Manufacturing parameters of brake friction materials have a crucial impact on its friction and wear behavior. The effects of manufacturing parameters on wear have been modeled aiming at the optimization of facing behaviors. The influences of the five most important manufacturing parameters, such as molding pressure, molding time, molding temperature, heat-treatment temperature, and heat-treatment time, have been investigated. Their effects have been investigated on different formulations of the friction material and under different brake interface temperatures. The neuro-genetic optimization model was developed to be able to predict and optimize the effects of manufacturing parameters on the brake friction material wear.

The curing process of thermosetting resins plays a key role on the final quality of the composite material components. Soft computing techniques proved to be an efficient method to control and optimize the curing process, replacing the conventional experimental and numerical approaches. In this paper artificial neural network (ANN) and fuzzy logic control (FLC) were implemented together to predict and control the temperature and degree of cure profile during the autoclave curing process. The obtained outcomes proved the capability of ANNs and FLC with respect to the hard computing methods.

Increasing dynamic performance and the general level of automation of commercial vehicles emphasize the issue of safety. Modern braking systems focus on sustaining vehicle stability, often degrading the brake performance. The major downgrades of the braking performance are nearly impossible to model using a classical mathematical approach, making them not feasible to use in real braking system controllers. In this paper, the use of combined Neural Networks and Fuzzy logic for the control of the braking system of a commercial vehicle while maximizing performance and sustaining stability is proposed. The control system comprises adhesion estimation, an inverse brake model, and a fuzzy logic controller to keep the system giving optimal control signals in various brake conditions while sustaining vehicle stability and steerability. The results based on a semi-trailer system reveal the success of the proposed AI-based braking system algorithm while braking under varying conditions. Graphical abstract