9 research outputs found
Friction Stir Welding using Mazak VCN-430a Milling Centre and Taguchi’s robust design method finding optimum parameters.
Master's thesis in Stuctural EngineeringFriction Stir welding has become an important method for welding of aluminium alloys. Excellent mechanical properties, economic benefits and environment friendly is just some of the advantages compared to ordinary fusion welding. However, large investments are needed as the workpiece need to be clamped properly against the worktable to resist any movement and the machine have to produce a large downforce. FSW can be performed using ordinary CNC- machines but force control or position control are suggested to make sound welds. This experiment used a Mazak VCN430a vertical milling centre and tools from Stirweld. The welding was conducted without any force control or force measuring system. To get a better control of the shoulder depth, the plates surface was measured using a measuring probe and the surface curvature was interpolated along the weld path. Friction stir welding was successfully performed on 300x150x3 mm plates of AA6082-T6 alloy using the Tagushi robust design approach. Optimal parameters for this particular machine and experiment was found to be 1200 rpm for the rotation speed, 150 mm/min for the welding speed, 2 seconds dwell time and a shoulder depth of 0.11 mm. The matrix experiment revealed almost the same value for the 0.07 mm shoulder depth which can be a prove of interaction among the parameters. Ultimate tensile strength test for the optimal weld parameters was 222.7 MPa. The predicted value was higher, but the measured value was inside the 2-standard confidence interval. Vickers hardness test showed that weakening of material had occurred throughout the specimen. The measured value 60 mm from the weld centre was approximately 75 Vickers HV. Developing the welding jig, clamping system and also simultaneously finding welding parameters without having any force control or position control proved itself to be very difficult. The clamping system seemed to be the most important factor to be able producing sound welds. For further friction welding experiment a welding jig and clamping system with the possibility of force control or position control need to be considered to eliminate defects
DIG-MAN: Integration of digital tools into product development and manufacturing education
General objectives of PRODEM education. Teaching of product development requires various digital tools. Nowadays, the digital
tools usually use computers, which have become a standard element of manufacturing
and teaching environments. In this context, an integration of computer-based technologies
in manufacturing environments plays the crucial and main role, allowing to enrich,
accelerate and integrate different production phases such as product development, design,
manufacturing and inspection. Moreover, the digital tools play important role in management
of production. According to Wdowik and Ratnayake (2019 paper: Open Access
Digital Tool’s Application Potential in Technological Process Planning: SMMEs Perspective,
https://doi.org/10.1007/978-3-030-29996-5_36), the digital tools can be divided
into several main groups such as: machine tools and technological equipment (MTE), devices
(D), internet(intranet)-based tools (I), software (S). The groups are presented in
Fig. 1.1. Machine tools and technological equipment group contains all existing machines and
devices which are commonly used in manufacturing and inspection phase. The group is used in
physical shaping of manufactured products, measurement tasks regarding tools and products,
etc. The next group of devices (D) is proposed to separate the newest trends of using mobile
and computer-based technologies such as smartphones or tablets and indicate the necessity
of increased mobility within production sites. The similar need of separation is in the case of
internet(intranet)-based tools which indicate the growing interest in network-based solutions.
Hence, D and I groups are proposed in order to underline the significance of mobility and
networking. These two groups of the digital tools should also be supported in the nearest
future by the use of 5G networks. The last group of software (S) concerns computer software
produced for the aims of manufacturing environments. There is also a possibility to assign the
defined solutions (e.g. computer programs) to more than one group (e.g. program can be assigned
to software and internet-based tools). The main role of tools allocated inside separate
groups is to support employees, managers and customers of manufacturing firms focused on
abovementioned production phases. The digital tools are being developed in order to increase
efficiency of production, quality of manufactured products and accelerate innovation process
as well as comfort of work. Nowadays, digital also means mobile.
Universities (especially technical), which are focused on higher education and research, have
been continuously developing their teaching programmes since the beginning of industry 3.0
era. They need to prepare their alumni for changing environments of manufacturing enterprises
and new challenges such as Industry 4.0 era, digitalization, networking, remote work,
etc. Most of the teaching environments nowadays, especially those in manufacturing engineering
area, are equipped with many digital tools and meet various challenges regarding an
adaptation, a maintenance and a final usage of the digital tools. The application of these tools
in teaching needs a space, staff and supporting infrastructures. Universities adapt their equipment
and infrastructures to local or national needs of enterprises and the teaching content
is usually focused on currently used technologies. Furthermore, research activities support
teaching process by newly developed innovations.
Figure 1.2 presents how different digital tools are used in teaching environments. Teaching
environments are divided into four groups: lecture rooms, computer laboratories, manufacturing
laboratories and industrial environments. The three groups are characteristic in the
case of universities’ infrastructure whilst the fourth one is used for the aims of internships of students or researchers. Nowadays lecture rooms are mainly used for lectures and presentations
which require the direct communication and interaction between teachers and students.
However, such teaching method could also be replaced by the use of remote teaching (e.g.
by the use of e-learning platforms or internet communicators). Unfortunately, remote teaching
leads to limited interaction between people. Nonverbal communication is hence limited.
Computer laboratories (CLs) usually gather students who solve different problems by the use
of software. Most of the CLs enable teachers to display instructions by using projectors. Physical
gathering in one room enables verbal and nonverbal communication between teachers
and students. Manufacturing laboratories are usually used as the demonstrators of real industrial
environments. They are also perfect places for performing of experiments and building
the proficiency in using of infrastructure. The role of manufacturing labs can be divided as:
• places which demonstrate the real industrial environments,
• research sites where new ideas can be developed, improved and tested.
Industrial environment has a crucial role in teaching. It enables an enriched student experience
by providing real industrial challenges and problems
The AutoICE Challenge
Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of the timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The AutoICE Challenge investigates the possibility of creating deep learning models capable of mapping multiple sea ice parameters automatically from spaceborne synthetic aperture radar (SAR) imagery and assesses the current state of the automatic-sea-ice-mapping scientific field. This was achieved by providing the tools and encouraging participants to adopt the paradigm of retrieving multiple sea ice parameters rather than the current focus on single sea ice parameters, such as concentration. The paper documents the efforts and analyses, compares, and discusses the performance of the top-five participants’ submissions. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development, and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4 %, and it was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple sea ice parameters with convolutional neural networks and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results
Estimates of stock size of northeast Arctic cod and haddock, "Sebastes mentella" and "Sebastes marinus" from survey data, winter 1988
An acoustic survey and a bottom trawl survey for cod and haddock
were carried out in the Barents Sea during the winter 1988.
The fish still had the westerly distribution established in the
previous year, but the echo abundance was substantially lower for
both cod and haddock. The results of the surveys confirm the
declining trend in recruitment.
Abundance indices for redfish indicate that the stock situation
is stabilizing for Sebastes mentella, but give cause for great
concern for the Sebastes marinus stock.
An acoustic survey on the spawning grounds of cod in the
Lofoten-Vesterålen area was carried out after the Barents Sea
survey. The estimate of mature cod was only 37% of the 1987
estimate
Estimates of stock size of Northeast Arctic cod and haddock from survey data 1986/1987
Combined acoustic and bottom trawl survey for cod and haddock were carried out in the Svalbard area in autumn 1986 and in the Barents Sea during the winter 1987. An acoustic survey for spwaning cod was carried out in March 1987 in the Lofoten area. The results show that the stocks are still increasing, but not as fast as previous surveys have indicated. One possible reason for this is that fish abundance in relatively shallow waters may have been generally overestimated by the current acoustic survey method
Marine indikatorer
The Nature Index (NI) is established to get an overview of the state and development of biodiversity within the major ecosystems of Norway. It includes marine, limnic and terrestrial ecosystems. The aim of the index is to measure if Norway manage to halt the loss of biodiversity by the end of 2010.
A number of indicators are chosen to represent the state of biodiversity. 125 scientists from various disciplines of research have contributed with data, expert judgements or modeled data for 310 indicators representing different aspects of biological diversity, such as trophic levels, key species and threatened and common species. In
order to assemble all the data to an index, a reference value has been estimated for each indicator. The reference value reflects an ecological sustainable value for the indicator, and the indicator values displays eventual
divergence from this state. This report describes how data on reference values, uncertainty and values for 1950, 1990,
2000 and 2010 has been set for each indicator. Furthermore this report documents the data sources for each of the indicators that are included in the data set