Nano Technology and Robotics Syllabus

Engineering Mathematics

Calculus, differential equations, matrices, numerical methods, probability, statistics and optimisation support analysis and quality.

Engineering Physics and Chemistry

Physics explains mechanics, heat and measurement. Chemistry supports materials, corrosion, lubricants and surface treatment.

Engineering graphics

Students learn projections, sections, dimensioning, tolerances and drawing standards. Drawings communicate product intent to manufacturing and inspection.

Engineering mechanics

Statics and dynamics cover forces, motion, work and energy. These foundations support machines and processes.

Strength of materials

Stress, strain, bending, torsion and failure help engineers design parts, tools and fixtures safely.

Material science

Students study metals, polymers, ceramics and composites, including structure, properties and processing. Material selection connects performance with manufacturability and cost. Course application: nanorobotic systems.

Metallurgy and heat treatment

Phase diagrams, solidification, microstructure, annealing, hardening and tempering explain how processing changes metals.

Nanofabrication and microfabrication processes

Core study covers casting, forming, machining, joining, powder processing, polymers and additive methods. Students compare quality, cost and production volume.

Thin-film deposition

Casting pours or injects molten material into a mould. Students study patterns, moulding, cores, gating, risers, solidification and defects.

Lithography and patterning

Rolling, forging, extrusion, drawing and sheet-metal processes shape material through plastic deformation. Force, friction, temperature and tooling influence results.

Micro- and nano-machining

Turning, milling, drilling, grinding and related operations remove material. Students learn tool geometry, cutting forces, speed, feed, wear, fluids and surface quality. Course application: nanorobotic systems.

Laser, ion-beam and electron-beam processing

Electrical discharge, electrochemical, laser, abrasive-water-jet and ultrasonic processes serve materials or geometries difficult for conventional cutting. Each has limitations. Course application: nanorobotic systems.

Microjoining and nanoscale interfaces

Arc, gas, resistance, solid-state, brazing, soldering and adhesive methods are studied. Heat input, metallurgy, distortion, defects and inspection affect integrity. Course application: nanorobotic systems.

Polymer nanocomposites and soft robotics

Injection moulding, extrusion, blow moulding, compression moulding and thermoforming produce polymer parts. Temperature, pressure, cooling and mould design matter. Course application: nanorobotic systems.

Nanoparticle synthesis and processing

Powders are prepared, blended, compacted and sintered. The route supports near-net shapes and specialised materials but requires control of porosity and handling. Course application: nanorobotic systems.

Nanoscale characterisation and metrology

Metrology covers measurement standards, errors, uncertainty, gauges, surface finish and dimensional inspection. A measurement without calibration and method is unreliable.

Geometric dimensioning and tolerancing

GD&T communicates allowable variation in form, orientation and position. Functional tolerancing improves assembly and avoids unnecessary cost.

Micro-manipulation and nanorobotic platforms

Students study construction, drives, kinematics and control of lathes, milling, drilling, grinding and machining centres.

Precision motion control

CNC subjects cover coordinate systems, programming, interpolation, tooling, offsets, workholding and safe setup. Simulation reduces risk but does not replace verification.

CAD and product modelling

Computer-aided design creates geometry and drawings. Parametric models support change and manufacturing integration when dimensions and constraints are meaningful.

Nanofabrication process planning

CAM generates toolpaths, while process planning selects operations, machines, setups, tools and inspection. Post-processing must match the actual machine and controller. Course application: nanorobotic systems.

Microgripper and end-effector design

Jigs, fixtures, dies, moulds and cutting tools improve accuracy and production. Design considers location, clamping, loading, wear, safety and maintenance.

Micro-positioning and fixture validation

Fixtures locate and hold workpieces against defined datums. The locating scheme should restrict required movement without over-constraining a variable part. Clamps must resist process forces without distorting the component. Course application: nanorobotic systems.

Designers consider loading clearance, chips, coolant, tool access, inspection, ergonomics and mistake-proofing. Replaceable wear elements and standard components reduce maintenance cost. Course application: nanorobotic systems.

Validation checks repeatability, first-piece quality, safe loading and performance across allowed raw-part variation. One good component after manual adjustment is not evidence of a production-ready fixture.

Probe, tip and microtool management

Tool selection considers work material, operation, machine power, rigidity, speed, feed, cooling and quality. Tool life should be tracked using measured wear or proven limits. Course application: nanorobotic systems.

Replacing tools too early wastes cost, while running them beyond control creates defects. Identification, presetting, storage and offset management improve consistency. Reground tools need verified geometry. Course application: nanorobotic systems.

Industrial engineering

Work study, plant layout, line balancing, ergonomics and productivity help design efficient systems. Improvement should not overload workers or bypass safety.

Operations research

Linear programming, inventory, queuing, scheduling and simulation support decisions under constraints.

Experimental planning and closed-loop control

Forecasting, routing, scheduling, dispatching and inventory coordinate material and capacity. Plans must adapt to breakdown, quality and supplier variation.

Quality engineering

Students learn control charts, capability, sampling, root-cause analysis, design of experiments and quality systems. Inspection alone cannot create quality. Course application: nanorobotic systems.

Process capability

Capability compares process variation with specification limits after the process is stable. A high capability number cannot compensate for biased measurement or unstable data. Engineers first confirm measurement reliability and control. Course application: nanorobotic systems.

Control charts distinguish normal variation from signals needing investigation. Adjusting a stable process after every small fluctuation can increase variation, while ignoring a trend can allow future failure. Course application: nanorobotic systems.

Safety-critical, one-sided, non-normal or low-volume characteristics may need different analysis. Results require adequate sample size and engineering context. Course application: nanorobotic systems.

Measurement-system analysis

A measurement system includes instrument, fixture, method, operator, environment and software. Repeatability and reproducibility studies examine whether measurement variation is small enough for the decision. Course application: nanorobotic systems.

If an instrument cannot reliably distinguish good from bad parts, additional inspection will not solve the problem. Calibration and measurement-system analysis serve related but different purposes. Course application: nanorobotic systems.

Design of experiments and failure analysis

Design of experiments changes factors systematically to identify effects and interactions. Engineers define responses, ranges, replication and safety boundaries before testing. Course application: nanorobotic systems.

Failure analysis preserves evidence, defines the symptom and investigates material, design, process, assembly and use. Teams should avoid blaming an operator before examining unclear instructions, fixtures, worn tools and unrealistic rates. Course application: nanorobotic systems.

Maintenance engineering

Preventive, predictive and corrective maintenance support availability. Condition monitoring uses vibration, temperature, oil or electrical data where suitable. Course application: nanorobotic systems.

Reliability and equipment effectiveness

Reliability engineering examines failure frequency, repair time, critical spares and preventive tasks. Plans should reflect risk and evidence rather than servicing every component at the same interval. Course application: nanorobotic systems.

Overall equipment effectiveness combines availability, performance and quality to show major production losses. Manipulating planned time or ignoring minor stops makes it misleading.

After safe recovery from breakdown, teams record symptoms, alarms, operating condition and recent changes. Replacing the failed part restores operation but may not address lubrication, alignment, contamination or overload that caused it. Course application: nanorobotic systems.

Electrical and electronic foundations

Automation students study basic circuits, motors, drives, power supplies, relays, semiconductor devices and industrial wiring. They learn how electrical behaviour connects sensors, controllers and machines.

Manufacturing graduates are not automatically authorised electrical specialists. High-voltage design, installation and maintenance require qualified people and approved safe-work procedures.

Sensors, transducers and measurements

Sensors measure position, speed, force, pressure, temperature, flow, proximity and other process variables. Students study range, resolution, accuracy, response, calibration, noise and installation.

A controller acts on the signal it receives, so poor sensor placement or calibration creates poor decisions. Redundant or fail-safe sensing may be necessary where malfunction can harm people or equipment.

Control-system engineering

Control subjects introduce mathematical models, feedback, stability, transient response and controller design. Students learn why a fast response can become unstable and how delays or disturbances affect a loop.

Practical control includes interlocks, modes, alarms, permissives and manual operation. A process should move to a safe state when critical information or power is lost.

Programmable logic controllers

PLCs execute industrial control logic for machines and production lines. Students learn inputs and outputs, ladder logic, timers, counters, sequencing, diagnostics and communication.

Programs should use structured naming, comments, version control and tested recovery. Online changes to running machinery require formal authority and risk control.

Pneumatics and hydraulics

Pneumatic systems use compressed air for motion and handling, while hydraulic systems provide high force through pressurised fluid. Students study valves, cylinders, pumps, circuits and control.

Stored pressure remains hazardous after a machine stops. Isolation, release and verification are required before service. Leaks also waste energy and create reliability or housekeeping problems.

Human-machine interfaces and SCADA

Human-machine interfaces show machine status, commands, alarms and trends. Supervisory systems collect information across equipment. Good interfaces distinguish mode, stale data, fault and unsafe state clearly.

Alarm floods and confusing screens increase operator workload. Interface design should be validated with actual users and include role-based access.

Automation

Sensors, actuators, programmable controllers, drives and control logic automate production. Safe guarding, interlocks and manual recovery are essential.

Automation selection begins with a stable process and measurable objective. Automating an uncontrolled or poorly understood operation can reproduce defects faster. Engineers compare fixed, programmable and flexible automation based on product volume and variety.

Robotics

Industrial robots perform handling, welding, painting, assembly and inspection. Students study kinematics, programming, tooling, cells and safety.

Computer-integrated manufacturing

CIM connects design, planning, machines, handling and business information. Integration requires reliable data and controlled interfaces.

Additive manufacturing

Additive processes build parts layer by layer from polymers, metals, ceramics or composites. Students study design freedom, supports, orientation, parameters, post-processing and inspection. Course application: nanorobotic systems.

Additive manufacturing is valuable for prototypes, complex parts and low volumes but is not automatically cheaper or stronger than conventional processes.

Digital manufacturing

Connected machines, industrial IoT, analytics, digital twins and manufacturing execution systems support visibility and decisions. Cybersecurity and data quality are important.

Shop-floor data and traceability

Production records may include material lot, machine, operator, program, tool, parameter, inspection and rework. Traceability should match product risk and customer need; collecting every possible signal creates cost and privacy concerns.

Sensors need calibration, synchronised time and clear units. Missing or overridden values should be visible. A dashboard cannot replace verification of the physical process. Course application: nanorobotic systems.

Digital work instructions can control revisions and show checks. Offline and recovery procedures are required when networks fail. Access control prevents unauthorised parameter changes. Course application: nanorobotic systems.

Digital twins

A digital twin is a maintained digital representation connected to a physical asset or process. It may support simulation, monitoring, prediction or training. A static three-dimensional model is not automatically a digital twin. Course application: nanorobotic systems.

The model needs a defined purpose, validated assumptions, reliable data and version control. Complex twins can cost more to maintain than the value they provide. Course application: nanorobotic systems.

Manufacturing cybersecurity

Connected controllers and industrial computers create cyber risk. Segmentation, controlled remote access, backups, updates, account management and incident plans are important. Course application: nanorobotic systems.

Production availability and safety influence security decisions. Updates need testing and scheduled deployment. Students should test only authorised laboratory systems.

Simulation

Finite element, forming, casting, machining and factory simulation help compare options. Models depend on assumptions and must be validated.

Surface engineering

Coatings, plating, thermal treatments and finishing improve wear, corrosion, friction or appearance. Pretreatment and environmental control matter.

Composite manufacturing

Lay-up, moulding, resin infusion and automated processes produce composites. Fibre orientation, voids, cure and inspection determine performance.

Manufacturing systems design

Students compare flow lines, cells, flexible systems and job shops. Capacity, variety, demand and investment guide selection.

Lean manufacturing

Lean methods reduce waste and improve flow through observation, standard work, visual management and problem-solving. Lean should not mean removing necessary people or safety buffers blindly. Course application: nanorobotic systems.

Supply chain

Procurement, logistics, supplier quality and inventory affect production. Resilience requires alternative sources, traceability and risk assessment.

Supplier development

Supplier development begins with clear drawings, specifications, capacity and quality expectations. Engineers review process flow, measurement, tooling, special processes and change control.

Incoming inspection cannot compensate indefinitely for an incapable supplier. Joint root-cause work is more sustainable than sorting every shipment, though critical risks may still need source inspection. Course application: nanorobotic systems.

Second sources reduce interruption risk but require qualification. Material described by the same generic grade may differ in route, surface or consistency. Substitution needs controlled approval. Course application: nanorobotic systems.

Inventory and material flow

Inventory protects against variation but ties up cash, space and obsolescence risk. Engineers analyse lead time, demand, reliability and batch size rather than applying one rule everywhere. Course application: nanorobotic systems.

Material handling should prevent mixing, damage and unsafe lifting. Point-of-use storage can improve flow when replenishment is reliable. Course application: nanorobotic systems.

Cost engineering

Cost includes material, labour, machine time, tooling, energy, quality, overhead and lifecycle. Engineers compare alternatives without ignoring hidden failure cost.

Sustainable manufacturing

Students study material efficiency, energy, water, emissions, repair, remanufacture and recycling. Environmental improvement needs measured baselines.

Remanufacturing and repair

Remanufacturing restores a used product through inspection, cleaning, repair, replacement, processing and testing. It differs from simple reuse because performance is recovered to a defined level.

Variable return condition makes planning difficult. Engineers create grading, disassembly, cleaning, rework and final-test routes. Product design can improve access, identification and replaceability.

Repair and remanufacture can retain embedded value, but transport, cleaning, replacement and reliability must be considered. Safety-critical components require appropriate standards. Course application: nanorobotic systems.

Energy and resource accounting

Plants can meter electricity, fuel, compressed air, water and material by process or product. Normalising consumption against output distinguishes improvement from lower production.

Compressed-air leaks, idle machines, poor insulation and excessive scrap are common losses. Reducing energy must not undermine ventilation, cooling or safety. Course application: nanorobotic systems.

Industrial waste hierarchy

Prevention is preferable to reuse, recycling, recovery and disposal. Engineers reduce offcuts, defects, expired material and unnecessary packaging before seeking a waste outlet. Course application: nanorobotic systems.

Scrap segregation preserves value and prevents hazardous mixing. Recycling contracts need traceability and legal compliance.

Safety and ergonomics

Machine guarding, lockout, ventilation, lifting, fire and human factors are core. Safety must be designed into process and layout. Course application: nanorobotic systems.

Research methods

Literature review, experimental design, statistics and technical writing support reliable projects.

Typical semester pattern

Laboratories

Important laboratories include workshop, foundry, welding, machining, metrology, CNC, CAD/CAM, automation, robotics, materials and additive manufacturing.

Project ideas

• machining parameter optimisation;
• low-cost inspection fixture;
• casting-defect investigation;
• robotic handling cell simulation;
• predictive-maintenance demonstrator;
• additive part design and validation;
• energy audit of a process;
• ergonomic workstation redesign;
• digital production dashboard with authorised data;
• tool-wear monitoring;
• line-balancing study;
• remanufacturing plan for a component.

Projects should define requirements, measurements, risks, cost and limitations. Experimental equipment must be used under supervision.