Search results

Most rapid prototyping (RP) relies on energy fields to handle materials, among which electricity has been much more utilized, resulting in distinctive responsiveness of non-linear, overshoot, variable inertia, etc. The purpose of this paper is to eliminate the drawbacks of array nozzle clogging, stringing, melt sagging, particularly in multi-material RP, by focusing on the electrothermal response so as to adaptively distribute thermal more accurate, rapid and balanced.

This paper presents an electrothermal response optimization method of nozzle structure for multi-material RP based on fuzzy adaptive control (FAC). The structural, physical and control model are successively logically built. The fractional order electrothermal model is identified by Riemann Liouville fractional differential equation, using the bisection method to approximate the physical model via least square method to minimize residual sum of squares. The FAC is thereafter implemented by defining fuzzy proportion integration differentiation control rules and fuzzy membership functions for fuzzy inference and defuzzification.

The transient thermodynamic and structural statics, as well as flow field analysis, are conducted. The response time, mean temperature difference and thermal deformation can be found using thermal-solid coupling finite element analysis. In physical experimental research, temperature change, together with material extrusion loading, were measured. Both numerical and physical studies have revealed findings that the electrothermal responsiveness varies with the three-dimensional structure, materials and energy sources, which can be optimized by FAC.

The proposed FAC provides an optimization method for extrusion-based multi-material RP between the balance of thermal response and energy efficiency through fulfilling potential of the hardware configuration. The originality may be widely adopted alongside increasing requirements on high quality and high efficiency RP.

The purpose of this paper is to verify the performance and function of the scale-up prototypes by predicting the material and energy consumption on the basis of dimension-reduced prototypes. Additive manufacturing (AM) costs determine carbon emissions in total life cycle, among which material and energy consumption are major components. Predicting material and energy consumption is fundamental to reducing costs.

This paper presents a material and energy co-optimization method for AM via multiple layers prediction (MLP). Material and energy consumption are predicted to reduce the AM costs. In particular, scalable, complex curved surface component is used to improve forecasting efficiency. Subsequently, the back pressure distribution is obtained by scale-up specimens, which can lay the foundation for the ergonomic conceptual design.

Taking evolutionary ergonomic product as an example, the relative gravity direction of backrest is calculated. The material and energy consumption are predicted with low deviation. Physical experiments were carried out to validate information. Digital and physical tests have revealed that material and energy co-optimization improves manufacturing efficiency.

The innovatively proposed MLP method predicts material and energy consumption of scale-up prototypes to reduce the costs. It is propitious to improve the carbon emission efficiency in life cycle of AM. The originality may be widely adopted alongside increasing environmental awareness.