EXTENDING ORIGAMI TECHNIQUE TO FOLD FORMING OF SHEET METAL PRODUCTS

This dissertation presents a scientific based approach for the analysis of folded sheet metal products. Such analysis initializes the examination in terms of topological exploration using set of graph modeling and traversal algorithms. The geometrical validity and optimization are followed by utilizing boundary representation and overlapping detection during a geometrical analysis stage, in this phase the optimization metrics are established to evaluate the unfolded sheet metal design in terms of its manufacturability and cost parameters, such as nesting efficiency, total welding cost, bend lines orientation, and maximum part extent, which aides in handling purposes. The proposed approach evaluates the design in terms of the stressed-based behavior to indicate initial stress performance by utilizing a structural matrix analysis while developing modification factors for the stiffness matrix to cope with the stress-based differences of the diverse flat pattern designs. The outcome from the stressed-based ranking study is mainly the axial stresses as exerted on each element of folded geometry; this knowledge leads to initial optimizing the flat pattern in terms of its stress-based behavior. Furthermore, the sheet folding can also find application in composites manufacturing. Thus, this dissertation optimizes fiber orientation based on the elasticity theory principles, and the best fiber alignment for a flat pattern is determined under certain stresses along with the peel shear on adhesively bonded edges. This study also explores the implementation of the fold forming process within the automotive production lines. This is done using a tool that adopts Quality Function Deployment (QFD) principle and Analytical Hierarchy Process (AHP) methodology to structure the reasoning logic for design decisions. Moreover, the proposed tool accumulates all the knowledge for specific production line and parts design inside an interactive knowledge base. Thus, the system is knowledge-based oriented and exhibits the ability to address design problems as changes occur to the product or the manufacturing process options. Additionally, this technique offers two knowledge bases; the first holds the production requirements and their correlations to essential process attributes, while the second contains available manufacturing processes options and their characteristics to satisfy the needs to fabricate Body in White (BiW) panels. Lastly, the dissertation showcases the developed tools and mathematics using several case studies to verify the developed system\u27s functionality and merits. The results demonstrate the feasibility of the developed methodology in designing sheet metal products via folding

Assembly line design using a hybrid approach of lean manufacturing and balancing models

This manuscript presents a method to design automotive production assembly lines that integrate lean manufacturing approaches and line balancing algorithms. In this work, we develop a clustering algorithm and task mutuality index for the assembly tasks to redesign and rebalance an assembly production line with a fixed layout and machinery. Moreover, we consider the product demand variability and the introduction of new product models. This paper describes the current and the future states of an automotive engines assembly production line using quantitative performance parameters and assembly tasks content flow charting, along with the analysis and results obtained from the proposed approach. The results show a reduction in required modeling and optimization efforts and a reduction in the required time to generate a feasible redesign of an assembly line while reaching the required takt time based on the demand forecast

Recent Advancements in Post Processing of Additively Manufactured Metals Using Laser Polishing

The poor surface roughness associated with additively manufactured parts can influence the surface integrity and geometric tolerances of produced components. In response to this issue, laser polishing (LP) has emerged as a potential technique for improving the surface finish and producing parts with enhanced properties. Many studies have been conducted to investigate the effect of LP on parts produced using additive manufacturing. The results showed that applying such a unique treatment can significantly enhance the overall performance of the part. In LP processes, the surface of the part is re-melted by the laser, resulting in smaller peaks and shallower valleys, which enable the development of smoother surfaces with the help of gravity and surface tension. Precise selection of laser parameters is essential to achieve optimal enhancement in the surface finish, microstructure, and mechanical properties of the treated parts. This paper aims to compile state-of-the-art knowledge in LP of additively manufactured metals and presents the optimal process parameters experimentally and modeling using artificial machine learning. The effects of laser power, the number of laser re-melting passes, and scanning speed on the final surface roughness and mechanical properties are comprehensively discussed in this work