An investigation of worn work roll materials used in the finishing stands of the hot strip mill for steel rolling

The surface failure characteristics of different work roll materials, i.e. High Speed Steel, High Chromium Iron and Indefinite Chill Iron, used in the finishing stands of a hot strip mill have been investigated using stereo microscopy, 3D optical profilometry, scanning electron microscopy and energy dispersive X-ray spectroscopy. The results show that the surface failure mechanisms of work rolls for hot rolling are very complex, involving plastic deformation, abrasive wear, adhesive wear, mechanical and thermal induced cracking, material transfer and oxidation. Despite the differences in chemical composition and microstructure, the tribological response of the different work roll materials was found to be strongly dependent on the material microstructure and especially the presence and distribution of microstructural constituents, such as the different carbide phases and graphite (in the case of Indefinite Chill Iron). Cracking and chipping of the work roll surfaces, both having a negative impact on work roll wear, are strongly influenced by the presence of carbides, carbide networks and graphite in the work roll surface. Consequently, the amount of carbide forming elements as well as the manufacturing process must be controlled in order to obtain an optimised microstructure and a predictable wear rate.

Buried screen-printed contacts for silicon solar cells

A Simple way to improve solar cell efficiency is to enhance the absorption of light and reduce the shading losses. One of the main objectives for the photovoltaic roadmap is the reduction of metalized area on the front side of solar cell by fin lines. Industrial solar cell production uses screen-printing of metal pastes with a limit in line width of 70-80 μm. This paper will show a combination of the technique of laser grooved buried contact (LGBC) and Screen-printing is able to improve in fine lines and higher aspect ratio. Laser grooving is a technique to bury the contact into the surface of silicon wafer. Metallization is normally done with electroless or electrolytic plating method, which a high cost. To decrease the relative cost, more complex manufacturing process was needed, therefore in this project the standard process of buried contact solar cells has been optimized in order to gain a laser grooved buried contact solar cell concept with less processing steps. The laser scribing process is set at the first step on raw mono-crystalline silicon wafer. And then the texturing etch; phosphorus diffusion and SiNx passivation process was needed once. While simultaneously optimizing the laser scribing process did to get better results on screen-printing process with fewer difficulties to fill the laser groove. This project has been done to make the whole production of buried contact solar cell with fewer steps and could present a cost effective opportunity to solar cell industries.

Skapa förutsättningar för att implementera Lean production i en tillverkningsprocess som kännetecknas av HVLV : - En fallstudie på Tibrokök Sverige AB

Idag är det vanligt att företagen konkurrerar med produkter som innefattar en fysisk vara som har utvidgats med olika tjänster för att kunna tillfredsställa kundens behov. I takt med detta ökar produkternas komplexitet och högre krav ställs på leverantörerna. Denna utveckling har lett till att många företag som tillverkar produkter med hög variation måste arbeta helt kundorderstyrt för att kunna tillgodose kundernas ökande krav. Företagen som tillverkar dessa komplexa produkter har en tillverkning som karaktäriseras av hög variation och låg volym vilket benämns med förkortningen HVLV. Utmaningen för dessa HVLV-företag ligger i att ha en hög produktmixflexibilitet med så låg resursanvändning som möjligt. För att effektivisera verksamheten har många företag därför intresserat sig för Lean production som har visat sig vara ett framgångsrikt koncept för tillverkande företag runt om i världen som effektiviserat sin produktion. Ett flertal artiklar har uppmärksammat begränsningar vid implementeringen av Lean production i HVLV-miljöer. Artiklar pekar vidare på behovet av ytterligare forskning kring Lean productions applicerbarhet i HVLV-miljöer och detta var uppkomsten till examensarbetets bakgrund och syfte. En fallstudie har genomförts på Tibrokök som är ett företag med en helt kundorderstyrd produktion som kännetecknas av HVLV. Med hjälp av fallstudien syftar examensarbetet till att undersöka om verktygen processkartläggning inkl. tidsstudier och layoutflödesdiagram kan bidra till att skapa förutsättningar för implementering av Lean production i en enskild tillverkningsprocess i en HVLV-miljö. Trots att verktygen har behövt anpassas något så anses de ha varit användbara och bidragande till skapandet av förutsättningar för implementering av Lean production i denna HVLV-miljö. Detta eftersom vi kunde identifiera många orsaker till slöseri samt ta fram ett förslag som skapar effektiviseringar av Tibroköks ytbehandlingsprocess i det framtida läget.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.