Artificial intelligence-based tool condition monitoring in robotic incremental sheet forming through vibration, tool wear and surface roughness analyses

Sheet metal forming is a fabrication process that allows sheet metal to be formed in 3D shapes with the use of a specific tool and die. However, the conventional sheet metal forming has disadvantages in terms of quality and low flexibility, and it also prolongs the time-to-market in producing low costs prototype products. Robot-based incremental sheet forming (ISF) is a new prospect and one of the relatively new sheet metal forming processes to fabricate a product with 3D complex shapes. Interests in new techniques with a variety approach for forming processes have created more studies by researchers on the robot-based ISF process. However, tool wear always makes the difficulty of the ISF process for sustaining the process performance. In the present study, the development of a comprehensive predictive model for tool wear in robot-based ISF using artificial intelligence (AI) has been conducted. The model would predict the critical degradation of tool wear and simultaneously the relationship with quality of the formed workpiece surface. The robot-based ISF experiments were carried out using a forming tool of AISI D2 tool steel with a 10 mm diameter that attached to the ABB IRB 4400/60 IRC5 industrial robotic arm. Three different materials of SUS316 stainless steel, Cu60Zn40 copper alloy and AA3003 aluminum alloy with 0.5 mm thickness were used as workpieces. As preliminary experiments, a parametric optimization was carried out to determine optimum processing parameters in robot-based ISF using L18 orthogonal array design of experiments. The vibration signals of the ISF process were recorded by the accelerometer sensors, which are located on the forming tool and workpiece. Subsequently, after the vibration signals through signal processing, pattern recognition was conducted to identify and categorize the tool condition by two clusters, which are a tool in good condition and worn out. The increasing of surface roughness on the workpieces can also be seen noticeably with the increasing of vibration on the forming process due to tool wear. This proving that vibration signals can provide the tool wear identification for the ISF process. The predictive models were developed and compared between three different AI models, which are artificial neural network (ANN), fuzzy logic (FL) and adaptive network-based fuzzy inference system (ANFIS). The prediction using ANN model with two hidden layers showed that it has an excellent prediction accuracy of 99.94 % for tool wear (architecture 2-4-4-1) and 91.77 % (architecture 2-5-3- 1) for surface roughness. The use of the ANN with two hidden layers is the best model to predict the tool wear in robot-based ISF. The successful development of prediction of tool wear in robot-based incremental sheet forming can provide a significant way in tool condition monitoring system to minimize downtime related with tool damaged and affected the quality of the workpieces.

Text Nazarul Abidin Ismail - IR.pdf Download (1MB)

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