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A new methodology to generate instruction commands for prompt machine control as a replacement for the previously prepared numerical control (NC) programs is developed to realize an innovative intelligent machine tool. This machine tool can eliminate NC program preparation, achieve cutting process control, reduce the production lead time, and reali...
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... model virtual master model in the computer, which is regarded as the displacement ε. Fig. 4 shows the relationship between the displacement of the stylus and the amount of interference. The amount of interference can be represented by the geometrical relationship between a circle of a stylus projected on the 2D plane and a surface of the master model. The angle θ max , at which the interference amount is maximized, is ...
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... The machining condition was simulated and stored in advance for generalized predictive control (GPC). Nishida et al. (2019) developed an integrated methodology for adaptive control based on the predicted cutting force, which was simulated with the assistance of computer-aided manufacturing (CAM) and a computer numerical controller (CNC). ...
With intelligent manufacturing development, applying adaptive control technology in the machining process is an effective way to increase productivity and quality. However, adaptive control alone cannot control cutting forces effectively when cutting conditions have excessive change. In this study, a digital twin of the milling process is introduced to cutting force adaptive control for system robustness and efficiency. The cutting force is indirectly measured based on the feed drive current using a Kalman filter, and unknown parameters in the estimation model are identified. A virtual machining system model is established based on online data communication and geometric operation. In addition, the machining state is predicted and introduced into the adaptive control algorithm based on the integrated digital twin for cutting force constraint control. Finally, rough milling of an S-shape specimen is carried out as the cutting experiment to verify the credibility and efficiency of the digital twin-driven cutting force adaptive control.
... The presently used relationships between the information signal and tool wear are phenomenological, frequently without quantitative substantiation. The research issues have been investigated in the context of machine learning for predictive maintenance in milling [35], development of a new algorithm for online tool wear control [36], development of an innovative, intelligent machine tool [37], adaptive speed control for waterjet milling [38], and model-free adaptive predictive control for CNC [39]. Therefore, at the present level of the development of material processing by cutting, it is necessary to determine the degree of correlation between the sound generated during the cutting process, i.e., one of the promising information signals, the wear of the cutter, and the workpiece surface roughness. ...
The article aims to use the generated sound as operational information needed for adaptive control of the metalworking process and early monitoring and diagnosis of the condition of the machined materials using a newly introduced surface roughness quality index due to the sound-controlled machining process. The object of the measurement was correlation between the sound intensity generated during cutting and the material parameters of the machined surface, i.e., the roughness of the machined surface and the degree of wear of the cutting tool. The roughness was measured during longitudinal turning of a steel billet with a P25 insert made of 12X18H10T steel and a T15K6 cutting insert made of a titanium, cobalt, and tungsten group alloy. The correlation between the sound and roughness of the machined surface was 0.93, whereas between the sound and wear of the cutting tool was 0.93. The correlation between sound and tool wear in the experiment with P25 and T15K6 cutting inserts and the correlation between sound and roughness is positive.
... As shown in the papers [40][41], the quality of adaptive control depends on the successful control of cutting modes, which is, in turn, ensured by the automation of the control process itself. The currently used relationships between the information signal and tool wear are phenomenological, often having no quantitative substantiation [42][43][44][45][46]. Therefore, at the present level of development of material processing by cutting, it is necessary to determine the degree of correlation between one of the promising information signals -the sound generated during the cutting process, the wear of the cutter and the surface roughness of the workpiece [47][48][49]. ...
The presented paper proposes a method of adaptive control of the metal cutting process based on the analysis and interpretation of recorded sound for the identification of the cutting process. The presented research results clearly demonstrated the efficiency of using the sound generated during cutting by CNC lathe 16 K20T1 for the adaptive metalworking control. The sound makes it possible to control the accuracy of the workpiece part geometry and the surface quality in the online mode. The online mode also makes it possible to make an informed decision about a timely interruption of the cutting process or the continuation of the process in the altered modes for its successful completion. As the research results presented in the paper show, there is a close correlation between the sound generated by cutting and the roughness of the surface being machined or the degree of wear of the cutting tool. The correlation between the sound and roughness of the machined surface is 0.94, whereas between the sound and wear of the cutting tool is 0.93. The paper aims to use the generated sound as operational information needed for adaptive control of the metal processing process and timely monitoring and diagnostics of the condition of processed materials using the precision index, newly introduced as the machining quality index.
A five-axis machining center (5MC) is capable of synchronous control, which makes it a feasible tool for quickly and accurately machining complicated three-dimensional surfaces, such as propellers and hypoid gears. Recently, the necessity of improving both the machined shape accuracy and the machined surface roughness of free-form surfaces is growing. Therefore, in our previous study, we aimed to maintain the feed speed vector at the end-milling point by controlling two linear axes and a rotary axis of the 5MC to improve the quality of the machined surface. Additionally, we developed a method for maintaining the feed speed vector at the end-milling point by controlling the three axes of the 5MC to reduce the shape error of the machined workpieces (referred to as the shape error herein), considering the approach path of the tool determined via calculation. However, a high machining force at the start of the workpiece cutting was observed and the factor contributing to this phenomenon was not determined, although this phenomenon leads to a shape error to a certain degree according to the machining condition. In this study, the main objective is to suggest a method to reduce the machining force at the start of the workpiece cutting and shape error. Hence, we develop a theoretical method to estimate the machining force by using an instantaneous cutting force model, which considers the synchronized motion of two linear axes and a rotary axis of the 5MC. Subsequently, we determine the most suitable approach path of the tool considering the prediction of the machining force. The results of this study indicate that the machining force can be estimated by applying an instantaneous cutting force using the feed per tooth and machining angle, and that both a high machining force at the start of the workpiece cutting and shape error reduction can be realized by using the proposed approach path of the tool.
Recently, there has been an increased demand for precise monitoring of the milling process using machine tools through a simple and cost-effective method. Accurate estimation of cutting forces is highly effective for this monitoring, and one approach is the modeling of tool spindles and tables of a machine tool. To model machine structures, well-known methods involving the use of impulse hammer response or structural analysis exist. However, the complex modeling is hard to achieve when using the impulse response. Moreover, it is often considerably difficult to achieve the modeling with structural analysis because the preparation of the accurate model and highly complicated calculations are required. Therefore, in this study, we propose a new monitoring method to identify model parameters of the machine structure and estimate cutting forces. First, a simplified assumed structure is prepared based on locations where sensors can be mounted. Next, measurement data during actual milling process are collected through the acceleration sensors mounted on the tool spindle and the dynamometer for the cutting force attached to the table. Subsequently, model parameters are identified from these data using machine learning. A 3-axis NC milling machine was used to evaluate the application range of the model parameters by changing cutting conditions, milling direction, cutting tools, and materials. The model parameters identified using the proposed method were equivalent to those using the impulse response. Furthermore, even in cases where the impulse response was difficult to identify, suitable model parameters were identified using machine learning. Finally, we confirmed that the proposed method can accurately achieve in-process monitoring of cutting forces in the X , Y , and Z directions.
With the development of science and technology, human cognition and perception have changed with the development of science and technology and the progress of culture. For this reason, people try to rely on machine vision and virtual reality technology to develop a more diversified space environment. Interior design and VR technology are also increasingly connected. Based on this, the purpose of this paper is to study the intelligent innovative design of indoor VR based on machine vision. Based on machine vision, this paper uses machine vision to achieve indoor positioning, target detection and positioning tracking and other functions, and then uses virtual reality technology to design, model, and optimize the indoor space. In addition, users can also use virtual reality technology. Technology to personalize interior spaces to your liking. By applying the method in this paper to the interior design, it can make up for the deficiencies in the current design method by taking advantage of its unique advantages. Accurate positioning of interior design, market demand, and customer’s wishes have a clear understanding, which has played a good guiding role in interior design and development. In addition, due to the method in this paper, the design can be carried out according to the user’s preferences, thus greatly reducing the design cycle. Compared with the past, the design cycle is reduced by 30%, the design cost is reduced by 20%, and the user satisfaction is greatly reduced. Improve.KeywordsMachine VisionVR TechnologyInterior Design3D Modeling
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.
