Koordinaattimittauksen kehittäminen metsäkonetuotannossa

Tämän diplomityön tarkoituksena oli koordinaattimittauksen kehittäminen ja systematisointi metsäkoneen valmistuksessa. Työssä tutkittiin kannettavalla nivelvarsimittalaitteella tehtävää koordinaattimittausta tehtaan omille tuotteille, valmistuksen apuvälineille ja toimittajilta tuleville osille. Tavoitteena oli mittaustoiminnan kokonaisvaltainen kehittäminen ja vastauksia haettiin seuraaviin tutkimuskysymyksiin: Mitä kohteita tulee mitata? Missä tuotantovaiheessa mittausta tarvitaan? Kuinka mittaustuloksia tulee käsitellä ja hyödyntää? Kuinka paljon mittaustoiminta vaatii resursseja ja kuinka hyvin käytössä oleva koordinaattimittauslaite soveltuu yrityksen tuotantoon? Lisäksi pohdittiin koordinaattimittauksen merkitystä yritykselle ja etsittiin kehityskohteita mittaustoiminnasta? Tutkimusaineistona käytettiin vuosien 2007 – 2008 aikana saatuja koordinaattimittauskokemuksia ja mittaustuloksia. Lisäksi tutkittiin yrityksen sisäisiä poikkeamaraportteja ja tutustuttiin koordinaattimittauslaitteen ja mittausohjelman toimintaan sekä yrityksen tuotantoon. Tutkimuksessa tultiin siihen tulokseen, että koordinaattimittauksen ensisijainen tavoite tulee olla valmistusprosessin kehittäminen ja mittauslaitetta tulee käyttää tehokkaasti hyväksi uusien tuotteiden valmistuksen alkuvaiheessa.

Laiteohjauksessamuodostuvan informaation jalostamisen mahdollisuudet

Teollisuuden automaatiojärjestelmät digitalisoituvat, samalla niiden tuottaman reaaliaikaisen datan määrä kasvaa ja etenkin sen saatavuus helpottuu. Samanaikaisesti laitevalmistajan liiketoimintamallit ovat muuttumassa perinteisestä konevalmistuksesta kohti palveluntarjontaa. Muuttuneessa tilanteessa laitteiden ohjauksessa käytettäviltä järjestelmiltä vaaditaan uusia ominaisuuksia. Informaation käsittely ja jalostaminen muodostuvat tärkeiksi kilpailu-tekijöiksi. Kirjallisuusosassa on tarkasteltu, miten data jalostuu informaatioksi ja siitä edelleen tietämykseksi. Työssä myös selvitetään, miten niitä voidaan hyödyntää liiketoiminnassa. Samalla perehdytään teollisuudesta löytyviin informaatio- ja tietämysjärjestelmiin. Kokeellisessa osassa esitellään toimiva tiedonkeruu- ja raportointijärjestelmä ja tutkitaan, miten sitä tulisi kehittää, jotta se sopisi paremmin muuttuviin liiketoimintamalleihin. Lopputuloksena kehitettiin mallijärjestelmä, jolla pystytään täyttämään laitevalmistajan ja loppukäyttäjän muuttuneet informaatiotarpeet osana laiteohjausta.

MTS/MTO-ratkaisut metsäkoneiden valmistuksessa

Kandidaatintyössä käsitellään MTS/MTO ratkaisuja ja tuotantomuotojen valintaa metsäkoneita valmistavassa yrityksessä, jolla on tuotantoa Suomessa. Työn tavoitteena on määrittää toimintaympäristöön parhaiten soveltuva tuotantomuoto soveltamalla kirjallisuudessa esitettyjä päätöskriteereitä ja tuotantomuodon valintamallia. Tämän lisäksi tavoitteena on jakaa esimerkin avulla nimikkeet tilaus- ja varastokomponentteihin sekä asettaa CODP-raja harvesterin tuoterakenteeseen.

Hysteresis Losses in Sintered NdFeB Permanent Magnets in Rotating Electrical Machines

Permanent magnet materials are nowadays widely used in the electrical machine manufacturing industry. Eddy current loss models of permanent magnets used in electrical machines are frequently discussed in research papers. In magnetic steel materials we have, in addition to eddy current losses, hysteresis losses when AC or a rotating flux travels through the material. Should a similar phenomenon also be taken into account in calculating the losses of permanent magnets? Actually, every now and then authors seem to assume that some significant hysteresis losses are present in rotating machine PMs. This paper studies the mechanisms of possible hysteresis losses in PMs and their role in PMs when used in rotating electrical machines.

Wooden packaging waste reuse and refining

The aim of the thesis was both to study wooden packaging waste reuse and refining generated in the forestry machine factory environment, and to find alternative wooden packaging waste utilization options in order to create a new operating model which would decrease the overall amount of waste produced. As environmental and waste legislation has become more rigid and companies' own environmental management systems’ requirements and control have increased, companies have had to consider their environmental aspects more carefully. Companies have to take into account alternative ways of reducing waste through an increase in reuse and recycling. A part of this waste is from different forms of packaging. In the metal industry the most heavily used packaging material is wooden packaging, as such material is heavy and the packaging has to be able to bear heavy stress. In the theoretical part of the thesis, the requirements of packaging and packaging waste legislation, as well as environmental management systems governing companies’ processing of their packaging waste, are studied. The theoretical part includes a process study of systems, which direct packaging waste and wooden packaging waste refining. In addition, methods related to the continuous improvement of these processes are introduced. This thesis concentrates on designing and creating a new operating model in relation to wooden packaging waste processing. The main target was to find an efficient model in order to decrease the total amount of wooden packaging waste and to increase refining. The empirical part introduces methods for approaches to wooden packaging waste re-utilization, as well as a description of a new operating model and its impact.

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.

Using Virtual Reality in the teaching of manufacturing processes with material removal in CNC Machine-Tools

This paper presents a methodology for the incorporation of a Virtual Reality development applied to the teaching of manufacturing processes, namely the group of machining processes in numerical control of machine tools. The paper shows how it is possible to supplement the teaching practice through virtual machine-tools whose operation is similar to the 'real' machines while eliminating the risks of use for both users and the machines.

Aspects of selection, justification and implementation of CNC machine tools in manufacturing and toolroom environments

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Thermal error evaluation and modelling of a CNC cylindrical grinding machine

With the relentless quest for improved performance driving ever tighter tolerances for manufacturing, machine tools are sometimes unable to meet the desired requirements. One option to improve the tolerances of machine tools is to compensate for their errors. Among all possible sources of machine tool error, thermally induced errors are, in general for newer machines, the most important. The present work demonstrates the evaluation and modelling of the behaviour of the thermal errors of a CNC cylindrical grinding machine during its warm-up period.

Hygroexpansivity profiles on a commercial paper machine

Paper products show dimensional changes when subjected to moisture content modification. Hygroexpansivity was investigated in a commercial paper machine operating at 1256 m/min by a set of measurements on 75 g/m(2) reprographic bleached eucalyptus pulp paper samples. The present work shows hygroexpansivity development in different sections of the paper machine along the manufacturing direction. The measurement results demonstrate the effects of papermaking process operations on paper hygroexpansivity and lead to the confirmation of fiber orientation degree, drying restraint and shrinkage and paper tension as significant influencing factors. Structural, strength and elastic properties of paper were also measured as a function of machine direction position and presented for discussion purposes.

Minimizing total tardiness in a stochastic single machine scheduling problem using approximate dynamic programming

This paper addresses the non-preemptive single machine scheduling problem to minimize total tardiness. We are interested in the online version of this problem, where orders arrive at the system at random times. Jobs have to be scheduled without knowledge of what jobs will come afterwards. The processing times and the due dates become known when the order is placed. The order release date occurs only at the beginning of periodic intervals. A customized approximate dynamic programming method is introduced for this problem. The authors also present numerical experiments that assess the reliability of the new approach and show that it performs better than a myopic policy.

Application of Gage R&R methodology for improving the training and evaluation of automatic optical inspection machine operators

The current level of demand by customers in the electronics industry requires the production of parts with an extremely high level of reliability and quality to ensure complete confidence on the end customer. Automatic Optical Inspection (AOI) machines have an important role in the monitoring and detection of errors during the manufacturing process for printed circuit boards. These machines present images of products with probable assembly mistakes to an operator and him decide whether the product has a real defect or if in turn this was an automated false detection. Operator training is an important aspect for obtaining a lower rate of evaluation failure by the operator and consequently a lower rate of actual defects that slip through to the following processes. The Gage R&R methodology for attributes is part of a Six Sigma strategy to examine the repeatability and reproducibility of an evaluation system, thus giving important feedback on the suitability of each operator in classifying defects. This methodology was already applied in several industry sectors and services at different processes, with excellent results in the evaluation of subjective parameters. An application for training operators of AOI machines was developed, in order to be able to check their fitness and improve future evaluation performance. This application will provide a better understanding of the specific training needs for each operator, and also to accompany the evolution of the training program for new components which in turn present additional new difficulties for the operator evaluation. The use of this application will contribute to reduce the number of defects misclassified by the operators that are passed on to the following steps in the productive process. This defect reduction will also contribute to the continuous improvement of the operator evaluation performance, which is seen as a quality management goal.