Automatic Equipment Design of Intelligent Manufacturing Flexible Production Line Based on Industrial Motorized Spindle

Based on the previous research on the production line automation, this paper carries out further research and further design and development on the basis of the original production line automation equipment. In this paper, the overall design of the automatic production line is carried out, and the various systems in the automatic production line are optimized, and the backward instruments are eliminated, and then some more advanced and convenient instruments are applied. Then, the hardware and software of the automatic production line are studied respectively, and the human-computer interaction module and real-time main control circuit module are re developed, and the electric shaft is applied to the automatic production line. Finally, the fuzzy PID controller of the stepping motor is designed. The experiment shows that the fuzzy PID control scheme is better than the traditional PID control scheme. After the rationalization of the system, the quality robustness of proactive planning is improved obviously. Then, the temperature of motorized spindle was tested.

Automatic Production and Preliminary PET Imaging of a New Imaging Agent [18F]AlF-FAPT

BackgroundFibroblast activating protein (FAP) has become an important target for cancer diagnostic imaging and targeted radiotherapy. In particular, [18F]FAPI-42 has been successfully applied to positron emission tomography (PET) imaging of various tumors. However, it exhibits high hepatobiliary metabolism and is thus not conducive to abdominal tumor imaging. This study reports a novel 18F-labeled FAP inhibitor, [18F]AlF-FAPT, a better FAPI imaging agent than [18F]FAPI-42.Materials and MethodsThe precursor of [18F]AlF-FAPT (NOTA-FAPT) was designed and synthesized using the standard FMOC solid phase synthesis method. [18F]AlF-FAPT was subsequently synthesized and radiolabeled with 18F using the AllInOne synthesis module. Dynamic MicroPET and biodistribution studies of [18F]AlF-FAPT were then conducted in xenograft tumor mouse models to determine its suitability.ResultsThe precursors NOTA-FAPT were obtained with a chemical purity of > 95%. [18F]AlF-FAPT was synthesized automatically using the cassette-based module AllInOne within 40 min. The non-decay corrected radiochemical yield was 25.0 ± 5.3% (n=3). In vivo imaging and biodistribution studies further demonstrated that compared with [18F]-FAPI-42, [18F]AlF-FAPT had a lower hepatobiliary uptake than [18F]FAPI-42, which was advantageous for imaging abdominal tumors.Conclusion[18F]AlF-FAPT can be synthesized automatically using a one-step method of aluminum fluoride. Collectively, [18F]AlF-FAPT is a better FAPI imaging agent than [18F]FAPI-42. This study proves the feasibility of using [18F]AlF-FAPT as a new radioactive tracer for PET imaging.

Production and maintenance in industries: impact of industry 4.0

Purpose Production industries are undergoing a digital transition, referred to as the fourth industrial revolution or Industry 4.0, as a result of rapidly expanding advances in information and communication technology. The purpose of this research is to provide a conceptual insight into the impact of unique capabilities from the fourth industrial revolution on production and maintenance tasks in terms of providing the existing production companies a boost by making recommendations on areas and tasks of great potential. Design/methodology/approach A survey and a literature review are among the research methods used in the research. The survey collected empirical data using a semi-structured questionnaire, which provided a broad overview of the company's present condition in terms of production and maintenance, resulting in more comprehensive and specific information regarding the study topics. Findings The study points out that, the implementation of I4.0-technology leads to an increase in production, asset utilization, quality, reduced machine down time in industries, and maintenance. Sensor technology, big data analysis, cloud technologies, mobile end devices, and real-time location systems are now being implemented to improve production processes and boost organizational competitiveness. Moreover, the study highlights that data acquired throughout the production process is utilized for quality control, predictive maintenance, and automatic production control. Furthermore, I4.0 solutions help companies to be more efficient with assets at each stage of the process, allowing them to have a stronger control on inventories and operational-optimization potential. Originality/value The findings of the study was supported by empirical data collected through survey that provides an intangible understanding of the importance of distinctive capabilities from the I4.0 revolution on production and maintenance tasks. In this study, some recommendations and guidelines to enhance these tasks are provided that are vital for existing production companies.

A Robotic Milling System Based on 3D Point Cloud

Industrial robots have advantages in the processing of large-scale components in the aerospace industry. Compared to CNC machine tools, robot arms are cheaper and easier to deploy. However, due to the poor consistency of incoming materials, large-scale and lightweight components make it difficult to automate robotic machining. In addition, the stiffness of the tandem structure is quite low. Therefore, the stability of the milling process is always a concern. In this paper, the robotic milling research is carried out for the welding pre-processing technology of large-scale components. In order to realize the automatic production of low-conformity parts, the on-site measurement–planning–processing method is adopted with the laser profiler. On the one hand, the laser profiler hand–eye calibration method is optimized to improve the measurement accuracy. On the other hand, the stiffness of the robot’s processing posture is optimized, combined with the angle of the fixture turntable. Finally, the experiment shows the feasibility of the on-site measurement–planning–processing method and verifies the correctness of the stiffness model.

Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm

In the era of rapid development in industry, an automatic production line is the fundamental and crucial mission for robotic pick-place. However, most production works for picking and placing workpieces are still manual operations in the stamping industry. Therefore, an intelligent system that is fully automatic with robotic pick-place instead of human labor needs to be developed. This study proposes a dynamic workpiece modeling integrated with a robotic arm based on two stereo vision scans using the fast point-feature histogram algorithm for the stamping industry. The point cloud models of workpieces are acquired by leveraging two depth cameras, type Azure Kinect Microsoft, after stereo calibration. The 6D poses of workpieces, including three translations and three rotations, can be estimated by applying algorithms for point cloud processing. After modeling the workpiece, a conveyor controlled by a microcontroller will deliver the dynamic workpiece to the robot. In order to accomplish this dynamic task, a formula related to the velocity of the conveyor and the moving speed of the robot is implemented. The average error of 6D pose information between our system and the practical measurement is lower than 7%. The performance of the proposed method and algorithm has been appraised on real experiments of a specified stamping workpiece.

Mechanical Design and Improvement of Computer Automatic Production Line

The mechanical design amelioration of automatic production line can intelligently adjust the production rhythm of the production line on demand, control the rhythm of production scheduling according to the production process requirements, and establish a modular design mode, so it has important research value. Based on this, this paper first analyses the mechanical design principle of the automatic production line, then studies the computer control system of the automatic production line, and finally gives the amelioration measures of the mechanical design of the automatic production line.

Automatic Assembly of Bolts and Nuts Based on Machine Vision Recognition

In this study, based on the computer vision technology, we developed a recognition system for nuts positioning to complete the automatic bolt assembly part of the automatic production line. The actual image of the nut was captured by an industrial camera, which would be processed by the following edge detection and Hough circle transformation. After that, the coordinates of the nuts were obtained in the pixel scale. Finally, the real position of nuts would be fed back to the robot arm, according to which the automatic assembly of the bolt would be completed. This computer vision based recognition system is an indispensable part for the efficiency and accuracy improvement of automatic production line.

Improved Design of Steel Pipe Transfer Device Turnover Mechanism

In the steel pipe manufacturing industry, the steel pipe manufacturing process is completed by multiple processes. And on the automatic production line, the transfer process between steel pipe working procedure is realized by the steel pipe transfer device. The unreasonable structural design of related parts in the steel pipe transfer device will result in the failure of the steel pipe transfer process to proceed smoothly, which will directly affect the normal operation of steel pipe production line, resulting in steel pipe productivity’s decrease and causing losses to steel pipe manufacturers. This article proposes an improved design method of the turnover mechanism of transfer device between steel pipe manufacturing processes.

Urban Growth Modeling Considering Educational Institutions In Qazvin, Iran: An Investigation Into The Performance of Three ANFIS Methods

Due to increasing urbanization, the rapid expansion of urban spaces has become a major environmental concern over the last few decades. Therefore, modeling the urban expansion as a complex system has been scrutinized in recent years; however, determining the rules that lead to the expansion of urban areas has always been a challenging factor in this field, especially for disaggregated models like cellular automata (CA). To overcome this issue, in this research, an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to enhance the simulation of urban growth through the automatic production of transition rules. The ANFIS can be associated with several inputs division methods, such as ANFIS accompanied by grid partitioning (ANFIS-GP), subtractive clustering (ANFIS-SC), and fuzzy c-means clustering (ANFIS-FCM). Hence, twenty-two ANFIS models based on Landsat images for the time interval from 2000 to 2010 and using different division methods were trained to investigate their effect on the efficiency of ANFIS in urban growth modeling. To examine the efficiency, the Cellular Automata-based Markov Chain (CA-MC) as a popular method was developed, and the simulation accuracy of CA-MC and the most accurate ANFIS models were obtained through comparison with observed data. The most accurate ANFIS-SC model had a Kappa of 0.76 and an overall accuracy of 93.41% for the 2019 simulated map. The results from this study reveal that the ANFIS model is effective at simulating urban expansion and the ANFIS-SC is superior to CA-MC, ANFIS-GP, and ANFIS-FCM models in urban expansion modeling.