Theoretical Study on Non-transmission High Efficient Parallel Camber Grinding Machine

Be directed against the development trend of modern CNC grinding machine towards high precision and high efficiency, some general weaknesses of existing camber grinding machine are analyzed in detail. In order to develop new type CNC camber grinding machine that can grind complex die, and genuinely achieved accurate feed and high efficient grinding, a new type camber grinding machine is put forward, called non-transmission virtual-shaft CNC camber grinding machine. Its feed system is a parallel mechanism that is directly driven by linear step motor. Therefore, traditional transmission types, such as the ball lead-screw mechanisms, the gears, the hydraulic transmission system, etc. are cancelled, and the feed system of new type CNC camber grinding machine can truly possess non-creep, good accuracy retentiveness a wide range of feed-speed change, high kinematical accuracy and positioning precision, etc. In order to realize that the cutting motion is provided with high grinding speed, step-less speed variation, high rotational accuracy, good dynamic performance, and non-transmission, the driving technology of hollow rotor motor is applied to drive the spindle of new type grinding machine,thus leading to the elimination of the transmission parts of cutting motion. The principle structure model of new type camber grinding machine is advanced. The selection, control gist and driving circuit line of the linear step motor are expounded. The main technology characteristics and application advantages of non-transmission virtual-shaft CNC camber grinding machine are introduced.

Development of the precision feeding system for sows via a rule-based expert system

To precisely meet the nutritional requirements of sows during the stages of pregnancy and lactation,a precision feeding system was developed by using the intelligent sow feeder combined with a rule-based expert system and the Internet of Things(IoTs).The model of uncertain knowledge representation was established for inference by using the certainty factor.The daily feeding amount of each sow was calculated by the expert system.An improved pattern matching algorithm Reused Degree Model-RETE(RDM-RETE)was proposed for the decision of daily feeding amount,which sped up inference by optimizing the RETE network topology.A prediction model of daily feeding amount was established by a rule-based expert system and the precision feeding was achieved by an accurate control technology of variable volume.The experimental results demonstrated that the HASH-RDM-RETE algorithm could effectively reduce the network complexity and improve the inference efficiency.The feeding amount decided by the expert system was a logarithmic model,which was consistent with the feeding law of lactating sows.The inferential feeding amount was adopted as the predicted feed intake and the coefficient of correlation between predicted feed intake and actual feed intake was greater than or equal to 0.99.Each sow was fed at different feeding intervals and different feed amounts for each meal in a day.The feed intake was 26.84% higher than that of artificial feeding during lactation days(p<0.05).The piglets weaned per sow per year(PSY)can be increased by 1.51 compared with that of relatively high levels in domestic pig farms.This system is stable in feeding and lowers the breeding cost that can be applied in precision feeding in swine production.

Precision Livestock Farming: Precision feeding technologies and sustainable livestock production

In order to be able to produce safe,uniform,cheap,environmentally-and welfare-friendly food products and market these products in an increasingly complex international agricultural market,livestock producers must have access to timely production related information.Especially the information related to feeding/nutritional issues is important,as feeding related costs are always significant part of variables costs for all types of livestock production.Therefore,automating the collection,analysis and use of production related information on livestock farms will be essential for improving livestock productivity in the future.Electronically-controlled livestock production systems with an information and communication technology(ICT)focus are required to ensure that information is collected in a cost effective and timely manner and readily acted upon on farms.New electronic and ICT related technologies introduced on farms as part of Precision Livestock Farming(PLF)systems will facilitate livestock management methods that are more responsive to market signals.The PLF technologies encompass methods for electronically measuring the critical components of the production system that indicate the efficiency of resource use,interpreting the information captured and controlling processes to ensure optimum efficiency of both resource use and livestock productivity.These envisaged real-time monitoring and control systems could dramatically improve production efficiency of livestock enterprises.However,further research and development is required,as some of the components of PLF systems are in different stages of development.In addition,an overall strategy for the adoption and commercial exploitation of PLF systems needs to be developed in collaboration with private companies.This article outlines the potential role PLF can play in ensuring that the best possible management processes are implemented on farms to improve farm profitability,quality of products,welfare of livestock and sustainability of the farm environment,especially as it related to intensive livestock species.

Prediction of the diet nutrients digestibility of dairy cows using Gaussian process regression

In order to effectively evaluate the diet nutritional value of dairy cows,it is essential to accurately predict the diet nutrients digestibility(DND).Conventional predicting DND methods are usually based on the least squares linear regression analysis(LS-LRA),which often relies on a large amount of training samples to accomplish reliable predictions.However,in real-world applications,it is often extremely difficult,costly and time-consuming to obtain a large number of measured samples,especially for the DND prediction of dairy cows.This paper applies a Gaussian process regression(GPR)technique to predict the DND indicators of dairy cows in small samples.To evaluate prediction accuracy effectively,we compared the GPR technique with the LS-LRA,radial basis function artificial neural network(RBF-ANN),support vector regression(SVR)and least squares support vector regression(LS-SVR)methods,using the required sample data obtained from actual digestion experiments.The prediction results indicate that the GPR technique is superior to other conventional methods(especially the LS-LRA method)in predicting the main DND indicators of dairy cows such as dry matter digestibility(DMD),organic matter digestibility(OMD),neutral detergent fiber(NDFD),acid detergent fiber(ADFD),and crude protein digestibility(CPD).It is worth mentioning that the developed GPR-based prediction technique is more suitable for the prediction problems with small samples,which is often the case in the prediction of DND indicators of dairy cows,and then more coincide with actual needs.