Mathematical modelling for energy efficiency improvement in laser welding

Abstract

Within the global manufacturing industry, there is increasing recognition of the need to improve energy efficiency, to reduce both costs and carbon footprint. Significant work has sought to improve the energy efficiency of a variety of different processes, but in the case of laser welding, research has primarily focussed on the lasermaterial interaction and not on energy rationalisation. This work addresses this knowledge gap by methodically investigating and analysing the energy requirements of a laser welding process. In this study, a mathematical model has been created in order to take a "whole-system" approach to laser welding electrical demand, accounting for all component sub-systems of the laser cell. This model was then experimentally tested via use of an electrical energy monitor to gather energy data for an autogenous welding process carried out on 316L stainless steel at a variety of parameters. Mathematical analysis of this data was then used to create a matrix of electrical power draws at different test conditions, which was further developed into an analysis of process productivity. This revealed a very strong non-linear inverse correlation between process rate (kg/hr) and specific energy consumption (J/kg). This process productivity measurement was placed in context with other manufacturing processes, revealing that laser welding has a relatively high process rate (1E-02 -1E0 kg/hr), and relatively low energy consumption (1E+08 and 1E+09 J/kg) in comparison. A further benefit of this model is that it allows the selection of processing parameters according to their energy demand. Energy flow analysis allows the direct comparison of the energy demand of different sets of processing parameters, which metallurgical analysis shows produce similar welds. In this study, it was shown that parameter selection alone was capable of producing an electrical energy saving of 60% for a given weld. This emphasises the importance of parameter selection and control in enabling environmentally cleaner manufacturing without compromising part quality or the need for investment in capital equipment.

Industrial Energy Optimisation: A Laser Cutting Case Study

“…A methodology for measuring and calculating the Specific Energy Consumption (SEC) (J/kg) for a laser process which incorporates the direct energy-consuming sub-systems and operating states of a complete laser system has been developed and demonstrated in a research environment [31,32]. The subject of this paper is the adaption of that existing methodology to an industrial case study.…”

“…Figure 7 compares the productivity plot for industrial laser cutting against the previously published data for laboratory-based laser welding [32] (Sig. : C 1 = 0.000, C 0 = 0.000).…”

“…In this work, the statistical modelling derived by Goffin et al [32] was applied to an industrial laser cutting process. The most immediate consequence of this was a significant reduction in the number of available data points for statistical analysis from 21 to 6.…”

“…However, that would be the next logical step. Previous parameter experimentation [32] indicates that considerable energy savings can be realised when processing parameters are optimised for energy efficiency, with a 60% saving identified for laser welding. The potential benefits of applying such an investigation to laser cutting are therefore also considerable.…”

Energy consumption and performance optimisation of laser cleaning for coating removal

“…The laser de-coating data carried out here can be considered as high-precision, low -throughput processes. In the research by Goffin et al [26], the specific energy consumption of laser welding system has been investigated and similar comparison applied on the Gutowski's hockey stick diagram. Since the laser welding mechanism is material melting, rather than the mainly vapourisation of laser de-coating, the specific energy consumption of laser welding is about 100 times less than laser de-coating, and processing rate is 100 times faster.…”