Intelligent Digital Design Technology for Smart Machine Tools
Intelligent and Digital Design Technologies for Machine Tools
In recent years, the global manufacturing industry has faced severe disruptions caused by geopolitical tensions between the United States and China, supply chain shocks from the COVID-19 pandemic, and the energy crisis triggered by the Russia–Ukraine war. Under these pressures, Taiwan’s machine tool industry has encountered unprecedented challenges. This paper explores how smart and digital upgrading strategies, with a focus on customized development of high-end special-purpose machines, can strengthen the technological capabilities of Taiwan’s machine tool sector. The key technologies discussed include optimized design of transmission systems, lightweight structural design, and integrated response design of servo control and structural dynamics.
The transmission system design emphasizes parameter matching among ball screws, motors, and bearings to balance performance and cost. Lightweight design introduces topology optimization and emerging composite materials to achieve energy saving and green manufacturing. The integrated servo–structure response design combines structural and control systems to enhance mechatronic analysis and meet machining accuracy requirements. This study highlights the critical role of intelligent and digital upgrading in enhancing R&D capability, with the aim of reinforcing Taiwan’s competitiveness in the global machine tool market and promoting sustainable industrial development.
Ball Screw Feed Drive System Design
The ball screw feed drive system plays a critical role in machine tools. Its primary task is to drive and accurately position moving components, ensuring precise alignment and feed motion between the cutting tool and the workpiece. Machining accuracy and efficiency are highly dependent on its performance. In Taiwan, most machine tools adopt feed systems based on ball screws, typically consisting of a servo motor, coupling, optional gearbox, bearings, and a ball screw–nut assembly, combined with linear guideways. This configuration offers high precision, high back-drivability, and high efficiency, and is widely used in precision equipment for IT, semiconductor, and medical applications.
The first step in drive system design is to define the duty cycle according to actual machining tasks, including load and speed data. These data can be obtained from theoretical modeling or from real machining process signals. Key indicators such as maximum load, average load, and corresponding speeds are extracted as the basis for screw and motor selection. Critical design parameters include the screw’s critical speed, axial stiffness, buckling load, and nut life, which require precise calculation to ensure system stability and reliability [1]. In motor selection, the required motor speed is determined from the feed rate and screw lead, while peak torque during acceleration and deceleration and load inertia must also be considered. At the same time, the inertia ratio between motor and system should be maintained within the recommended range of 1:3 to avoid control instability and vibration. If the inertia ratio is too high, a reduction gearbox can be introduced to effectively reduce the dynamic impact of the load on the motor.
To simplify the design process and improve accuracy, this study developed an intelligent transmission system design module that integrates calculation formulas and component selection logic. The module automatically reads machining process data, analyzes load and speed information, and guides users in selecting components such as screws and motors. The system then automatically calculates all key performance indicators and checks them against allowable limits. If specifications are not met, red warning messages are displayed and optimization suggestions are provided. This approach enables designers to complete optimal matching at an early stage, ensuring a balance between performance and cost. This method not only improves design efficiency but also embodies the core concept of intelligent machine tool design, laying a solid foundation for Taiwan’s high-end customized machine tool applications.
Lightweight Structural Design Technology
To achieve the goal of green manufacturing, lightweight design of machine tool structures has become a key direction of technological development. According to statistics, more than 90% of the carbon emissions over the life cycle of a machine tool come from energy consumption during the usage stage, mainly due to power consumption in metal cutting processes. Therefore, reducing machine weight, lowering moving inertia, and decreasing driving energy consumption through structural design, in addition to improving peripheral components, has become an effective strategy to enhance energy efficiency.
Traditional structural components such as beds, columns, crossbeams, and headstocks are mostly made of metallic materials and account for more than 70% of the total machine weight. To achieve lightweighting, this study proposes two main technical paths: geometric structure optimization and the introduction of emerging lightweight materials. Topology optimization represents a typical geometric optimization method. Through computational analysis under specified load and boundary conditions, it automatically generates an optimal material distribution. By evaluating element density distribution, unnecessary material can be removed while stiffness efficiency is significantly improved.
With regard to new materials, composite materials (such as carbon fiber and mineral cast) have become key breakthroughs in machine tool lightweighting due to their high specific strength, high damping, thermal stability, and low manufacturing energy consumption. In global application trends, carbon fiber composites and mineral cast structures are emerging as two major directions for lightweight and highly stable designs. At EMO 2023 in Germany, FOOKE presented the ENDURA® 700LINEAR equipped with a carbon-fiber spindle head, achieving more than 25% weight reduction and embedding sensors for health monitoring, successfully improving positioning accuracy and thermal stability [2]. At the 2025 Beijing exhibition, Shanghai Topu CNC also introduced a gantry machining center with a fully composite structure, using large-area carbon fiber to reduce moving inertia and enhance damping, thereby improving stability during high-speed and long-duration cutting. On the other hand, mineral cast structures have attracted increasing attention for their high damping, low thermal conductivity, and excellent stability. At JIMTOF 2024, MAZAK presented the VCN-460 HDCC vertical machining center with a mineral cast base, effectively suppressing vibration and thermal drift and improving machining consistency. Similar machines were exhibited by several Chinese manufacturers at the 2025 Beijing show, reflecting the growing importance of mineral cast as a structural material for medium- and large-sized machine tool upgrades.
In this study, hybrid structures combining metal frames and carbon fiber composites were also developed for headstock design. With the aid of CAE simulation, a 20% weight reduction was achieved while meeting stiffness requirements. The first bending mode natural frequency increased by 91% (from 170 Hz to 326 Hz), and the damping ratio increased by three times (from 0.165% to 0.513%), demonstrating excellent mechanical properties. Through modular design and production line implementation, this study verifies that lightweight structures not only reduce energy consumption and improve dynamic response and control accuracy, but also have strong potential for scalable industrial application, laying an energy-efficient foundation for next-generation smart machine tools.
Integrated Servo Control and Structural Response Design Technology
As a typical mechatronic system, machine tools require close integration between structural design and control performance. Traditional designs have focused mainly on static and dynamic stiffness. However, with increasing demands for high-end machining, the integration of servo control systems and structural response has become the key to improving overall system performance. Studies by Zaeh et al. [3], Kim et al. [4], and Van Brussel et al. [5] have combined finite element analysis, multibody dynamics simulation, and control software to simulate the dynamic characteristics of machine tools.
This paper proposes a digital modeling framework that integrates servo control and structural response. At the early stage of machine design, coupling analysis between control models and mechanical models can be performed to improve the accuracy of predicting dynamic behavior under real machining conditions.
This technology establishes equivalent dynamic response models of mechanical structures through CAE, including stiffness, damping, and mass parameters, and connects them with servo control loops (position, velocity, and current control). By simulating the influence of servo commands on motion accuracy during real machining, designers can analyze system responses in time and frequency domains under different acceleration, servo gains, and control filter settings, and further conduct structural reinforcement or parameter optimization. For example, excessive feed acceleration may excite structural resonance and dynamic errors. Through simulation, performance bottlenecks can be identified in advance, avoiding prototype failures and repeated design iterations that increase development cost.
This approach also supports the development of machines that have not yet been physically manufactured. Structural response functions can be established in advance based on CAD models, enabling early-stage optimization of servo–structure matching. Compared with the servo parameter simulation function provided by FANUC CNC GUIDE 2 (2022) [6], the proposed model can not only process feedback signals of existing machines but also integrate CAE-based simulations for machines under development, providing greater flexibility for highly customized machine designs. Overall, integrated digital analysis of control and structure can significantly shorten trial machining and tuning time, and accurately identify key design factors affecting machining quality, making it an indispensable technology in smart machine tool design.
Conclusion
This paper provides a systematic description of intelligent and digital design technologies for machine tools, focusing on three core technologies: optimized transmission system design, lightweight structural design, and integrated servo control–structure response. The transmission system part establishes an intelligent selection module based on parameter matching among ball screws, motors, and bearings, enabling rapid response to load and feed requirements. The structural design combines topology optimization and emerging material technologies to achieve the goal of green machine tools with both high stiffness and low energy consumption. The integrated servo control design incorporates structural response into control loop analysis and constructs a digital twin model that can predict machining accuracy and dynamic behavior, thereby enhancing overall system performance.
This study highlights the key value of intelligent design in strengthening the R&D capabilities of Taiwan’s machine tool industry. In the future, machine tool development will no longer focus solely on optimizing individual modules, but will instead incorporate mechatronic integration and digital simulation technologies from the early design stage to meet the precision machining requirements of high-end special-purpose machines, strengthen global competitiveness, and promote sustainable industrial development.
