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The mechatronic design process usually covers two different engineering domains: structure design and system control. The relationship between these two domains is very tight. In order to reduce the disturbance caused by parameters in either one, the domain knowledge from those two different fields needs to be integrated. So the technique of multil...
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... a better product in a shorter period of time. The design of aircraft, spacecraft, automobiles, robots and others demands a methodology that is more sophisticated and efficient than traditional serial design approaches. This type of approaches is characterized by a sequential design cycle, with respect to participating design groups. As shown in Fig. 1, the basic classical steps of a typical mechatronics design process can be outlined as follows: 1) The structure engineer designs a mechanical structure by primarily addressing the structural objectives, such as the overall mass and dimensions of the structure, stress requirements, and characteristic quantities such as natural ...
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... the Y axis of the feed drive system, the parameter t M represents the overall mass of the moving parts, which include the table mass and the saddle mass, and the shape of the saddle for the first design result is shown in Fig. 10(a). The mechanical characteristics of the saddle can be found by the approach of FEM. The value of t M directly affects the physical properties of the saddle. The saddle is therefore modified to a less weight model as depicted in Fig. 10(b) without reducing the structural natural frequency. As a result, the mass of saddle can be decreased ...
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... the table mass and the saddle mass, and the shape of the saddle for the first design result is shown in Fig. 10(a). The mechanical characteristics of the saddle can be found by the approach of FEM. The value of t M directly affects the physical properties of the saddle. The saddle is therefore modified to a less weight model as depicted in Fig. 10(b) without reducing the structural natural frequency. As a result, the mass of saddle can be decreased to 25 Kg and the first resonance frequency of the saddle is increased to 513 Hz from 490 Hz. After the saddle of the feed drive system modification, a set of new controller parameters can be found by employing (10), (11) and (12). The ...
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... frequency. As a result, the mass of saddle can be decreased to 25 Kg and the first resonance frequency of the saddle is increased to 513 Hz from 490 Hz. After the saddle of the feed drive system modification, a set of new controller parameters can be found by employing (10), (11) and (12). The final design parameters are also listed in Table 2. Fig. 11 indicates, that bandwidth of the position loop is increased from 12.5 Hz up to 15.3 Hz and satisfies design requirement. In addition, from Fig. 12, tracking time of the position loop is also decreased to 0.04 sec from 0.06 sec. ...
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... 490 Hz. After the saddle of the feed drive system modification, a set of new controller parameters can be found by employing (10), (11) and (12). The final design parameters are also listed in Table 2. Fig. 11 indicates, that bandwidth of the position loop is increased from 12.5 Hz up to 15.3 Hz and satisfies design requirement. In addition, from Fig. 12, tracking time of the position loop is also decreased to 0.04 sec from 0.06 sec. ...
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Citations
... Kim et al. [24][25][26][27] employed nonlinear multi-objective optimization methods for the matching design of the mechanical structure and control system of feed systems. Such optimization-based design methods [28][29][30][31][32][33] consider the coupling effects between mechanical and control systems but do not fully account for the integrated impact of motion processes, control systems, motors, and mechanical structures. The underlying mechanisms of subsystem coupling and their comprehensive influence on the dynamic performance of the feed system remain unclear. ...
The feed system of CNC machine tools is critical in determining machining accuracy. Current design methods often utilize lightweight and structural optimization to enhance natural frequencies or restrict the load-to-inertia ratio to ensure dynamic performance. However, it is challenging to determine whether these methods can meet accuracy requirements during the design phase. This paper proposes using position and velocity errors as design criteria to meet accuracy requirements during the design phase. However, the feed system of CNC machine tools is influenced by various subsystems. Still, comprehensive research on the coupling effects of these subsystems and their integrated impact on dynamic performance is currently lacking. This paper addresses this issue by comprehensively studying the integrated effects of various subsystems on the dynamic performance of the feed system. It proposes comprehensive optimization design criteria for the dynamic design of the feed system of CNC machine tools. First, the influence of the control system is isolated using intelligent optimization algorithms. Then, the influence mechanisms of motor and mechanical structure parameters on the dynamic performance of the feed system under different motion processes are studied. Finally, based on the integrated effects of coupling between subsystems on feed system performance, a comprehensive optimization design criteria for the feed system of CNC machine tools considering the coupling effects of various subsystems is proposed. This paper provides a theoretical basis for the dynamic design of machine tool feed systems.
... Nonetheless, such design procedures are often performed sequentially or iteratively [5], where the hardware is designed first and the control software afterwards [6]. Consequently, this yields sub-optimal designs [7] and update cycles with slow incremental improvements with respect to the final products. ...
Due to ever increasing performance requirements, model-based optimization and control strategies are increasingly being adopted by machine builders and automotive companies. However, this demands an increase in modelling effort and a growing knowledge of optimization techniques, as a sufficient level of detail is required in order to evaluate certain performance characteristics. Modelling tools such as MATLAB Simscape have been created to reduce this modelling effort, allowing for greater model complexity and fidelity. Unfortunately, this tool cannot be used with high-performance gradient-based optimization algorithms due to obfuscation of the underlying model equations. In this work, an optimization toolchain is presented that efficiently interfaces with MATLAB Simscape to reduce user effort and the necessary skill and computation time required for the optimization of high-fidelity drivetrain models. The toolchain is illustrated on an industrially relevant conjugate cam-follower system, which is modelled in the Simscape environment and validated with respect to a higher-fidelity modeling technique, namely, the finite element method (FEM).
... A design optimization method is presented in [139] that is divided into two stages-structure design process and control design. For structural design, a CAD modeling tool (namely Pro/ENGINEER) was employed and 3D models designed, and the AnSys which delivers product modeling solutions was used to design the structure. ...
... Unlike the studies presented in [139,150] which are based on sequential design approaches, the method presented in this thesis is a simultaneous design method where the design parameters, constraints, objectives from different disciplines, dynamics, statics and physical dimension models are all considered and evaluated simultaneously. Since the method in this thesis is developed based on individual components rather than considering a model in a system level, it allows more flexibility for design and configuration of systems. ...
... State feedback with Kalman filter state observer is the basis for the pole placement controller in [2]. The drive control parameters are determined from the multibody mechanical model representation in [3] to reduce the system vibrations. Tuning of cross coupling controller for the machine tool drives extends the stochastic linear quadratic Gaussian regulator, and it combines both the drive and the cutting dynamics into a unified model in [4]. ...
The paper deals with the creation and implementation of a methodology for optimizing the parameters of cascade control of the machine tool axis drives. The first part presents the identification of a dynamic model of the axis based on experimental data from measuring the axis dynamics. The second part describes the controller model, selection of optimization objective functions, and optimization of constraint conditions. The optimization of controllers is tuned by simulation using identified state-space model. Subsequently, the optimization procedure is implemented on the identified model, and the found control parameters are used on a real machine tool linear axis with different loads. The implementation of the proposed complex procedure on a real horizontal machine tool proved the advantage of simultaneous tuning of all parameters using optimization methods. The strategy solves the problem of mutual interaction of all control law parameters disabling effective usability of gradual sequential tuning. The methodology was developed on a speed control loop, the tuning of which is usually the most difficult due to the close interaction with the dynamic properties of the machine mechanics. The whole procedure is also applicable to the position and current control loop.
... This co-designing of hardware architecture and control configuration has only been applied to a limited extent. Examples of the co-designing of geometric properties and controller tuning parameters for a four-bar system (using a Genetic Algorithm), parallel robots (using a differential evolution), and machine tools (using a multilevel decomposition) can be found in [2][3][4], respectively. Similarly, co-designing of controller tuning through the use of a Linear Quadratic Regulator (LQR) method and the geometrical properties of a pendulum system, a satellite altitude control, and an angular motor controlling a mass position was detailed in [5][6][7], respectively. ...
For complex systems, it is not easy to obtain optimal designs for the hardware architecture and control configurations. Every design aspect influences the final performance, and often the interactions of the different components cannot be clearly determined in advance. In this work, a novel co-design optimization method was applied that allows the optimal placement and selection of actuators and sensors to be performed simultaneously with the determination of the control architecture and associated controller tuning parameters. This novel co-design method was applied to a state-space model of a downscaled active car suspension laboratory setup. This setup mimics a car driving over a specific road surface while active components in the suspension have to increase the driver’s comfort by counteracting unwanted vibrations. The result of this co-design optimization methodology is a Pareto front that graphically represents the trade-off between the maximum performance and the total implementation cost; the co-design results were validated with measurements of the physical active car suspension setup. The obtained controller tuning parameters are compared herein with existing controller tuning methods to demonstrate that the co-design method is able to determine optimal controller tuning parameters.
... The complete system design consists of many different interacting hardware and control design possibilities that each have an (unknown) influence on the overall system performance. In conventional development strategies, the hardware and control designs are treated sequentially and are therefore approached entirely separated [29]. This results in a so-called 'industrial design gap' between hardware engineering on the one hand and control engineering on the other hand. ...
... The result is an optimization of the design variables of a hard disc suspension geometry and the controller feedback gains through the use of the linear-quadratic regulator (LQR) method. Similarly, the co-design of the controller tuning through the use of a linear-quadratic regulator (LQR) method and the geometrical properties of machine tools, an active vehicle suspension system, a satellite altitude control, an angular motor controlling a mass position, and a parallel manipulator mechanism was detailed in [29,32,33,49,76], respectively. ...
... It will be up to the design engineer to make a final decision on which Pareto point is most appropriate for the specific application. However, without further information, Pareto point 8 with an associated cost of 43.2% and a fitness value of 29.05 seems the most interesting because the fitness value improves relatively little when more cost is allowed while reducing the implementation cost indicates a significant reduction in performance. ...
Het ontwerp van hedendaagse, complexe machines wordt veelal uitgevoerd door verscheidene ingenieursteams die elk een uiteenlopend aspect van dit multidisciplinair systeemontwerp op zich nemen. Het probleem daarbij is dat deze verschillende ingenieurs het ontwerpvraagstuk vanuit een andere invalshoek bekijken en daarbij ook andere doelstellingen voor ogen hebben, terwijl hun individuele keuzes een grote invloed kunnen hebben op de te behalen systeemprestaties. In dit doctoraat wordt een methode voorgesteld om de gelijktijdige optimalisatie uit te voeren van het mechanisch ontwerp en het regeltechnisch ontwerp van systemen die bestaan uit verschillende interagerende subsystemen. Met behulp van deze methode kan het co-ontwerp als een mathematisch optimalisatieprobleem geformuleerd worden, waarbij ook enkele uitbreidingen beschouwd worden om deze optimalisatie rekenkundig efficiënt te kunnen uitvoeren. De flexibiliteit en effectiviteit van de ontwikkelde methode worden aangetoond door deze toe te passen op verschillende types van modellen en opstellingen. De resultaten tonen aan dat een ontwerpingenieur met de voorgestelde werkwijze in staat is om het mechanisch en regeltechnisch co-ontwerp met succes uit te voeren. Daarbij voorziet de resulterende Pareto-analyse een heel waardevol inzicht in hoe de maximaal te behalen systeemprestaties zich verhouden tot de totale implementatiekost om een optimaal systeemontwerp te bekomen.
... Haemers). from each other [6] . First, mechanical engineers design a physical setup, corresponding with the structural objectives on, for instance, weight, inertia or strength. ...
... The result is an optimization of the design variables of a hard disc suspension geometry and the controller feedback gains through the use of the Linear Quadratic Regulator (LQR) method. Similarly, the codesign of control parameters and the geometrical properties of machine tools (using a multilevel decomposition), parallel robots (using a differential evolution algorithm) and a four-bar system (using a Genetic Algorithm) was detailed in [6] , [9] and [10] , respectively. A design method for the sequential optimization of the mechanical structure and the control parameters for a two-link high-speed robot is developed in [11] . ...
... In this way, the basic state-space representation from (1) is extended to (6) . Every sensor and actuator placement binary is a design parameter for the optimization procedure, as explained later in the paper. ...
In plants consisting of multiple interacting subsystems, the decision on how to optimally select and place actuators and sensors and the accompanying question on how to control the overall plant is a challenging task. Since the impact of sensor and actuator placement on performance is not theoretically described, an optimization method exploring the possible configurations is needed to find a trade-off between implementation cost and achievable performance. In this paper, a novel model-based procedure is presented to simultaneously co-design the optimal number, type and location of actuators and sensors and to determine the corresponding optimal control architecture and accompanying control parameters. This is in contrast to earlier work that addresses the co-design between control configuration and hardware architecture, but without taking into account changing control architectures. As an optimization output, a Pareto front is presented, providing insights on the optimal total plant performance related to the hardware and control design implementation cost. The proposed algorithm is not focused on one particular application or a specific optimization problem, but is instead a generally applicable method and can be applied to a wide range of applications (e.g., mechatronic, electrical, thermal). In this paper, the co-design approach is validated on a mechanical setup.
... Another example for the application of modal analysis in industry is control systems. The design of a control system for a machine tool axis is an integration between the controller characteristics and the mechanical part properties [9]. High positioning accuracy is defined in a control system by the bandwidth of the servo control. ...
Realization of modal analysis in a wide spectrum of applications has
been established since decades. The dynamic characteristics obtained
from analytical and experimental modal analysis helped researchers
as well as engineers to determine the dynamic behavior of structures
and assemblies. Extensive research work is given to the detection and
evaluation of damages in mechanical structures using knowledge of
modal frequencies and mode shapes. The fact that the dynamic characteristics
thus obtained, are influenced by the structure’s physical
and mechanical properties has inspired many researchers, recently to
use modal analysis as a tool for the identification of mechanical properties
and remaining life prediction of manufactured components.
It is not the intention of the authors in this paper to investigate the
knowhow of each of the applications mentioned within the scope of
the paper; since the reader can obtain detailed knowledge of most
of the applications from the respective references written at the end
of this paper. The main objective of this paper, is to summarize
different important applications of modal analysis and classify these
applications into three main categories; quality assessment, material
characterization, and life prediction. The work also describes
what has been achieved in each category and provides an insight for
researchers on what still needs to be achieved. Accordingly; the authors
introduce a new application that will revolutionize the basis on
which engineering design is traditionally used, by the fact that it is
better testing the product instead of testing the material from which
the product will be made. This new philosophy is currently worked
upon by the authors.
... Furthermore, traditional methods for mechatronics design are often based on a sequential approach, where the mechanical structure is designed first, and then fitted with off-the-shelf electric motors, drive electronics, and sensors. Finally, a control system is designed and optimized for the already existing mechanical system [7,8]. Such a design method, that does not consider aspects from a control point of view during the design of the mechanical system, is unlikely to result in a system with optimal control performance. ...
The mechanical structure has a main influence of the machining performance and the servo performance. In this study, a mechanical structure-based design method is presented to design and optimize an ultraprecision fly-cutting machine tool. This method takes full account of the influence of mechanical components on the machining performance and servo performance at the design stage. The effect of the components structure on the roughness of machined surface is discussed, and an optimized structural form of the aerostatic spindle is given. The influence of the mechanical structure on the control system and electronic drives is discussed, and an integrated dynamic design model is built and used to optimize the hydrostatic slide. Furthermore, the impact of mechanical system dynamic performance of the machine tool on the processing topography is analyzed by the finite element model of the machine tool. This method provides a theoretical basis for the design and optimization of mechanical components and machine tools stiffness loop.
... Thus, many efforts for the modeling and design of feed drive system components are conducted in order to achieve the required accuracy [1], [2]. Integrated design approaches of feed drive control and mechanical subsystems have been made [3], [4] in order to optimize the whole system parameters for higher machine tool performance. However, the real system is modeled as a gray box and only its basic parameters are used for simulation; therefore, the simulation model is simpler than the system which leads to undesired differences between analytical and measurement results. ...
Simulation models are used to study and design real systems in order to optimize their performance. However, the lack of complexity in models, as compared to systems, leads to differences between simulation and actual behavior. Identification of the effective system parameters in the simulation models would reduce the discrepancies between the model and the real system and results in better simulation of system behavior. In this paper, radial basis neural networks are proposed for the identification of the effective parameters of the machine tool feed drive system. Neural networks are first trained with an estimated set of model parameter values and corresponding step responses of the position control loop. After the training period, the response of the model with the identified parameters is compared to the system step position response at different axis positions for validation. An application of neural network to other trajectories is done on a ramp function. The obtained results reveal considerable potential of neural networks in identifying accurately the system parameters and in reducing the discrepancies between experimental test results and the model.
