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.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Phytotoxity of waste waters with Cr and

The dry and fresh biomass and metal concentration (Cr, Ni) in roots and shoots of mustard (S. alba L.) seedlings was evaluated in laboratory experiments with three types of washing waste-waters from cutlery production line. All tested washing waters reduced root dry mass, where-as the dry mass of shoots was either not affected or it increased. The effect of tested washing waters was stronger on fresh mass production than on dry mass production. This indicates problems in water reception and translocation. While the accumulation of Cr was higher in the roots, Ni was distributed equally through the whole plant seedling. Cr uptake in the roots and shoots was in average about 1.7 and 7.3 times, respectively, lower than that of Ni. Ni percentage uptake from washing waters in the roots and shoots was nearly equal and range from 10.2 to 15.8%.

Part dynamics in the intermediate regime of a linear vibratory feeder

Linear vibratory feeder is one of the most extensively used part feeding systems in a production line. The part motion on the feeder can be sliding or hopping or a combination of these two. Based on the dynamics of part motion this paper identifies three distinct regimes. A mathematical model was developed that can predict the trend in conveying velocity in these regimes. This model can provide the parts position as a function of time and has considered relative displacement between the part and the conveying surface. The simulation was validated by performing experiments for a range of vibration frequencies and amplitudes.

Web Service for Point Cloud Supported Robot Programming Using Machine Learning

In industrial automation, the use of robots is already standard. But there is still a lot of room for further automation. One such place where improvements can be made is in the adjustment of a production system to new and unknown products. Currently, this task includes the reprogramming of the robot and a readjustment of the image processing algorithms if sensors are involved. This takes time, effort, and a specialist, something especially small and middle-sized companies shy away from. We propose to represent a physical production line with a digital twin, using the simulated production system to generate labeled data to be used for training in a deep learning component. An artificial neural network will be trained to both recognize and localize the observed products. This allows the production line to handle both known and unknown products more flexible. The deep learning component itself is located in a cloud and can be accessed through a web service, allowing any member of the staff to initiate the training, regardless of their programming skills. In summary, our approach addresses not only further automation in manufacturing but also the use of synthesized data for deep learning.

Reclaiming the Soul of the University in an Agile Corporate World

In today's agile corporate world, the expectation is that the university will be able to rapidly adapt and evolve in response to its ever-changing global, educational, economic, social, political, and technical environments. But, at what cost? This chapter argues that many of our universities have lost their soul in their race to become agile because their focus has shifted away from fully achieving their core purpose—the creation and the dissemination of knowledge—to production-line teaching and learning and income-based research. There is now universal apprehension arising from the belief that university leaders are more concerned with income and budgets than knowledge and people. In response, this chapter argues for a radically new understanding of what constitutes truly effective university leadership which is readily able to create an agile university culture while simultaneously ensuring it sustains its commitment to its core purpose.