Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine

Lower cost automotive piston from 2124/SiC/25p metal-matrix composite

Engineered materials have made a breakthrough in a quest for materials with a combination of custom-made properties to suit particular applications. One of such materials is 2124/SiC/25p, a high-quality aerospace grade aluminium alloy reinforced with ultrafine particles of silicon carbide, manufactured by a powder metallurgy route. This aluminium matrix composite offers a combination of greater fatigue strength at elevated temperatures, lower thermal expansion and greater wear resistance in comparison with conventionally used piston materials. The microscale particulate reinforcement also offers good formability and machinability. Despite the benefits, the higher manufacturing cost often limits their usage in high-volume industries such as automotive where such materials could significantly improve the engine performance. This paper presents mechanical and forging data for a lower cost processing route for metal matrix composites. Finite element modelling and analysis were used to examine forging of an automotive piston and die wear. This showed that selection of the forging route is important to maximise die life. Mechanical testing of the forged material showed a minimal reduction in fatigue properties at the piston operating temperature

Automotive leaf spring design and manufacturing process improvement using failure mode and effects analysis (FMEA)

Nowadays human safety and comfort are the most considerable parameters in designing and manufacturing of a vehicle, that is why every organization ensures the quality and reliability of components used in the vehicle. Leaf spring is also a component of vehicle which plays an important role in human safety and comfort. It acts as a structural member and an integral part of suspension system. It is important to eliminate the failures in designing and manufacturing process of leaf springs because of its importance in functionality and safety of vehicle. In this research, failure mode and effects analysis has been used to analyze and reduce the risks of 42 possible failures that can occur in automotive leaf spring. It starts from determining, classifying, and analyzing all potential failures and then rating them with the help numeric scores. The four numeric scores namely severity, occurrence, detection, and Risk Priority Number (RPN) are used to find the high potential failures of semi-elliptical leaf springs. In the end, actions are recommended for RPN greater than 250, to increase quality and reliably of product. </jats:p

Simulation-based analytical design for aluminium recycling processing plant

Indiscriminate disposal of beverage cans as waste poses a great threat to the environment, causing flooding, landfill, and blockage of drainages, leading to land pollution and sometimes accident. Hence, there is a need to design a system capable of converting these wastes into usable products. In this study, a simulation-based analytical design for aluminum recycling processing plant was carried out to ascertain the efficiency and reliability of the design before fabrication using finite element analysis (FEA) approach. The simulation results revealed a lesser maximum stress of 6.323 MPa for the furnace outer casing under the action of load with a displacement of 0.0795 mm. The stress of the machine components is less than the yield strength of the selected materials, making the machine fit and workable. The analytical results agree with the numerical analysis; hence the conceptual design is fit for fabrication based on the design analysis and evaluation. After the design analysis and simulation, the designed recycling process plant parts are found to be under negligible deflection and stress which is far below the yield strength of chosen materials

Thermal-hydraulic modelling and analysis of hydraulic damper for impact cylinder with large flow

The hydraulic damper has a great sense for impact machine to extend life and improve the environmental performance. The objective of this paper is to provide a systematic investigation to design or evaluation of a hydraulic damper used in the impact machine. A novel hydraulic damper using guiding sleeve to enlarge buffer chamber area is designed and manufactured by ingenious tactics. The performance of a prototype hydraulic damper is acquired by the test. A nonlinear thermal-hydraulic model for the hydraulic damper is presented by analyzing the internal fluid dynamic phenomenon and heat transfer with respect to the prototype. Comparisons between test data and simulation result confirm the validity of the thermal-hydraulic model. In the meantime, evaluation of the importance of some key factors using the model for designing is discussed. It shows the influence of orifice diameter, inner diameter of buffer chamber and setting pressure of the relief valve to hydraulic damper characteristics with large flow, which gives a theoretical basis to design and optimize hydraulic damper with large flow for impact machine