identifiability. In order to guarantee the stability of the identified drive models, the pole locations are constrained with frequency and damping ratio limits. Hence, the rapid identification task is cast as a constrained minimization problem. Two solution strategies have been developed. In the first approach, Lagrange Multipliers (LM) technique is applied, which yields successful estimation results. However, implementation of LM is computationally intensive and requires the use of a dedicated symbolic solver. This limits the portability for industrial implementation. In the second approach, a Genetic Algorithm (GA) search technique is developed, which is a more practical but slightly approximate alternative. The GA allows parameter bounds to be incorporated in a natural manner and converges to 2-3% vicinity of the LM solution in one-tenth of the computation time. The GA solution can be easily ported to different computation platforms. Both LM and GA identification techniques were validated in simulations and experiments conducted on virtual and real machine tool drives. It is shown that although the parameters estimated using the rapid identification scheme do not always match their true values, the key tracking and disturbance rejection characteristics of the drives are successfully captured in the frequency range of the CNC motion commands. Therefore, the drive models constructed with rapid identification can be used to predict the contouring accuracy of real machine tools in a virtual process planning environment.

Virtual manufacturing has gained considerable importance in the last decade. To obtain reliable predictions in a virtual environment, the factors that influence the outcome of a manufacturing operation need to be carefully modeled and integrated in a simulation platform. The dynamic behavior of the Computer Numerical Control (CNC) system, which has a profound influence on the final part geometry and tolerance integrity, is among these factors. Classical CNC drive identification techniques are usually time consuming and need to be performed by an engineer qualified in dynamics and control theory. These techniques require the servo loop or the trajectory interpolator to be disconnected in order to inject the necessary identification signals, causing downtime to the machine. Hence, these techniques are usually not practical for constructing virtual models of existing CNC machine tools in a manufacturing environment. This thesis presents an alternative strategy for constructing virtual drive models with minimal intervention and downtime to the machinery. The proposed technique, named “rapid identification”, consists of executing a short G-code experiment and collecting input/output data using the motion capture feature available on most CNC controllers. The data is then processed to reverse engineer the equivalent tracking and disturbance transfer functions and friction characteristics of the machine. It is shown that virtual drive models constructed this way can be used to predict the real machine’s contouring performance for large class of drive systems, controlled with different control techniques. In the proposed scheme, the excitation is delivered by smoothly interpolated motion commands. Hence, convergence of parameters to their true values is not guaranteed. When the real system contains pole-zero cancellations, namely due to feedforward control action, this also results in a loss of identifiability. In order to guarantee the stability of the identified drive models, the pole locations are constrained with frequency and damping ratio limits. Hence, the rapid identification task is cast as a constrained minimization problem. Two solution strategies have been developed. In the first approach, Lagrange Multipliers (LM) technique is applied, which yields successful estimation results. However, implementation of LM is computationally intensive and requires the use of a dedicated symbolic solver. This limits the portability for industrial implementation. In the second approach, a Genetic Algorithm (GA) search technique is developed, which is a more practical but slightly approximate alternative. The GA allows parameter bounds to be incorporated in a natural manner and converges to 2-3% vicinity of the LM solution in one-tenth of the computation time. The GA solution can be easily ported to different computation platforms. Both LM and GA identification techniques were validated in simulations and experiments conducted on virtual and real machine tool drives. It is shown that although the parameters estimated using the rapid identification scheme do not always match their true values, the key tracking and disturbance rejection characteristics of the drives are successfully captured in the frequency range of the CNC motion commands. Therefore, the drive models constructed with rapid identification can be used to predict the contouring accuracy of real machine tools in a virtual process planning environment. This thesis presents an alternative strategy for constructing virtual drive models with minimal intervention and downtime to the machinery. The proposed technique, named “rapid identification”, consists of executing a short G-code experiment and collecting input/output data using the motion capture feature available on most CNC controllers. The data is then processed to reverse engineer the equivalent tracking and disturbance transfer functions and friction characteristics of the machine. It is shown that virtual drive models constructed this way can be used to predict the real machine’s contouring performance for large class of drive systems, controlled with different control techniques. In the proposed scheme, the excitation is delivered by smoothly interpolated motion commands. Hence, convergence of parameters to their true values is not guaranteed. When the real system contains pole-zero cancellations, namely due to feedforward control action, this also results in a loss of identifiability. In order to guarantee the stability of the identified drive models, the pole locations are constrained with frequency and damping ratio limits. Hence, the rapid identification task is cast as a constrained minimization problem. Two solution strategies have been developed. In the first approach, Lagrange Multipliers (LM) technique is applied, which yields successful estimation results. However, implementation of LM is computationally intensive and requires the use of a dedicated symbolic solver. This limits the portability for industrial implementation. In the second approach, a Genetic Algorithm (GA) search technique is developed, which is a more practical but slightly approximate alternative. The GA allows parameter bounds to be incorporated in a natural manner and converges to 2-3% vicinity of the LM solution in one-tenth of the computation time. The GA solution can be easily ported to different computation platforms. Both LM and GA identification techniques were validated in simulations and experiments conducted on virtual and real machine tool drives. It is shown that although the parameters estimated using the rapid identification scheme do not always match their true values, the key tracking and disturbance rejection characteristics of the drives are successfully captured in the frequency range of the CNC motion commands. Therefore, the drive models constructed with rapid identification can be used to predict the contouring accuracy of real machine tools in a virtual process planning environment.

Zugl.: Erlangen, Nürnberg, Univ., Diss., 2005

Zugl.: Aachen, Techn. Hochsch., Diss., 2007

Zugl.: Aachen, Techn. Hochsch., Diss., 2007.