# Production Optimization of Premium Food Can with Distortion Printing under Waving Requirement
###### Abstract  
This research aims to propose a novel approach for evaluating and minimizing scraps in an industrial production of premium food cans with distortion printing. Beyond conventional formability criteria, a waving requirement is introduced to ensure aesthetic quality of the printed graphics. The research focuses on real production conditions, specifically involving double-cold-reduced (DR) low-carbon steel sheets and chromium-coated tin-free steel with a thickness of 0.16 mm. The sheets are laminated on both sides with a plastic film prior to undergoing distortion printing on the exterior. Subsequently, a blank is subjected to a drawing-redrawing process to form a food can. To address challenges associated with characterizing these thin sheets, a material parameter identification method is proposed and demonstrated. The thickness profile and flange length are identified as key criteria for this identification process. Measurements of thickness distribution and flange length are obtained using digital image correlation (DIC) and microscopy techniques. Within the manufacturing system, uncertainties related to material properties and forming processes can result in scraps or defects. To analyze these processes, finite element analysis (FEA) is employed and validated through experiments. For the evaluation of scrap rates, uncertainty propagation is conducted using a metamodeling technique, specifically employing radial basis function (RBF) neural networks. The study concludes by offering process optimization recommendations aimed at reducing the scrap rate. waving; scrap rate; distortion printing; food can production; parameter identification +
Footnote †: journal:  
Today, manufacturing business operators are striving to enhance product quality and accelerate the development of various technologies. Quality is one of the leading criteria in a business success. Total productive maintenance (TPM) is deployed inside the metal-forming industry to improve metal industry workstations, and the overall equipment effectiveness (OEE) is utilized in [4]. The deep drawing process is crucial to the metal food packaging industry, with a high production rate and excellent dimensional accuracy. Utilizing a very thin steel sheet, which possess high hardness and high yield strength, is a key aspect of this production. Designing tooling and determining production parameters can be challenging due to the need for extensive trial and error to achieve optimal tooling shapes and production conditions. Advanced computer modeling to simulate metal-forming behavior is vital in reducing the time and cost associated with trial and error. Waving failure is a significant issue in distortion printing food cans, whereas it is not a concern in traditional cans. Figure 2 shows an accepted can and a rejected can due to the waving defect. The red circle in the figure shows the graphical distortion as a defect. For general practice in industry, a can with a waving height greater than 1.0 mm is rejected. A visual inspection in a real production line after several engineering improvements has revealed a scrap rate of 1.59%, significantly higher than the target of 0.25%. A high scrap rate indicates a low production and quality control efficiency, adversely affecting production output and manufacturing costs. FEA is an important tool for analyzing and designing the DRD process. Discrepancies between FEA and experimental results can arise from numerical errors in the FEA or changes in input variables. Numerous studies have utilized FEA to deterministically solve sheet metal-forming problems. For instance, the effects of tooling conditions on the deep drawing process of C.R.1 steel cylindrical cups with an initial thickness of 0.9 mm are studied using FEA and experimental methods in [5]. The forming optimization of ultra-low carbon steel with a thickness of 0.7 mm via inverse evolutionary search is presented in [6].