Abstract

With the rise of smart manufacturing technologies and a shift towards a new industrial revolution, brings forth many new challenges, one of which is how to adapt and integrate these technologies into existing assembly lines. Scania CV AB has joined this race and, with the help of smart manufacturing solutions, works on increasing efficiency amongst its assembly lines. This thesis is aimed at creating concept designs using different smart manufacturing technologies in the assembly line of a pedal car, to evaluate and adapt the concept suited for a real assembly line. The thesis starts with studying the different smart manufacturing technologies to better understand them and the scientific methods used. This follows up with the methodology where several scientific methods such as morphological matrix and weight based decision making matrix are used to generate and evaluate different concept designs. This is followed by a qualitative analysis that helps in selecting the concept design that best suits the needs of the assembly line under consideration. The different concepts are visualized and the evaluation based on different parameters are discussed. This thesis lays a foundation to realize that an aggregate of an optimized process plan, a continuous improvement strategy and the right use of smart manufacturing technologies contributes to the productivity of the assembly line in the long run. Med en ökning av smarta tillverkningsteknologier och ett skift mot en ny industriell revolution, kommer nya utmaningar. Av dessa utmaningar ifrågasätts hur man anpassar och kombinerar dessa olika teknologier gentemot existerande monteringslinjer. Scania CV AB har tagit del i denna resa och, med hjälp av smarta tillverkningslösningar, jobbar ständigt mot att effektivisera sina monteringslinjer. Detta examensarbete fokuserar på att använda olika och smarta tillverkningsteknologier i en monteringslina för tillverkning av en trampbil. Detta görs genom att evaluera och anpassa olika koncept som är lämpade för en verklig monteringslina. Examensarbetet börjar med en undersökning av nuläget för att få en bättre uppfattning om smarta tillverkningsteknologier samt de vetenskapliga metoder som används. Även här så undersöks referensprodukten - trampbil samt de olika programvarorna AviX, ExtendSim och LayCAD. Genom att ha satt tydliga arbetsmetoder och följt upp mot dessa så diskuterar och hänvisar nästa kapitel resultaten. Resultaten visar hur olika koncept har tagits fram samt vilka teknologier som går ihop med dessa. Fördelar, nackdelar och risker hos respektive teknologi har benämnts. Ett systematiskt arbetssätt har tagits fram mot hur man anpassar konceptet för monteringslinan av en trampbil till en verklig monteringslina, samt hur man har jobbat runt de restriktioner som uppkommit. Examensarbetet slutar med ett kapitel där slutsatserna, på en överskådlig nivå, tas fram. Hur dessa teknologier har kombinerats och evaluerats, samt att koncepterna som har tagits fram har lagt en grund för framtida projekt att följa upp emot.

Abstract

Over the past years, the manufacturing industry has witnessed a great enhancement in the field of Additive Manufacturing that allows forming an inchoate market to produce custom-made products with low unit costs. At the same time, everyone may be a designer as well. However, the required equipment is only accessible for all the people. The Cloud Manufacturing aims to resolve this challenge by connecting the customers to the manufacturers. Therefore, this paper introduces a practical fabrication method for 3D printers which can bring flexibility and benefits to cloud manufacturing scheme. A model of this integration is provided and implemented on two types of 3D printers. Further, by printing sample specimens, the advantages of this approach such as using low memory size for operating each printer and the ability of changing the design during the fabrication process is described in details.

Abstract

Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline. Document type: Article