This special issue of the International Journal of Automation Technology brings together research advancing the field of precision engineering in the context of next-generation automation systems. Precision engineering has long served as the foundation of industrial innovation, enabling the construction of systems with high precision, efficiency, and reliability under demanding conditions. As automation becomes increasingly intelligent and interconnected, the role of precision is becoming increasingly important, affecting not only the performance but also the reliability, sustainability, and adaptability to complex environments.

The contributions reported in this issue cover diverse yet complementary research areas. Specifically, advanced mechanism design, improved uncertainty quantification in early-stage design processes, and development of state-of-the-art measurement techniques based on imaging are covered. Structural optimization approaches aimed at reducing the errors resulting from geometric distortions and misalignments are also explored. By integrating theoretical innovations with practical applications, these studies provide practical insights that engineers and researchers can employ to enhance the capabilities and reliability of automation technologies.

Synergies between precision engineering and emerging fields, such as artificial intelligence, robotics, and cyber-physical systems, are expected to lead to transformative advances. We hope that this special issue will encourage continued collaboration across disciplines and foster technologies that not only meet current industry demands, but also anticipate future needs. The editors would like to thank all contributors and reviewers for their efforts to make this special issue a valuable resource for researchers, practitioners, and innovators in the global automation community.

In Japan, the renewal and renovation of power lines, poles, and other electric transmission facilities have increased due to aging. The maintenance of power line is performed by several workers from the bucket of an elevated work vehicle, which involves dangerous and difficult live-line work. In this study, we propose a unique pneumatic robot arm to support this maintenance work. The robot arm has five degrees of freedom, including a telescopic mechanism for approaching power lines. The worker operates this robot arm with various special tools attached to the tip, thereby reducing the burden on the worker. The arm mechanism can be easily insulated and waterproofed using pneumatic drive and is lightweight. The design method for each pneumatic drive joint of the proposed robot arm is demonstrated. Simple control methods of the pneumatic drive are also introduced to improve the robot arm operability. The potential of the prototype robot arm to support power line work is further demonstrated.

Modern artificial objects have become increasingly complex, and this complexity is mirrored in the design process itself. When critical design changes occur in downstream phases, there is a high risk of deterioration in quality, cost, and delivery owing to rework across related processes. Therefore, potential design changes must be predicted in the early stages of design and proactive measures should be taken. In the early design stage, the process is inherently based on fallible design hypotheses, and the fallibility of these hypotheses can lead to design changes in later stages. Accordingly, the certainty of each hypothesis must be evaluated by considering the evidence that supports it. However, design hypotheses are often supported by multiple heterogeneous pieces of evidence with varying degrees of support, and the information available in the early stage is typically incomplete. As a result, rationally evaluating the certainty associated with each hypothesis is not easy. To address this issue, this study proposes a transparent and systematic method to quantify the certainty of design hypotheses while accounting for incomplete evidential information in the early design phase. It first organizes the conceptual foundation of the evidence underlying hypothesis certainty and models it. Then, by applying Dempster–Shafer theory, a computational framework capable of determining proposition certainty from multiple evidence sources under incomplete information, we propose a method to quantify the certainty of design hypotheses. The proposed method is applied to hypotheses generated in a design experiment, and procedural validity and user evaluation were examined. This study introduces a new approach for managing fallible design knowledge based on Dempster–Shafer theory, suggesting a conceptual basis for the early detection and mitigation of risks associated with potential design changes.

X-ray computed tomography (XCT) is an inspection technique that has gained significant importance in coordinate metrology, thanks to its ability to capture detailed information about complex-shaped parts. The XCT process involves multiple steps, each contributing to uncertainties. One crucial aspect in dimensional and geometrical metrology is the surface determination technique (SDT), which focuses on distinguishing the boundaries of the scanned object. Despite its importance, there is still a lack of research on how various SDTs affect the outcomes of coordinate measurements. This research aims to provide an in-depth comparison across a variety of dimensional and geometrical measurands to assess the performance of multiple SDTs. Measurements from coordinate measuring machines serve as the references for comparison.

This study proposes a new structural design and analysis of a metrology reference module for a quasi-Abbé-error-free three-axis measurement platform used in 12-inch wafer inspection, with travel ranges of 300 mm (X/Y) and 5 mm (Z). Based on a prior platform architecture with zero Abbé error in all three axes, this study focuses on optimizing the metrology reference module by integrating three-axis reference mirrors into a monolithic structure. This integration eliminates assembly errors, simplifies construction, and enhances structural performance. Finite element analysis (FEA) was used to optimize stiffness-to-weight ratio and minimize deformation under static loads, which improved interferometric measurement accuracy and reduced errors caused by the effect of structural deformation on the wafer. A prototype was built based on the FEA results and validated through experimental modal testing. The results show a 96.4% decrease in static deformation (from 15.62 µm to 564 nm) and a 45.67% increase in the first natural frequency, indicating significant improvements in both static and dynamic rigidity. The results of the modal testing further validated the strong correlation between experimental measurements and simulation, indicating that the established model accurately captures the dynamic characteristics of the structure. This confirms the high accuracy and reliability of the FEA model for structural performance prediction.

To ensure the reliability of small-lot machining for thin-structured parts, an on-machine workholding state estimation method based on measured strain has been proposed. When applying this method to actual machining scenarios, it is necessary to select appropriate measuring points to achieve estimation accuracy. This paper proposes a method capable of systematically selecting measuring points for individual cases. First, finite element simulation was used to calculate strain values for each fixturing case. Moreover, the variations in strain values corresponding to the variated fixturing process were calculated using feasible varied fixturing conditions. Using the strain values corresponding to the feasible fixturing conditions, an evaluation criterion was applied to evaluate the candidate measuring points. The feasibility of the proposed criterion was investigated by estimating workpiece deformation using different measuring candidate points and comparing the accuracy of the estimations. In the investigation, the estimated workpiece deformation results were compared with the actual workpiece deformation. The comparison demonstrated that the proposed method can effectively identify appropriate strain measuring points.

In industrial plants, piping and equipment are intricately interconnected, and there are many components with a variety of shapes. To use point clouds of industrial plants for simulation of maintenance work, it is necessary to extract the components. Deep learning is effective for recognizing components in point clouds of industrial plants. However, training classifiers is challenging due to the difficulty in acquiring diverse point cloud datasets and the labor-intensive process of annotating large-scale point clouds. A promising approach to address these issues is to train the classifier on virtual point clouds generated from CAD models. However, classifiers trained on these virtual point clouds often fail to achieve sufficient segmentation accuracy due to discrepancies between virtual point clouds and actual point clouds captured by terrestrial laser scanners. This paper proposes methods to improve segmentation accuracy by reducing these discrepancies. First, we introduce a method to incorporate features such as missing points, noise, and outliers observed in actual point clouds. Furthermore, we propose a data augmentation approach that applies up-sampling using a deep learning model trained on paired virtual and real point clouds to reduce the discrepancy between them. Our evaluation demonstrates that the proposed methods effectively improve the segmentation accuracy of point clouds of industrial plants.

Variable displacement hydraulic pumps are applied to a wide range of fields for energy saving, but the displacement control is easily influenced by changes in dynamic characteristics depending on the operating point, and the control valve and pump displacement have constraints. Therefore, high control performance cannot be obtained without considering these nonlinearities. In a previous study, we designed a pump displacement control system based on a model predictive control (MPC) method that can consider various constraints at the design step. However, the previously presented control system requires the pre-designed reference trajectory of the pump displacement at the design step. Furthermore, the pump displacement cannot track to other reference trajectories. In this study, an extended MPC proposed in a previous study is combined with an adaptive model matching-based MPC with an inverse optimization method, proposed as a control system by the authors. This compensates for modeling errors and optimizes the weights of the evaluation function to achieve tracking to arbitrary time-varying reference trajectories using a virtual reference signal. To improve tracking performance, variable control input constraints, which are also proposed in our previous study, are introduced. The tracking performance of this control system for arbitrary time-varying reference trajectories have been verified by experiments. The experimental results have shown that the proposed control system achieves high tracking accuracy for an arbitrary time-varying reference trajectory and significantly reduces the man-hours for the parameter design of the control system.

The size of general-purpose machine tools used in production tends to be oversized for the parts being produced. Downsizing these machine tools can reduce manufacturing costs by decreasing the size of large production lines. Additionally, smaller machine tools can be easily transported and installed, facilitating the construction of flexible production lines that can adapt to changes in demand. However, downsizing machine tools reduces structural rigidity, leading to deformation due to cutting forces. The displacement of the tool from the target position caused by this deformation makes high-precision machining of hard materials, such as steel, difficult. This study proposes a method to reduce machining errors in small machine tools by predicting deformation during cutting and compensating for tool position based on the results of a simple static load test. To verify the effectiveness of the proposed method, a new small 3-axis NC milling machine was developed. The proposed method successfully reduced machining errors by 87% in the side cutting of a steel workpiece.

Japan’s rapidly aging population and shrinking workforce are creating serious challenges, especially in jobs that require long hours outdoors. To solve this, Japan urgently needs innovative solutions, including automation technologies for outdoor work such as farming, construction, and maintenance. One promising approach is the application of model predictive control (MPC) to outdoor mobile robots. Although MPC has been widely studied in the context of mobile robotics, there remains a paucity of practical research specifically targeting cleaning robots operating in outdoor environments. Alternative approaches, such as geometric path-following methods like pure pursuit, are frequently employed in simpler applications, but often encounter limitations in achieving high-precision trajectory tracking. This study proposes a model predictive contouring control (MPCC) framework for trajectory tracking in outdoor cleaning robots. The proposed method offers the capability to balance the trade-off between execution time and trajectory accuracy. Both simulation and experimental results validate the effectiveness of the proposed MPCC approach.

Superconductive-assisted machining (SUAM) is a polishing method that applies the magnetic flux-pinning phenomenon of a superconductor to a magnet tool with a polishing pad, thereby reducing tool interference. SUAM methods utilize two types of devices: a single magnet system and a double magnet system (DMS). The DMS increases the magnetic flux density by placing the lower magnet under the superconductor, resulting in higher polishing pressure. In our previous study, we used a superconducting ceramics bulk in a conventional SUAM system. In this study, superconducting tapes were combined with the superconducting ceramics bulk to increase the holding force of the SUAM system. Four types of superconductors using superconducting ceramics bulk and tape were evaluated using SMS to determine their structure as a function of magnet tool position. The maximum holding force was obtained when the superconductor was combined with the ceramics bulk and superconducting tape. The holding force was measured by varying the initial position of the lower surface magnet in the DMS. When the bottom magnet was placed 4 mm from the superconductor, the attractive force was 1.6 times greater than at a 20 mm distance. The polishing performance of aluminum in SUMA was evaluated using superconducting ceramics bulk and tape. A superconducting tape can accommodate greater polishing pressure, resulting in higher polishing efficiency. After polishing, a surface roughness of Sa<0.1 µm was obtained with reduced polishing pressure.

Among machining conditions, tool paths exert a significant influence on the final machining outcome. In conventional practice, computer-aided design (CAD) models created from basic dimensions are typically used to generate tool paths within computer-aided manufacturing software. In recent years, 3D annotated models—CAD models enriched with product manufacturing information (PMI)—have become increasingly widespread. However, CAD models are often modified manually to achieve the desired machining results while accounting for machining errors. Consequently, operators consume time and effort in generating tool paths. Moreover, knowledge and experience in machining are required to modify CAD models according to PMI, such as unilateral tolerances. In this study, a method is proposed that leverages operator expertise to automatically modify CAD models and generate tool paths capable of satisfying specified size limits. The method employs a chain expression of dimensions, where the dimensional chain originates from the datum used as a reference for machining. Based on the dimensional chain, the objects requiring shifts are systematically identified. Subsequently, basic dimensions with unilateral tolerances are converted into target dimensions with bilateral tolerances to achieve the desired machining results. Case studies confirm that CAD models can be automatically modified when the target dimension is set to the median value of the size limits. Furthermore, machining experiments demonstrate that the proposed method effectively ensures compliance with the specified size limits.

In recent years, the industrial sector has increasingly relied on technology for monitoring the operating status of production and processing equipment as a measure to address labor shortages. In the plastic molding industry, which is the subject of this study, efforts have been made to track operational conditions by installing a variety of sensors capable of detecting different types of problems. Furthermore, it is evident that so-called DX manufacturing, in which sensor-derived information is utilized to achieve efficient production through digital technologies such as AI and the Internet of Things, will become mainstream in the future. In this study, attention was directed to the cobwebbing phenomenon, a defect observed in injection molding, with a particular focus on its monitoring technology. Although the diameter of the threads pinched during the cobwebbing phenomenon is approximately 0.1 mm, early detection is critical because the defect can cause mold damage and deterioration. To address this issue, strain data were obtained from a mold equipped with a strain gauge, and discrimination technology was investigated by comparing the normal state with the pinched state using the Mahalanobis distance, a pattern recognition method. The results indicated that it may be possible to detect a thread with a diameter of about 0.05 mm if it becomes trapped during molding, and certain mold features that enhance discrimination accuracy were identified. Therefore, this study reports an example of monitoring technology for the entrapment of cobwebbing, specifically foreign matter entering during the molding process.