The concept of Self-Optimizing Machining Systems (SOMS) has been proposed against the background of Industry 4.0 and the Digital Twin concept, based on cyber-physical systems. In order to improve manufacturing productivity, quality, and efficiency, each component technology related to the machining process, such as CAD/CAM, process modeling/simulation, process monitoring/control, and workpiece assessment, as well as the machine tools themselves, has been developed independently to date. However, series of processes, including the interactions among these component technologies, have finally determined the machining performance and the quality of the products. SOMS deals with the information links among these components comprehensively and plays the important role of combining these links and functionalities to optimize the overall machining system. Nevertheless, an intensive implementation and combination of these technologies has yet to become state-of-the-art in industry, while further research and development for SOMS is required for Industry 4.0 and Digital Twin.

This special issue focuses on the research trends of SOMS, especially the interaction links among machine tools, process monitoring, and work assessment. From researchers who are active on the front lines of manufacturing engineering, the latest achievements related to the development of SOMS are presented in 6 papers. On one hand, the development of sensor-integrated components is indispensable for SOMS to monitor the status of a process and feed it back to a related component in order to control the machining process and its environment. On the other hand, it can be said that visual simulation, virtual metrology, and other epoch-making, on-machine technologies for evaluating machined surfaces, as well as process optimization based on machined surface information, are strongly required.

We hope this special issue will contribute to future research and development for researchers and engineers in the field of manufacturing and machining systems.

Single-lip deep-hole drilling (SLD) is characterized by high surface quality and compressive residual stress in the subsurface of the drill hole. These properties depend significantly on the thermo-mechanical conditions in the machining process. The required subsurface properties can be adjusted in-process via process monitoring and control when the thermo-mechanical conditions are maintained in the optimum range. Herein, a sensor-integrated SLD tool is introduced, which allows the temperatures near the cutting zone to be measured and the vibrations occurring directly at the drill head to be recorded. A microcontroller-based wireless measurement data transmission method is presented.

The gloss is one of the most important characteristics of plastic injection molding parts, whereby the molding process needs to consider the influence of the three main factors such as the surface roughness of the cavity, chemical properties of the plastic, and injection parameters. The surface roughness of the plastic injection mold was considered for the gloss that occurs with the parts of the plastic injection processes. Therefore, the objective of this research is to predict the gloss for plastic injection parts based on the artificial neural network method from the input parameters of 3D surface roughness from virtual machining on a 3-axis CNC machining center, and plastic injection parameters. The shape generation motions were mathematically described by combining 4 × 4 transformation matrices including the kinematic motion deviations, machining parameters, end milling ball nose geometries, and cutting force. The results of the research showed the prediction of gloss in plastic injection parts from a neural network model compared to the gloss value of the actually measured workpiece and response surface methodology combined with central composite design. It was found that the average error of the gloss was 2.36%. The proposed method provides us with a systematic method to estimate the gloss for plastic injection parts before producing the actual cavity mold, which leads to increased accuracy as well as efficiency in manufacturing plastic injection parts.

In recent years, the quality of the machined surfaces of molds for optical or medical instruments has needed improvement, and the fabrication of mirrored surfaces by means of cutting only has been gaining in importance. In order to obtain smooth surfaces, it is necessary to reduce various vibrations in the machine tool during the machining process. Many factors cause vibration in a machine tool, including feed mechanisms, pumps, and chatter. A high-speed spindle for precision machining is one source of vibration, but it is a challenge to avoid the non-repeatable run-out (NRRO) of a spindle. This study has developed an excitation system that can reduce the influence of non-repeatable run-out on machining surfaces. This paper presents a newly-developed excitation system with an excitation unit for each the X and Y direction. The excitation units consist of a voice coil motor and leaf springs, fixed on a spindle head. The tool run-out and vibration of the spindle head are measured by a displacement sensor and an acceleration sensor fixed on the spindle head, and their NRRO components are obtained through extraction using band-pass filters. By using these NRRO components as feedback signals, the excitation unit generates the force to cancel the NRRO of the tool. In order to determine the performance of the developed system, experimental evaluation was performed on a vertical 3-axis machining center. Since the spindle used for evaluation had three bearings, the measured NRRO of the tool had three peaks in the frequency domain. First, we conducted evaluation experiments under non-machining conditions, and all NRRO peaks were reduced by applying the developed system. Furthermore, there was no interference of the excitation units in the X and Y directions. Next, we evaluated the influence of the NRRO of the tool on the machining surface under finish machining conditions. The reduction in NRRO components in the measured surface profiles was observed through feedback. It was concluded that the developed system can reduce the influence of NRRO on machining surfaces.

The volumetric accuracy of a machine tool generally changes with time. Its periodic check, performed at a user’s site in a semi-automated manner, can be a key to ensure a sufficient volumetric accuracy in the long term. A laser interferometer can only measure the linear positioning error motion of a linear axis. This paper proposes a scheme to identify all the two-dimensional (2D) error motions of two linear axes in a plane based on a set of distance measurements using only a laser interferometer. Unlike conventional tracking interferometers, the proposed scheme requires only a numerically controlled rotary table on which a laser interferometer is mounted. It regulates the laser beam direction based on the command target position in an open-loop control manner. This paper presents an algorithm to identify 2D error motions of two linear axes by performing only a single tracking test, in addition to the direct measurement of linear positioning error motions of two linear axes. The experimental comparison of the estimated error motions with their direct measurements is presented. The uncertainty analysis is also presented.

In the machining field, the quality of a machined surface is characterized using both quantitative and sensory parameters. It is important to quantitatively evaluate sensory parameters to automate the evaluation of machined surfaces and determine the machining conditions. In this study, we quantitatively evaluate the gloss degree, which is a sensory parameter, via visual simulation. The gloss degree is evaluated based on an angular luminance distribution for machined surfaces cut using different tools. Using the quantitative evaluation result, observation is conducted to predict the appearance of the machined surface, and a sensory test is performed. The result shows that the quantitative evaluation results are consistent with the sensory test results.

The quality of water-soluble coolant is managed based on the maintenance schedules provided by a manager including periodic replacement of coolant. Post-maintenance is adopted when an anomaly is detected in the daily measurements. However, the reliability of management is dependent upon on the competence and experience of an operator. Condition monitoring allows users to detect critical changes in a water-soluble coolant. In contrast to the conventional method, condition monitoring can be assumed to be continuous and remote using ICT technologies. In this study, the spindle motor and NC data were utilized as internal sensors to monitor the quality of water-soluble coolant. The signal obtained from this sensor system can be easily broadcasted to the Internet as digital data and extended to an automatic data analysis using AI and machine learning in the future. It can be stated that this study enhances continuous and remote monitoring of water-soluble coolant and has the possibility of monitoring the changes in sludge concentration and Brix%. However, the sensor data cannot be used as an absolute index to estimate the quality of water-soluble coolant. It is a valuable indicator only when it is analyzed in combination with other sensor data such as pH and Brix%. The method proposed in this study can be widely extended to monitor the condition of water-soluble coolant and cutting tools.

In terms of future transportation, flying cars are envisioned not only as air taxis but also as air ambulances. Flying cars such as urban air mobility vehicles, passenger drones, and electrical vertical take-off and landing (eVTOL) aircrafts have the potential to relieve seriously congested ground traffic in cities via direct point-to-point air movements. To date, conventional research in which the airframe of a flying car is optimized for use in medical emergencies has not identified a sustainable solution. The purpose of this study is to verify the technical applicability of a flying car for use in medical emergencies. The weighted sum method is used to optimize the design of multi-rotor, vectored-thrust (tilt-rotor), and lift + cruise types of flying cars. A simulation scenario that considers cruising speed and flight height is conducted based on an analysis of stakeholder interviews with a pilot, an in-flight doctor, and an operating company. To optimize the parameters of a flying car airframe, four objective functions, namely the energy required for a round trip, noise value from rotors, downwash speed from rotors, and landing area size, are chosen because the results of a requirement analysis revealed that they were significant for the sustainability of the flying car system. The results of the simulation reveal that the required battery energy densities for all three types exceed the current lithium-ion battery capacities. Therefore, an upgrade in battery capacity is critical for the realization of a flying car. Although the noise level is found to be less than that of a conventional helicopter, it is necessary to develop a rotor to decrease noise levels for environmental reasons. Finally, both the downwash speed and landing area of a flying car are estimated to be less than those of a conventional helicopter, making it possible for the flying car to land in tight spaces.

In this paper, we propose a viewpoint planning method for object identification. We introduce the policy of maximizing the posterior probability of the orientation of an object observed after a robot moves its viewpoint and show a novel formulation of viewpoint planning. In addition, we propose criteria for viewpoint selection based on past sensing experience. Finally, we confirm the effectiveness of the proposed method via simulations using a mobile manipulator.

In this study, we present the pose estimation of a small connector attached to the tip of a cable sticking out from a circuit board. Since such connectors are generally small and float in various configurations on their workpieces, it is difficult to achieve automation of grasping and inserting the connector into the corresponding socket. We focus on the task of grasping a connector and propose methods for detecting the location of the connector and estimating its 6DoF pose. In this regard, we use a high-precision three-dimensional digitizer to capture the object point cloud and combine several methods, such as deep learning and registration, to perform data processing. We conducted grasp experiments on several connectors using an actual industrial robot and confirmed the effectiveness of our approach in terms of pose estimation.

Much emphasis is now being placed on the research and development of fully closed and controlled plant factories aimed at supplying fresh vegetables safely and constantly. Plant factories produce large volumes of clean and safe vegetables in an artificially controlled environment. One of the important issues in the management and control of plant factories is establishing systematic methods for planning the production and shipping of daily produced vegetables to various customers, such as supermarkets, vegetable shops, and restaurants. Customer demand is influenced by the sales in the individual shops and trade volumes, and prices in the wholesale markets of the vegetables, since the share of the plant factory-made vegetables is very small. Systematic methods are required for the management and control of plant factories to forecast both orders from individual customers and wholesale market conditions. In the previous study, the market situations and the trading processes of the factory-made lettuces were investigated, and a market model was proposed to simulate the trading processes between the customers and the plant factories. This study deals with demand forecasting of wholesale markets for vegetables, aimed at taking into consideration the wholesale market conditions for production planning in plant factories.

The workpiece materials used in the experiment were pure Ti and Ti alloy, which are commonly applied in the biomedical and aircraft industries. Although they are attracting increasing interest due to their superior mechanical properties, they are also known to be difficult-to-machine. This study investigated the cutting conditions for realizing minimum tool wear and optimum surface roughness using a TiAlN coated carbide end mill with a diameter of 6 mm, corner radius of 0.5 mm, and four blades in high-speed milling experiments based on the dry cutting process. In this case, peripheral speed was set as the main parameter. Cutting resistance and cutting temperature are also important parameters, but they are difficult to use as indicators for setting cutting conditions directly from the obtained data. Therefore, changing the rotational speed is a practical way to change the peripheral speed, while changing the machining conditions directly from the cutting resistance and temperature is difficult and impractical.