Evalaution and optimization of laser cutting parameters for plywood materials

Laser process parameters influence greatly the width of kerfs and quality of the cut edges. This article reports experiments on the laser plywood-cutting performance of a CW 1.5 kW CO2¬ Rofin laser, based on design of experiments (DOE). The laser was used to cut three thicknesses 3, 6 and 9 mm of plywood panels. The process factors investigated are: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the cutting edge quality parameters namely: upper kerf (UK), lower kerf (LK), the ratio between upper to lower kerfs and the operating cost to the process parameters mentioned above. Mathematical models were developed to establish the relationship between the process parameters and the edge quality parameters, and special graphs were drawn for this purpose. Finally, a numerical optimization was performed to find out the optimal process setting at which both kerfs would lead to a ratio of about 1, and at which low cutting cost take place

6.06 – Mathematical Modeling of Weld Phenomena, Part 2: Design of Experiments and Optimization

The objective of this chapter is to give an essential presentation on the theory and practice of implementing modelling and optimization techniques to evaluate the welding process. This chapter includes comprehensive research works on the Design of Experiments (DoE.) The application of DoE, evolutionary algorithms, and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality

Optimization Techniques in Material Processing

The objective of this chapter is to give an essential presentation on the theory and practice of implementing modeling and optimization techniques to evaluate different manufacturing process for a wide range of materials. This chapter includes comprehensive research works on the Design of Experiments (DoE), and the Artificial Neural Network (ANN) that is widely used to develop a mathematical relationship between the process input parameters and the output

An innovative cost modelling system to support lean product and process development

This paper presents a cost modelling system for lean product and process development to support proactive decision making and mistake elimination at the design stage. The foundations of the system are based upon three lean product and process development enablers, namely: Set-based concurrent engineering, knowledge-based engineering, and mistake proofing (Poka-yoke). The development commenced with an industrial field study of eleven leading European industries from the aerospace, automotive, telecommunication, medical and domestic appliance sectors. Based on the requirements of industrial collaborators, the developed system comprises six modules: value identification, manufacturing process/machines selection, material selection, geometric features specification, geometric features and manufacturability assessment, and manufacturing time and cost estimation. The work involved the development of a feature-based cost estimation method for the resistance spot welding process. The developed system was finally validated using an industrial case study. The developed system has the capability to provide estimates related to product cost and associated values concurrently, facilitate decision making, eliminate mistakes during the design stage, and incorporate ‘customer voice’ during a critical decision making stage