Novel application of mapping method from small displacement torsor to tolerance: Error optimization design of assembly parts

The manufacturing/assembly error of machine parts is a key factor that influences the performance and economy of mechanical systems. To achieve high assembly precision and performance on the basis of low manufacturing accuracy and cost, this study primarily optimizes the assembly error of machine parts. First, the small displacement torsor is used to characterize the small deformation between the mating surfaces of parts. Subsequently, to realize the combination of small displacement torsor and tolerance, the small displacement torsor with manufacturing error and assembly deformation is mapped to the tolerance domain. Second, based on the relationship between small displacement torsor and tolerance, an assembly error optimization model is established on the basis of the conventional tolerance-cost model, considering the emergence of manufacturing error and assembly deformation. Third, aiming at the high-pressure rotor for aeroengines, the error optimization design of assembly is carried out developed with the assembly accuracy requirement as the constraint condition, and the total costs of the manufacturing and assembly processes as the objective. The optimization results indicate that the manufacturing error range of each mating surface after optimization changes, from small to large, under the premise of ensuring the product’s performance, which verifies that the difficulty in processing parts is reduced, and that the efficiency of parts processing is also improved. Meanwhile, the relative manufacturing cost after optimization is reduced by 6.79%, which reflects the economic requirements to a certain extent. The content of this article provides the necessary design basis and reference for the realization of high assembly accuracy of mechanical systems, under low cost requirements from the design perspective.

Colorants in coatings

The aim of this chapter is to provide a compact overview of colorants and their use in coatings including a brief introduction to paint technology and its raw materials. In addition, it will focus on individual colorants by collecting information from the available literature mainly for their use in coatings. Publications on colorants in coatings applications are in many cases standard works that cover the wider aspects of color chemistry and paint technology and are explicitly recommended for a more detailed study of the subject [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. Articles or information on paint formulation using coatings which contain colorants are rare [19]. This formulation expertise is often company property as it is the result of many years of effort built up over very long series of practical “trial-and-error” optimization tests and, more recently, supported by design of experiment and laboratory process automation [20, 21]. Therefore, it is protected by rigorous secrecy agreements. Formulations are in many ways part of a paint manufacturer’s capital, because of their use in automotive coatings, coil coatings, powder coatings, and specialist knowledge is indispensable to ensure their successful industrial use [22]. An important source to learn about the use of pigments in different coating formulations are guidance or starting formulations offered by pigment, additive, and resin manufacturers. These are available upon request from the technical service unit of these companies. Coating formulations can also be found scattered in books on coating and formulation technology [4, 5, 18, 23,24,25,26,27]. This overview can in no way claim to be complete, as the literature and relevant journals in this field are far too extensive. Nevertheless, it remains the author’s hope that the reader will gain a comprehensive insight into the fascinating field of colorants for coatings, including its literature and current research activities and last but not least its scientific attractiveness and industrial relevance.

Brushless direct current motor with MV-PID controller management system based on arm microcontroller

Purpose The brushless direct current motor (BLDCM) is widely accepted and adopted by many industries instead of direct current motors due to high reliability during operation. Brushless direct current (BLDC) has outstanding efficiency as losses that arise out of voltage drops at brushes and friction losses are eliminated. The main factor that affects the performance is temperature introduced in the internal copper core windings. The control of motor speed generates high temperature in BLDC operation. The high temperature is due to presence of ripples in the operational current. The purpose is to present an effective controlling mechanism for speed management and to improve the performance of BLDCM to activate effective management of speed. Design/methodology/approach The purpose is to present an optimal algorithm based on modified moth-flame optimization algorithm over recurrent neural network (MMFO-RNN) for speed management to improve the performance. The core objective of the presented work is to achieve improvement in performance without affecting the design of the system with no additional circuitry. The management of speed in BLDCM has been achieved through reduction or minimization of ripples encircled with torque of the motor. The implementation ends in two stages, namely, controlling the loop of torque and controlling the loop of speed. The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol. Findings The parameters are enriched with Multi Resolution Proportional Integral and Derivative (MRPID) controller operation to achieve minimal ripples for the torque of BLDC and manage the speed of the motor. The performance is increased by adopting this technique approximately 12% in comparison with the existing methodology, which is the main contributions of the presented work. The outcomes are analyzed with the existing methodologies through MATLAB Simulink tool, and the comparative analyses suggest that better performance of the proposed system produces over existing techniques, and proto type model is developed and cross verifies the proposed system. Originality/value The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol. The parameters are enriched with MRPID controller operation to achieve nil or minimal ripples and to encircle the torque of Brushless Direct Current and manage the speed.

Psychological analysis of classroom learning based on face recognition and neural network

With the rapid development of deep learning and parallel computing, deep learning neural network based on big data has been applied to the field of facial nerve recognition. This innovative operation has attracted extensive attention of scholars. The reason why the application of neural network is realized lies in deep learning, which can reduce the error and change the weight by means of back propagation and error optimization, so as to extract more key points and features. In spite of this, data collection and key points extraction is still a very complex problem. This paper mainly aims at the above problems, studies the way of deep learning and information extraction and its internal structure, and optimizes its application to classroom learning, so as to provide effective help for the realization of distance education.