A Fourth Industrial Revolution has been proposed, and various research and development activities geared toward the realization of smart factories have become extremely active. In smart factories developed individually within companies, the direction of the development activities is changing drastically. Regarding common technologies, research activities and feasible case study activities that are collaborations among industry, academia, and government and that show consideration and awareness of ecosystems are becoming active.

It has been five years since the IJAT last published a special issue on smart manufacturing. Smart factory technologies are becoming more and more important in the industrial world, not as a temporary boom, but as a steady development. Research on smart factory technologies is progressing, as they are considered to be important technologies in the industrial world, along with Digital Transformation (DX) technologies, among others.

This special issue addresses the latest in advanced research on smart factories. We have received many submissions from researchers at universities, public research institutes, and companies, and we are pleased to publish eight papers. The latest research being published mainly concerns the following five topics:

- Smart and digital twinning of machine tools

- Smart and digital twinning of production systems

- Optimization of production systems using scheduling algorithms

- Optimization of production systems including worker operations

- Optimization of energy consumption for production systems

The results of these studies will serve as a reference for further smart factory technologies in the future.

We deeply appreciate the careful work of all the authors, and we thank the reviewers for their incisive efforts. Without these contributions, this special issue could not have been created. We also hope that this special issue will trigger leading to further advances in smart factories.

Promoting digital transformation (DX) and realizing smart factories have become critical for manufacturing companies to meet increasing demands such as short-term delivery, quality assurance, and environmental, social, and corporate governance (ESG) as well as to improve productivity and quality of work (QoW). To this end, digital tools should be provided for practical application in the preparation of the environments in which the companies can learn and study how to use digital technologies and tools by trial and error, while developing human resources for utilizing them for their own problem solving. In this paper, we describe the activities we used to develop various digital tools in the fields of manufacturing, robotics, and service engineering. We integrated these into a cyber physical system (CPS) developed for our model factory and offered a course for the company workers to learn these digital technologies. We also planned to develop our activities in collaboration with companies, universities, and other research institutes.

A practical digital twin for machine tools is proposed in this study. The proposed digital twin is capable of time-domain simulation of machine tools and consists of a controller model, machining process model, and machine dynamic model. To predict the quality of the machined surface after the finishing processes, a precise dynamic model is required. The developed dynamic model consists of an interaction force model, vibration model, and friction force model. A linear auto regressive with exogenous inputs (ARX) model is adopted for the interaction and vibration models. Based on a systematic analysis of the disturbance forces of the machine tool, the friction characteristics are extracted to a displacement-dependent friction model and velocity-dependent friction model. A nonlinear Hammerstein model is adopted to identify the friction. Online identification systems based on the recursive least-squares (RLS) method are developed and tested for each model.

In recent years, wood has been considered as a building material from the perspective of SDGs. Pre-cut lumber is often used for wooden houses, including roofs, floors, and pillars. It is preprocessed into a certain shape for the joint parts and has joint brackets if necessary. The use of them has the advantages of shortening the construction period and reducing the construction cost. The number of pre-cut lumber production factories in Japan decreased from 757 in 2001 to 659 in 2011 and then increased to 730 in 2016. Compared with 2011, the number of factories with sales of <500 million yen in 2016 decreased by approximately 30%, while the number of factors with sales of ≥500 million yen increased by 80%, indicating a trend toward a larger scale [1]. Therefore, improving the productivity of these factories is imperative. We have been researching the conversion of factories into smart factories to improve the productivity of pre-cut lumber manufacturing company A. In this company, employees take 1.2 h to prepare packing plans every day, and the company faces the challenge of improving the efficiency of planning operations. Herein, we propose an algorithm using an iterative local search (ILS) with Or-opt (ILS + Or-opt) for packing formation support systems to improve the efficiency of planning operations. This algorithm has the following two features. First, it uses Or-opt to create a neighborhood for a local search. Second, uses exchange neighborhoods when repeating the ILS to achieve diversity in the search.

An automation system of machine tools can free operators from simple and hard labor by the automatic loading and unloading of workpieces, and allowance of unmanned operation during nights and holidays. To achieve complete shop floor operations, various works that are manually performed by operators need to be automated as well. It is also crucial to accurately monitor tools to detect tool wear and chip winding during machining. In this study, we propose an on-machine tool condition monitoring system that measures tool length and diameter using images to detect tool breakage and winding chips.

Manufacturing functions are often performed by groups of engineers who cooperate and gather at work sites. However, since the beginning of the COVID-19 pandemic, the movement and activities of groups of people have been restricted, especially in indoor spaces. This reduction in travel by engineers also implies a reduction in associated costs. Telepresence technology, which is studied in the field of virtual reality, can be used as a way to reduce travel. Telepresence allows users to engage with a site from a remote location as if they were present. Thus, engineers would be able to participate in a working group without the necessity of physically traveling to the site to cooperate with local manufacturing people. A variety of telepresence systems have been proposed; however, relatively few methods have been widely implemented compared with video chat applications that have recently become an established infrastructure in many companies. This is most likely because most proposed systems use robots, head-mounted displays, or dedicated multi-functional applications that require engineers to learn how to use them. One way to use a video chat application to understand a remote space is to have a remote participant move a camera used in a video chat application. In contrast, many VR social networking services use a viewing method with which users can change their viewing direction on the computer screen. In this study, we demonstrate that a system that allows users to rotate their viewing perspective on a laptop computer screen can provide an easier understanding of a virtual space than a system that requires a remote person to move a webcam. Based on these results, we propose a system that allows users to view a remote location on a laptop computer screen via a video chat application and an off-the-shelf spherical camera, and evaluate its usefulness.

The goal of the Fourth Industrial Revolution is to develop smart factories that ensure flexibility and adaptability in complex production environments, without human intervention. Smart factories are based on three main pillars: integration through digitalization, employment of flexible structures, and the use of artificial intelligence (AI) methods. Genetic programming (GP) is one of the most promising AI approaches used in the automated design of production-scheduling rules. However, promoting diversity and controlling the bloating effect are major challenges to the success of GP algorithms in developing production-scheduling rules that deliver high-quality solutions. Therefore, we introduced a multi-objective technique to increase the diversity among GP individuals while considering the program length as an objective to avoid the bloating effect. The proposed approach employs a new diversity metric to measure the distance between GP individuals and the best rule in the current generation. Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) was used to select individuals based on three objectives: solution quality, similarity value, and program length. To assess the effectiveness of the proposed approach, we compare the two versions with three GP methods in the literature in terms of automatically generating dispatching rules on 10 benchmark instances of the job-shop scheduling problem. The experimental results show that the proposed distance measure enhances the phenotypic diversity of individuals, resulting in improved fitness values without the need for additional fitness assessments. In addition, the integration of NSGA-II with the GP algorithm facilitates the evolution of superior job shop dispatching rules with high diversity and shorter lengths under the makespan and mean tardiness objectives.

In recent years, reducing energy consumption has become a key issue in the industrial world. Therefore, industrial corporations must develop methods of pre-evaluation and production management for reducing their energy consumption while maintaining productivity. Moreover, production lines occasionally generate defective products, reducing the productivity and wasting energy, which affects the energy consumption per unit of production. These production lines require inspection machines to exclude defective products. The layout and configuration of inspection machines change when defective products are excluded, which affects the energy consumption per product. However, no methods have been developed for evaluating the energy consumption per product by considering the number of defective products and the layout and configuration of the inspection machines. In this study, we formulated the energy consumption rate of a production line that generates defective products as the production planning and management method. Specifically, we developed a formula for the energy consumption rate of a production line by considering the defect rate of its production machines and the layout and configuration of the inspection machines. A simulation involving a semiconductor manufacturing line was conducted to validate the proposed theory.

It is becoming increasingly important for companies to accommodate childcare leave to support childbirth and childcare activities. It is important for them to raise the utilization rate of the maternity and childcare leave mechanisms. On the premise of the use of maternity and childcare leave, production planning and management are realizing that childbirth and childcare are important. We first analyzed female workers’ current situation in the manufacturing industry. Next, we proposed the production system simulation model while considering the production system in relation to maternity and childcare leave, and implemented the simulation while considering this premise. In addition, we proposed the cost evaluation method. To confirm the effectiveness of the proposed method, we carried out a case study that targeted an automobile parts production line.

The recent development of low-cost accelerometers, driven by the Industrial Internet of Things (IIoT) revolution, provides an opportunity for their application in precision manufacturing. Sensor data is often of the highest consideration in any precision machining process. While high-cost vibration sensors have traditionally been employed for vibration measurements in industrial manufacturing, the measurement uncertainty affecting the accuracy of low-cost vibration sensors has not been explored and requires performance evaluation. This research focuses on the characterization of measurements from low-cost tri-axial micro electro-mechanical systems (MEMS) accelerometers in terms of identifying the parameters that induce uncertainties in measured data. Static and dynamic calibration was conducted on a calibration test bench using a range of frequencies while establishing traceability according to the ISO 16063 series and the IEEE-STD-1293-2018 standards. Moreover, comparison tests were performed by installing the sensors on machine tools for reliability evaluation in terms of digital transmission of recorded data. Both tests further established the relationship between the baseline errors originating from the sensors and their influence on the data obtained during the dynamic performance profile of the machine tools. The outcomes of this research will foresee the viability offered by such low-cost sensors in metrological applications enabling Industry 4.0.

To address the problem of wet ball milling in a strong noise environment, it is difficult to accurately detect the internal load parameters of the cylinder during grinding. In this paper, a mill load parameter prediction method is proposed based on complementary ensemble empirical mode decomposition (CEEMDAN)-refined composite multiscale dispersion entropy (RCMDE) and-long and short-term memory (LSTM) neural networks. Using this method, the vibration signals of the mill barrel under strong noise were decomposed using the CEEMDAN algorithm, sensitive modal components with strong correlation with the original signal were selected for reconstruction using the correlation coefficient method, and features of the reconstructed signals under different load parameters were extracted through RCMDE. The load characteristic vector of an RCMDE mill was used as the input of LSTM neural networks, and the filling rate, material and ball ratio, and grinding concentration were used as the output to establish the internal load prediction model of wet mill. Experiment results show that the prediction method has a high accuracy, with average absolute percentage errors of the filling rate, feed-to-ball ratio, and grinding concentration of 6.08%, 3.50%, and 3.47%, and average absolute errors were of 0.0167, 0.0146, and 0.0146, respectively.

Abrasive wire cutting (AWC) and wire electric discharge machining (WEDM) are efficient and economical processes for the fabrication of precision parts from bulk material. Operating costs and manufacturing lead times are low compared to more general methods such as 5-axis CNC milling, turning, or electro-discharge machining. In this paper, an algorithm based on differential geometry in Euclidean space is proposed for reverse engineering of ruled geometries. The algorithm can determine whether a given geometry is producible by wire cutting, and can also derive the associated wire trajectories. Implementation is demonstrated by producing complex turbine blade geometries on 4-axis wire cutting machines with an overall shape accuracy of 20–40 μm peak-to-valley.

A five-axis machining center is known for its synchronous control capability, allowing complicated three-dimensional surfaces, such as propellers and hypoid gears, to be quickly created. Prior research has shown that it is necessary to improve not only the machined shape accuracy but also the machined surface roughness of free-form surfaces. Therefore, in this research, we aimed to maintain the feed speed vector at the endmilling point by controlling two linear axes and a rotary axis with a five-axis machining center to improve the machined surface quality. In previous research, we suggested reducing the shape error of machined workpieces (referred to as shape error in this research) by considering the differences in the servo characteristics of the three axes in the machining method. The shape error was significantly decreased by applying the proposed method, which uses a parameter (referred to as precedent control coefficient in this research) determined by calculation, rather than trial and error. Moreover, to maintain the feed speed vector at the endmilling point when machining complex shapes, a rapid velocity change in each axis is required, causing inaccuracy owing to torque saturation. The results of the experiments and simulations of previous research indicated that torque saturation can be evaluated via simulation. In this research, to reduce the shape error while avoiding torque saturation when movement has high angular velocity, we developed a theoretical method to obtain the most suitable precedent control coefficient of each axis by using a block diagram that considers torque saturation. Therefore, both shape error reduction and torque saturation avoidance can be realized by using the proposed method.

Several types of multiple straight convex shapes were formed on a thin aluminum sheet with a grooved die using impulsive water pressure. The maximum pressure was 160 MPa in the high-speed forming, wherein a drop hammer testing machine was used, whereas it was 100 MPa in the low-speed forming because of the limitations of the press machine. The effects of the forming speed, cross-sectional shape, and pitch of the grooves on the deformation behavior were investigated. The increase in the impulsive water pressure was found to be affected significantly by the compressibility of water. The symmetricity of the convex shape in the cross-section decreased at both ends for a smaller pitch because of the imbalance of the material flow at both peripheries of the groove. The concave surface profile of the pressure side was more rounded in the high-speed forming than that in the low-speed forming when semicircular and rectangular grooved dies were used. This may be attributed to the fact that the plastic deformation becomes more uniform owing to the positive strain rate sensitivity of the test material. In the forming with rectangular grooves, fracture occurred under the low- and high-speed conditions, wherein the maximum pressure was set to 100 MPa. However, the material did not fracture during high-speed forming with a pressure of 160 MPa, where the convex shape was higher and the material contacted the bottom of the groove. This behavior may be because the dislocation density of the material did not increase rapidly owing to the strain rate being maintained high until the material suddenly stopped deforming in the latter condition. In forming with a trapezoidal grooved die, the formed profiles were considerably similar under all conditions because the strain was considerably smaller than that with the other grooves.