University of Turku Digital Design Master’s Thesis Guide: KTEK0015, 30 ECTS, FEM, Optimisation and UTUGradu

What this guide covers

This guide is for the University of Turku Digital Design specialisation track in the current 2024-2027 Mechanical Engineering curriculum. It is based on the exact Peppi object KTEKMSCDIGDES2427, programme 99983, rather than on a generic mechanical-engineering thesis model. The most important structural point is that the 40 ECTS Master’s Thesis and Project category contains four separate objects: KTEK0015 Master’s Thesis in Technology, Mechanical Engineering, 30 ECTS; KTEK0026 Master’s Thesis in Technology Seminar, Mechanical Engineering, 5 ECTS; TTDK1308 Degree Qualifying Examination, 0 ECTS; and KTEK0016 Project Work, Mechanical Engineering, 5 ECTS. The guide also explains how digital-design methodology, FEM, optimisation, additive manufacturing, reverse engineering, metrology, CFD, experimental evidence and stakeholder-centred design should be bounded in a thesis.

Current programme object

The current Peppi programme object is KTEKMSCDIGDES2427, programme 99983. The degree is Master of Science (Technology), organised in the Faculty of Technology and connected to the Department of Mechanical and Materials Engineering. The formal degree size is 120 ECTS. Digital Design is one of three Mechanical Engineering specialisation tracks. When planning a thesis, use the curriculum period attached to your own study right and HOPS rather than copying a thesis structure from an older curriculum or from a Materials Engineering track. This distinction is especially important because the Mechanical Engineering seminar and project are both placed inside the 40 ECTS thesis-and-project category.

How the 120 ECTS degree is structured

The public programme structure combines 20 ECTS Joint studies in Mechanical Engineering, 20 ECTS Digital Design studies, a 40 ECTS Master’s Thesis and Project category, 20-25 ECTS Minor or Thematic Studies and 15-20 ECTS Other Studies. Peppi models the Advanced Studies component as 80 ECTS, consisting of 20 ECTS joint studies, 20 ECTS track studies and the 40 ECTS thesis-and-project category. The 115-125 ECTS range shown at the Peppi root reflects selectable module ranges and should not be reported as the formal degree size. The formal programme remains 120 ECTS.

Read the 40 ECTS Master’s Thesis and Project block correctly

The 40 ECTS category is not a 40 ECTS thesis. KTEK0015 is the individually examined 30 ECTS thesis. KTEK0026 is a separate 5 ECTS seminar. TTDK1308 is the 0 ECTS maturity examination. KTEK0016 is a separate 5 ECTS Project Work course. All four are grouped inside the same 40 ECTS category, but their assessment purposes are different. This is a key difference from the Materials Engineering tracks, where the thesis seminar is placed elsewhere. Do not inherit the Materials Engineering packaging into Digital Design, and do not merge project work or seminar credit into the 30 ECTS thesis itself.

Exact thesis course: KTEK0015

The exact thesis course is KTEK0015 Master’s Thesis in Technology, Mechanical Engineering, 30 ECTS. The thesis is expected to demonstrate scientific work, management of research methods, knowledge of the research field and scientific writing. It should analyse a practical or theoretical research problem using appropriate methods and scientific literature. In Digital Design, this may involve a design process, computational analysis, physical experiment, optimisation study, reverse-engineering workflow or a combination of methods. The thesis must still have a research question and an evidence chain. Producing a CAD model, simulation or prototype alone is not enough unless the work shows what question the artefact helps answer and how the result was evaluated.

The separate KTEK0026 thesis seminar

KTEK0026 Master’s Thesis in Technology Seminar, Mechanical Engineering is a separate 5 ECTS course inside the 40 ECTS Master’s Thesis and Project category. It supports the thesis process but does not change KTEK0015 from 30 ECTS to 35 ECTS. Plan seminar activity so that research planning, presentation and discussion support the actual thesis timeline. Seminar evidence and thesis evidence can relate to the same research problem, but they remain different academic objects. When describing the degree structure, report KTEK0026 separately instead of hiding it inside the thesis credit.

The separate KTEK0016 Project Work course

KTEK0016 Project Work, Mechanical Engineering is another separate 5 ECTS object inside the 40 ECTS category. Project work may provide a design brief, industrial context, prototype, simulation workflow, stakeholder contact or engineering dataset that later contributes to thesis development. However, successful project delivery does not replace individual thesis examination. If project work and thesis work use the same industrial problem, define what belongs to the project course and what is new, individually defensible research for KTEK0015. This avoids double counting and makes the contribution easier for supervisors and examiners to evaluate.

TTDK1308 maturity examination

TTDK1308 is the 0 ECTS Degree Qualifying Examination for the master’s degree. In the normal route, the thesis abstract or another suitable part of the thesis can function as the maturity test and demonstrate familiarity with the thesis field. A conditional written route can apply depending on previous degree and Finnish or Swedish educational-language background. Because the exact route depends on the student’s background, do not copy another student’s procedure. Recheck your own HOPS and current University instructions before final submission so an unresolved maturity requirement does not delay degree completion.

Finding a topic and supervisor

KTEK0015 allows thesis work to arise from a company, organisation or University research group. Digital Design topics can involve product or service design, finite-element analysis, structural optimisation, reverse engineering, metrology, additive manufacturing, thermal or flow analysis, digital-factory processes or a combined design-and-validation problem. A useful topic is narrow enough for a 30 ECTS thesis and specific enough to connect one research question to evidence. Instead of asking whether a redesigned component is “better”, define whether the comparison concerns mass, stiffness, stress, deformation, manufacturability, dimensional accuracy, usability, cost, uncertainty or another explicit criterion.

Build a thesis plan before modelling or prototyping

At the beginning, define the research problem, questions, requirements, evidence level, methods, datasets, geometry, software, test equipment, comparison baseline, validation strategy, risks, confidentiality constraints, milestones and expected outputs. Digital-design projects can become technically wide very quickly because CAD, simulation, optimisation, prototyping and measurement each create their own decisions. A written plan helps prevent the project from becoming a collection of tools without one coherent research question. It also helps identify long dependencies such as software access, company approval, test-rig availability, manufacturing lead time, scanning access or repeated simulation runs.

Use supervision as evidence control

Regular supervision should be used to review methodological decisions, not only to report progress. Discuss changes in geometry, load cases, boundary conditions, mesh, material models, objective functions, manufacturing assumptions, measurement systems, excluded data, failed simulations and design changes. Record major decisions so the final Methods section can explain why the approach changed. If a project moves from concept design to structural simulation, then to additive manufacturing and metrology, confirm at each step what the new evidence actually supports. This prevents a final conclusion from becoming broader than the method.

How examination and grading work

The current KTEK0015 course evidence states that at least two examiners evaluate the thesis using University evaluation and grading guidance. Final acceptance is decided by the head of the department, and the grade is based on examiner evaluation. Supervisor support therefore does not replace formal examination. Write the thesis so an examiner can trace the engineering question through literature, requirements, method, inputs, analysis, validation, uncertainty and conclusion. A visually impressive model or prototype cannot compensate for unclear assumptions, missing validation or a conclusion that claims more than the evidence demonstrates.

Choose a research question at the correct evidence level

Digital Design can move across several evidence levels: user or stakeholder need, design requirement, CAD geometry, numerical model, simulated structural or thermal response, manufactured prototype, measured geometry, experimental performance and industrial feasibility. These levels are connected but not interchangeable. A simulation can predict stress under stated assumptions without proving service life. A prototype can demonstrate that one build succeeded without proving repeatable production. A scan can establish geometric deviation without proving functional performance. State the evidence level in the research question and keep the conclusion at that level unless additional evidence justifies moving further.

Organise the literature around requirements and evidence

A useful literature review should separate design methodology, physical theory, numerical method, manufacturing process, measurement method and application context. When comparing published designs or simulation results, check whether geometry, material, loads, constraints, units and performance metrics are compatible. Avoid ranking designs from headline numbers produced under different boundary conditions. If standards, tolerances or accepted engineering definitions are relevant, identify the exact rule used. The review should explain what is known, what is uncertain, which methods are suitable for the current problem and where the thesis will contribute evidence.

Digital Design Methodology: document the design process

KTEK0029 Digital Design Methodology is a core track course. A thesis using design methodology should not present only the final concept. Document the problem framing, requirements, stakeholders, alternatives, selection criteria, iterations and evaluation logic. Explain why a method was chosen and what evidence was used to move from one design decision to the next. If stakeholder needs are translated into engineering requirements, preserve that traceability. A design that looks simpler or more attractive is not automatically superior; the thesis should show how the selected criteria support the conclusion.

Design thinking and stakeholder evidence

Design Thinking is an approved track option and can be relevant when the problem includes users, organisations or service interactions. If interviews, workshops, observations or prototype feedback are used, explain who participated, how data were collected, how responses were analysed and how they changed the design. Stakeholder preference is not the same as mechanical validation. A user may prefer one concept while structural or manufacturing evidence favours another. Keep these evidence types separate and make trade-offs explicit rather than combining them into an unexplained overall judgement.

Finite-element analysis: define the model completely

KTEK0028 Finite Element Method and Analysis is a core Digital Design course. FEM conclusions depend on geometry, material model, loads, boundary conditions, contacts, element type, mesh and solver settings. Report the assumptions that materially affect the result. Material properties should match the intended material state, temperature and loading regime. Boundary conditions should represent the physical problem rather than being chosen only because they make the model stable. A smooth stress contour is not proof that the model is correct; it is only an output from a model that still needs engineering justification.

Validate FEM and check numerical resolution

When numerical resolution can influence a conclusion, use a suitable mesh-convergence or discretisation check. If an analytical solution, benchmark, experiment or trusted reference case is available, use it to validate the model at the level of the intended claim. Calibration and independent validation should be distinguished. Agreement at one operating point does not automatically prove validity over every load, geometry or material regime. A numerically converged model can still be physically wrong if the governing assumptions are inappropriate. Report both numerical and physical limitations.

Optimisation requires an explicit mathematical problem

Digital Design students can use optimisation-oriented courses such as KTEK0069 Optimisation Driven Design and KTEK0030 Design Optimisation. An optimisation study should define design variables, objective functions and constraints. An “optimal” design is only optimal for that formulation. If the objective changes from mass reduction to stiffness, cost or thermal performance, the preferred design can change. When multiple objectives are combined, explain the weights or trade-offs. Compare the optimised result with a baseline under equivalent conditions, and do not hide engineering requirements inside a single unexplained score.

Optimisation is not the same as manufacturability

Topology or structural optimisation can create geometry that performs well numerically but is difficult to manufacture, inspect, join or maintain. If the design will be additively manufactured or machined, introduce relevant manufacturing constraints before the final design is frozen. A lower simulated mass does not automatically mean a better industrial design. The thesis should explain how manufacturability, tolerances, supports, post-processing, material behaviour and inspection requirements affect the final geometry. If the optimised model must be redesigned for production, preserve the distinction between the numerical optimum and the manufacturable engineering solution.

Digital Factory: separate information flow from product evidence

KTEK0011 Digital Factory is part of the joint Mechanical Engineering studies. A digital-factory thesis may study data flow, digital workflow, automation, traceability or integration between design and production. Demonstrating a connected workflow does not automatically prove higher product quality, production robustness or lower cost. Define what outcome is actually measured. If the claim concerns traceability, show how data are linked. If it concerns production efficiency, define the process and metric. If it concerns quality, include suitable quality evidence instead of assuming that digitalisation itself guarantees improvement.

Additive manufacturing needs complete process context

KTEK0012 3D Printing & Additive Manufacturing is another joint study. When additive manufacturing is part of a thesis, report the process, material or feedstock, build orientation, key settings, support strategy and relevant post-processing. Dimensional accuracy, surface finish, porosity, microstructure and mechanical performance are different evidence dimensions. A successfully printed part shows that one build was achieved under specified conditions, but it does not automatically establish repeatable process capability or production readiness. If the thesis compares additive and conventional manufacturing, state the technical and economic assumptions behind the comparison.

Separate prototype success from component qualification

A prototype can be very useful for checking geometry, assembly, usability or early performance, but prototype success should not be described as final product qualification. Qualification may require repeated manufacturing, dimensional inspection, load testing, fatigue evidence, environmental testing, material traceability or compliance with relevant standards. State exactly what the prototype demonstrates. If only one part was manufactured, acknowledge the limitation. If a prototype is evaluated only by fit or appearance, do not extend the conclusion to structural safety, lifetime or production capability without further evidence.

Axiomatic Design: keep requirements traceable

KTEK0013 Axiomatic Design is included in the joint Mechanical Engineering studies. If axiomatic-design ideas are used, make functional requirements, design parameters and decomposition logic explicit. The value of the method is reduced if the thesis jumps directly to a final concept without showing how requirements were translated into design choices. Requirement decomposition should preserve traceability from the original need to the final design. A design that uses fewer parts or looks simpler is not automatically better unless the chosen requirements and evaluation criteria justify that conclusion.

Reverse engineering: distinguish geometry from intent

KTEK0031 Reverse Engineering and Industrial Metrology is an approved Digital Design option. Reverse engineering can reconstruct geometry from an existing part and support redesign, inspection or documentation. However, a reconstructed CAD model does not automatically prove the original designer’s intent, original tolerances or original manufacturing process. Explain what was directly measured, what was inferred and what was redesigned. Preserve coordinate systems, datums, scan alignment and model-processing decisions where these affect the result. If the reverse-engineered model is later used for simulation, show how measurement uncertainty and geometry simplification were handled.

Industrial metrology requires uncertainty

The metrology course covers tactile and optical 3D measurement, method selection, uncertainty, statistical methods, surface finish and geometrical tolerancing. Select a measurement system that fits feature size, accessibility, surface properties and required accuracy. Nominal scanner resolution is not the same as measurement uncertainty. If a measured deviation is close to a tolerance limit, uncertainty can affect the engineering decision and should be reported. Repeatability and reproducibility are different concepts. When comparing parts, use consistent measurement definitions, alignment rules, datums and surface parameters.

Use tolerances and measurement systems consistently

A numerical deviation is meaningful only in relation to a design requirement, tolerance or functional criterion. If Geometrical Product Specifications or another standard is used, identify the exact definition that controls the analysis. Avoid switching between different datum schemes or alignment methods without explaining the effect. Surface-finish comparisons should use the same parameter definitions and measurement conditions. If tactile and optical systems give different results, investigate method-specific limitations rather than selecting the more favourable value. The goal is a defensible measurement argument, not merely a dense table of dimensions.

CFD and experimental thermodynamics are optional supporting methods

KTEK0061 Computational Fluid Dynamics and KTEK0056 Experimental Thermodynamics appear as recommended studies rather than mandatory Digital Design core courses. They can support theses involving flow, heat transfer or thermomechanical behaviour. CFD conclusions depend on governing equations, flow assumptions, boundary conditions, mesh and numerical settings. Experimental thermal evidence depends on instrumentation, calibration, test conditions and uncertainty. If simulation and experiment are combined, explain how the measurements constrain or validate the model. Agreement at one operating point should not be presented as proof that the model is valid everywhere.

Integrate simulation and experiment without mixing them

A strong thesis can combine design, simulation, manufacturing and experiment, but each method should retain its own evidence status. A simulation predicts behaviour under model assumptions. A manufactured prototype demonstrates a physical artefact. A metrology result measures geometry. A load or thermal test measures performance under a test condition. The thesis should explain how these layers connect. If experimental data are used to calibrate the model, do not reuse the same evidence as independent validation without acknowledging the dependency. Keep measured, calculated, simulated and inferred results visibly distinct.

Company work and NDA boundaries

Mechanical Engineering theses can be connected to companies, and KTEK0025 Industrial Internship can also provide industrial experience. Before using confidential CAD, process parameters, cost data or performance results, agree what can appear in the public thesis, what must remain confidential and how examiners can access the evidence they need. A company-requested design problem still requires an academic research question and defensible method for KTEK0015. Confidentiality should not make the scientific basis impossible to examine. Resolve the boundary before writing the final manuscript rather than after sensitive information has already been embedded.

Keep data, CAD and analysis reproducible

Preserve the files needed to reconstruct the thesis reasoning: source geometry, CAD revisions, material-property sources, model versions, scripts, solver inputs, raw measurement data, calibration records, scan-processing steps, optimisation settings and final figure provenance. Record important deviations from the planned method. A final screenshot or exported plot is not enough if the path from input to conclusion cannot be reconstructed. Reproducibility does not require publication of confidential company files, but the research group and examiners should be able to understand how every important result was produced.

AI use must remain accountable

The University provides guidance on responsible AI use, but responsibility for the thesis remains with the student. If AI assists with code, CAD scripting, literature triage, translation, data cleaning or drafting, verify the output and follow current University, faculty and supervisor instructions. Do not allow a generative tool to invent references, loads, material properties, measurement results or simulation parameters. Distinguish AI used as part of the research method from AI used as a productivity aid. If an AI model influences engineering decisions, document the method and validation needed for that scientific role.

Make figures and tables auditable

Every technical figure should make clear whether it shows measurement, simulation, optimisation, CAD geometry or derived data. Include units, conditions and relevant sample or run information. A stress map should state the load case and model context. A metrology plot should identify the coordinate system and measurement method. An optimisation chart should explain the objective and constraints. If uncertainty bars or ranges are shown, define what they represent. Readers should be able to trace a major figure back to a documented dataset, model or experiment rather than treating the figure as self-explanatory evidence.

A defensible thesis chapter structure

The current public evidence does not impose one universal chapter template for every Digital Design thesis. Use a structure that makes the research logic easy to audit. A practical pattern is Introduction, Background or Literature Review, Research Questions and Requirements, Methods, Results, Discussion, Conclusions, References and appendices where needed. Design-heavy projects may add a Design Process or Requirements chapter. Computational work may divide model development and validation. Keep Results focused on what was observed or calculated and Discussion focused on interpretation, uncertainty, limitations and implications.

Turnitin checks originality, not engineering validity

University of Turku degree theses use Turnitin as part of originality checking. Similarity review is important for source use and academic integrity, but a low similarity percentage does not validate a finite-element model, optimisation formulation, measurement uncertainty, prototype test or design conclusion. Engineering validity comes from appropriate methods, transparent assumptions, controls, validation and examiner review. Treat Turnitin as an integrity control rather than proof that the technical result is correct. Likewise, automated AI-related indicators should not replace evidence-based academic judgement under current University rules.

UTUGradu submission, examination and publication

UTUGradu manages the electronic higher-degree thesis process, including originality checking, examination, approval, publication and archiving. Operational details can change separately from the stable KTEK0015 30 ECTS rule, so recheck current instructions when submitting. Confirm that the intended final manuscript is uploaded, metadata are correct, confidentiality issues are resolved and the maturity route applicable to your background has been addressed. Do not rely on screenshots or checklists from an older cohort when the current University process is available.

A practical thesis timeline

A workable sequence is: confirm the current HOPS and 40 ECTS package; identify topic and supervisor; define the research question, requirements and evidence level; resolve company access and confidentiality; create the thesis plan; establish baseline geometry and methods; run early simulation or measurement pilots; refine assumptions; perform the main analysis; validate where possible; manufacture or test prototypes if relevant; document uncertainty; draft Methods and Results early; complete seminar and project obligations; revise the manuscript; then complete Turnitin, UTUGradu, maturity and examination steps. Build contingency time for failed simulations, mesh issues, manufacturing delays, scanner access, company review and redesign.

Final pre-submission checklist

Before submission, confirm that the manuscript names KTEK0015 as the 30 ECTS thesis and does not call the whole 40 ECTS category a 40 ECTS thesis; keeps KTEK0026 seminar and KTEK0016 project work separate; addresses TTDK1308 correctly; defines requirements and evidence levels; documents FEM assumptions, mesh and validation where relevant; separates optimisation from manufacturability; distinguishes prototype success from qualification; reports metrology uncertainty; keeps measured and simulated evidence separate; documents CAD/data provenance; resolves NDA, privacy and permit issues; verifies AI-assisted material; checks references and figures; and follows current Turnitin and UTUGradu instructions.