DEV Community: Dr. Ing. Salman ST MSc

How AI is (not) Considered a Real Threat to the Academic World

Dr. Ing. Salman ST MSc — Sat, 22 Aug 2026 13:04:52 +0000

How AI is (not) Considered a Real Threat to the Academic World

AI shifting 3D drawing software applications?
Artificial Intelligence (AI) is currently starting to shift and change the paradigm of 3D drawing software applications (such as Blender, SolidWorks, AutoCAD, or AutoCAD 3D) through several key mechanisms:

Generative Text-to-3D (From Text Directly to 3D Models)
Previously, to create 3D objects such as rotor rig components or shafts, designers had to create 2D sketches, extrude, revolve, and fillet them manually.
Now, generative AI allows the creation of initial 3D models simply by entering text commands (prompts), such as "3D model of a rotating shaft with pillow block bearings." The AI instantly generates a basic 3D mesh in seconds.

Image-to-3D (Converting 2D Images to 3D)
AI can analyse 2D images or photos (even 2D engineering drawings) and automatically reconstruct them into 3D models with depth.
This significantly saves time on initial drafting because users don't need to build geometry from scratch.

AI-Driven Parametric & Generative Design (Shape Optimization)
In the mechanical engineering industry, AI doesn't simply "draw," but determines the best shape based on mechanical loads.
Designers simply input parameters (e.g., vibration loads, material limits, and bearing locations). The AI then simulates hundreds of variations of the most optimal, strong, and lightweight 3D structural shape without the need for manual drawing each one.

CAD Command Automation (Co-pilot in 3D Software)
Modern CAD software is starting to integrate AI assistants. Instead of navigating complex button menus, users can simply type or speak a command: "Make an M8 bolt hole at the center of this surface", and the AI will execute the modelling steps.

Will Manual 3D Software Disappear Completely?
Not completely. AI currently serves as a co-pilot, not a full replacement. Rapid prototyping, generative shape ideation, and automation of repetitive tasks. Manual control is required for: High-precision tolerances (e.g., shaft size tolerances of 0.01 mm), highly specific finite element simulations (FEA), and manufacturing engineering drawing standards (ISO/ANSI). Designers are still needed for finishing and technical verification.

Architects under threat?
The architectural profession is not threatened with extinction, but rather is undergoing a significant role shift. AI is not replacing architects as decision-makers, but rather replacing repetitive and time-consuming technical tasks.

The AI is transforming the architectural profession and which parts cannot be replaced.
Lightning Design Exploration (Text-to-Image / 3D): The conceptual stage, which once took weeks, can now be completed in a matter of hours. AI can generate dozens of visualization options for facades, floor plans, and room ambiance based on descriptive commands.
Layout & Building Optimization (Generative Design): AI can automatically analyze floor plan layouts that maximize natural lighting, air circulation, energy efficiency, and compliance with the basic building coefficient (KDB).
Technical Document Automation: The previously manual creation of working drawings, construction details, and material calculations (Bill of Quantities) is now increasingly automated by AI integrated into BIM (Building Information Modeling) systems.

Why Can't Architects Be Replaced by AI?
Legal and Safety Liability: AI is not professionally licensed and cannot be held legally responsible if a building structure fails or collapses.
Client Empathy and Context: Understanding the client's emotional desires, local cultural dynamics, and the occupant's sense of humor or lifestyle requires human empathy that algorithms lack.
Field Supervision & Construction Reality: Real-world site conditions, negotiations with contractors, and solving emergent problems in the field still require the presence and intuition of a physical architect.
The profession that is actually threatened is not "architects," but rather architects who refuse to use AI. Architects who utilize AI will work much faster, more effectively, and focus on aesthetic value and high-level concepts.

The fields are substituted by AI
The impact of AI doesn't eliminate any field entirely, but rather reduces the workforce requirement for jobs that are repetitive, rule-based, or focused on basic data processing.

Most Affected Professions (Experiencing a Decrease in Demand)
Drafter & Junior CAD Operator: Basic 2D/3D working drawings can now be rapidly automated by AI. The industry needs senior architects/engineers who verify results more than draftsmen who start from scratch.
Plain Text Translator & Simple Editor: Translation of general documents, articles, or standard business documents is now dominated by Neural Machine Translation and LLM. Human translators are now needed more only for literary works, complex legal documents, or cultural localization.
Data Entry & Basic Customer Service: Manual data entry tasks and answering general chat support are now fully shifted to interactive AI agents.
Generic Copywriter & Content Writer: Writing basic SEO articles, e-commerce product descriptions, or drafting standard marketing copy has seen a drastic decline.
Basic Programmer / Junior Coder: Basic coding (boilerplate code), simple bug fixes, and automation scripts are now handled by AI. Human developers are required to focus on complex system architecture and business logic.

College Majors Experiencing Curriculum & Focus Shifts

Majors will not close, but their basic curricula have shifted and are required to adapt to ensure their graduates remain relevant:

Literature & Foreign Languages:
Shift: The focus of studies is no longer simply memorizing grammar or literal translation techniques, but rather on Computational Linguistics, Cultural Discourse Analysis, and Prompt Engineering.

Information Technology / Computer Science:
Shift: The emphasis on simply being able to code is starting to diminish. The focus is shifting to Software Architecture, Cybersecurity, Data Management, and AI Engineering Systems.

Fine Arts & Visual Communication Design (DKV):
Shift: Technical skills in operating design software (such as Adobe Photoshop/Illustrator) are no longer the primary selling point. The focus is shifting to Art Direction, Creative Concepts, Intellectual Property Rights (IPR), and User Experience (UX).

Accounting & Business Administration:
Shift: Manual bookkeeping and transaction recording have been automated. Graduates are expected to become Strategic Financial Analysts, Forensic Auditors, and Business Consultants.

Human Skills That Cannot Be Replaced by AI

In this era, any field you pursue will remain secure if you possess this combination of skills:
Critical Thinking & Problem Solving: The ability to validate whether AI output is correct, safe, and ethical (such as checking the mathematical equations of machine vibrations).
Interpersonal Communication & Empathy: Ability to negotiate, lead a team, and understand the real needs of clients.
Legal/Professional Responsibility: Official license (Registered Engineer, Integrated Architect, Professional Certification) that can hold legal responsibility for an engineering work.

But are there any professions or majors at universities that will be displaced by AI?
Yes, there are. The impact of AI doesn't eliminate any field entirely, but rather reduces the workforce requirement for jobs that are repetitive, rule-based, or focused on basic data processing.

Most Affected Professions (Experiencing a Decrease in Demand)
Drafter & Junior CAD Operator: The creation of basic 2D/3D working drawings can now be rapidly automated by AI. The industry needs senior architects/engineers who verify results more than draftsmen who start from scratch.
Plain Text Translator & Simple Editor: The translation of general documents, articles, or standard business documents is now dominated by Neural Machine Translation and LLM. Human translators are now needed more only for literary works, complex legal documents, or cultural localization.

Data Entry & Basic Customer Service: Manual data entry tasks and responding to general support chats have now shifted entirely to interactive AI agents.
Generic Copywriter & Content Writer: Writing basic SEO articles, e-commerce product descriptions, or drafting standard marketing copy has seen a drastic decline in demand.

Entry-Level Programmer / Junior Coder: Basic coding (boilerplate code), simple bug fixes, and automation scripts are now handled by AI. Human developers are required to focus on complex system architecture and business logic.
College Majors Experiencing Curriculum Shifts & Focus
The following majors will not close, but their basic curriculum has shifted and is required to adapt to ensure graduates remain relevant:

Literature & Foreign Languages:
Shift: The focus of courses is no longer simply memorizing grammar or literal translation techniques, but rather on Computational Linguistics, Cultural Discourse Analysis, and Prompt Engineering.

Information Technology / Computer Science:
Shift: The emphasis on simply "coding" is decreasing. The focus is shifting to Software Architecture, Cybersecurity, Data Management, and AI Engineering Systems.

Fine Arts & Visual Communication Design (DKV):
Shift: Technical skills in operating design software (such as Adobe Photoshop/Illustrator) are no longer the primary selling point. The focus has shifted to Art Direction, Creative Concepts, Intellectual Property Rights (IPR), and User Experience (UX).

Accounting & Business Administration:
Shift: Manual bookkeeping and transaction recording have been automated. Graduates are in demand as Strategic Financial Analysts, Forensic Auditors, and Business Consultants.
Human Skills That Cannot Be Replaced by AI
In this era, any field you pursue will remain secure if you possess this combination of skills:
Critical Thinking & Problem Solving: The ability to validate whether AI output is correct, safe, and ethical (such as checking mathematical equations for machine vibrations).
Interpersonal Communication & Empathy: The ability to negotiate, lead teams, and understand the client's real needs.
Legal/Professional Liability: Official licenses (Registered Engineer, Integrated Architect, Professional Certification) that can hold you legally accountable for your engineering work.

But is AI truly capable of completely replacing software applications like Onshape, Inventor, and SolidWorks?

No, AI will not completely replace or replace CAD software (Onshape, Inventor, and SolidWorks).
Instead, AI is integrated into these software as an additional feature.
The main reason is the fundamental difference between AI drawings and 3D CAD models for engineering.

AI Drawing vs. Precision Geometry (CAD)

Generative AI (Image/Mesh): Generates shapes based on visual approximations (pixels or a mesh of surface polygons). AI doesn't care whether the bolt holes are ∅20 mm precise or ∅20.04 mm.
CAD Software (SolidWorks/Inventor): Works based on absolute parametric geometry. Dimensional tolerances, axes of symmetry, and center distances require 100% mathematical certainty for machine components (such as bearings and shafts) to be assembled and manufactured on a CNC machine/lathe.

CAD Functions Aren't Just "Drawing" CAD software is the foundation of the entire manufacturing and engineering analysis ecosystem: Dynamics Testing & Simulation (FEA/CFD): Calculates residual stresses, vibration resonances, and structural loads. Manufacturing (CAM): Converts 3D shapes into machine instruction code (G-code) for real-life metal cutting. BOM & Bill of Materials: Generates material lists, bolt specifications, and production costs in a structured manner.
Big CAD Brands Are Adopting AI Dassault Systèmes (SolidWorks) and Autodesk (Inventor) are not threatened, but are instead incorporating AI into their products: Generative Design: You input load requirements (e.g., "withstand shaft vibration at 1200 RPM"), then the AI in Inventor/SolidWorks designs the strongest structural shape and places it directly in the CAD workspace. Text-to-CAD Copilot: In the future, you can simply type in SolidWorks: "Make a pillow block bearing seat"

What areas cannot be solved by AI?
Despite the rapid development of AI, there are fundamental human domains that AI cannot solve or replace independently:

Legal and Moral Accountability AI is simply software without legal status. AI cannot be sued, detained, or have its professional license revoked if a system failure occurs. Example: Medical decisions involving life-threatening risks, the signing of structural feasibility documents for buildings (architectural/civil engineering), or the approval of machine designs operating at high speeds still require the stamp of human legal responsibility (Certificate of Expertise/SIP).
Real-Time Empathy and Deep Emotional Understanding AI can mimic empathetic words through language processing, but it does not experience or feel emotions.

Example: In-depth psychotherapy, trauma crisis management, high-level diplomatic negotiations, and relationship counseling require intuitive awareness and mutual trust between humans that cannot be fostered by mathematical algorithms.

Field Intuition and Handling Unexpected Situations (Unstructured Physical Environment) AI and robotics operate based on historical data or predictable environments. However, the dynamics of the real physical world often present unpredictable variables (chaos). Example: A mechanic/vibration technician in the field identifying a strange noise in a turbine based on a burning smell, a sudden temperature change, or a vibration felt through his foot — then making an instant, improvised decision without any data in the database.
Discovery of New Theories and Scientific Leaps (True Scientific Breakthrough) AI excels at analyzing massive amounts of data (pattern recognition), but it lacks the curiosity or imagination to question existing laws of physics. Example: Formulating the theory of relativity like Einstein or creating a new physics paradigm that has no historical data points on the internet. AI simply extrapolates existing data.
Art Based on Lived Experience and Indigenous Culture Generative AI can imitate a painting style or compose a song, but the value of high-class artwork often lies in the creator's backstory (life struggles, suffering, and the socio-political context of the era). AI lacks lived experience.

Main Pattern:
AI can solve problems based on calculation, pattern analysis, and the automation of technical tasks. However, problems requiring moral responsibility, real empathy, free will, physical intuition, and creation from nothing remain the absolute domain of humans.

Do institutions such as universities or the educational world restrict AI?

Yes, educational institutions and universities restrict AI, but the forms of restrictions have changed drastically.
While at the beginning of ChatGPT's emergence (around 2022–2023), many universities attempted to completely ban the use of AI, now (2025–2026) the majority of universities worldwide and in Indonesia have shifted from total prohibitions to regulations and ethical restrictions.
Forms of AI Restrictions in Academic Environments
Restrictions in Final Projects & Scientific Articles (Theses/Dissertations/Journals)

Co-Authorship Prohibited: International publishers (such as Elsevier, IEEE, ASME) and universities prohibit the inclusion of AI as an author because AI has no legal responsibility for the accuracy of the content.
AI Use Must Be Declared: Students/researchers are required to include a detailed list of AI uses (e.g., "AI is used only to check English grammar in Section 2").
Prohibited for Primary Data Analysis: Numerical data processing, research conclusion generation, and primary analysis should not be left entirely to AI without manual validation.

Restrictions During Exams & Critical Evaluations
Colleges are returning some evaluation methods to oral examinations (trials/practicals), written exams in class without internet access (closed-book), or live case analysis in laboratories to ensure student understanding.

Automatic Similarity Detection (AI Detector & Plagiarism Tools)
Colleges are using software integrations such as Turnitin AI Detection to detect how much of the text is AI-generated. Manuscripts with too high a percentage of AI indications may be rejected or returned.

Why Don't Universities Ban AI Completely Anymore?
AI Has Become an Industry Standard: Industries (including engineering, architecture, and IT) need graduates who are proficient in using AI as a co-pilot. Banning AI completely will leave students unprepared for the workforce.
AI Is Absolutely Difficult to Detect: AI detection tools often produce false positives (false accusations against otherwise neat human writing), so universities are placing greater emphasis on academic integrity than simply relying on detectors.
Current Campus Policy: "AI-Assisted, Human-Driven"
In general, official regulations on current campuses categorize the use of AI into three categories:

🟢 Allowed: Grammar checking, brainstorming, or formatting mathematical codes/equations.
🟡 Conditionally Restricted: Using AI to summarize literature or translate languages (must be stated in the appendix/declaration).
🔴 Strictly Prohibited (Ethical Violation): Asking AI to write a thesis from scratch, falsifying simulation/experimental data with AI, or using AI during written exams.
But what about informatics majors, which rely heavily on coding? Now, people can easily acquire coding skills from AI?

In Informatics/Computer Science majors, the ease of obtaining code from AI has indeed drastically changed the educational landscape. However, this hasn't killed off Informatics majors, but rather completely changed the value of an Informatics graduate. Previously, the primary value of a programmer was memorizing syntax and writing code from scratch. Now, AI can do that in seconds. Shifting Focus in Informatics Education: Informatics education is currently shifting from simply "How to Code" (How to write code) to Systems Engineering & Critical Thinking (How to design and validate systems). From "Code Writer" to "Code Reviewer & Architect"AI often produces code that looks correct on the surface, but has security vulnerabilities, is memory-intensive, or is inefficient when accessed by thousands of users simultaneously. Informatics students are now required to become scientists who can distinguish AI code that simply "runs" from code that is safe, optimal, and scalable (easily developed). 2. Emphasis on Problem Solving & System LogicAI does not know what business problems actually need to be solved. Students are trained to analyze real-world needs, design database architectures, and determine complex system flows before the code is created. 3. Understanding Algorithms & FundamentalsThe code generated by AI comes from historical data. When new problems arise that have never been data on the internet, a deep understanding of computational mathematics, basic data structures, and algorithm complexity ($O(n)$) becomes a key that only educated computer scientists have. How Do Lecturers Evaluate Students Today? Because AI can easily code assignments, the way universities test Informatics students has changed: Oral & Defensive Exams (Code Defense): Students can use AI to code, but when submitted, they must be able to explain the logic line by line in front of the lecturer: "Why is this algorithm used?", "What if the website is accessed by 100,000 people simultaneously?" Live Coding Exams Without Internet: For basic courses, students are still tested on basic logic on a whiteboard or laptop without an internet connection. Advanced & Complex Projects: Assignments are no longer just "Create a simple calculator/CRUD application" (which is easy to create AI), but also create distributed systems, hardware/IoT integration, or network security. Conclusion The ease of getting code from AI has actually increased the standard for graduating Informatics. People who can only copy-paste AI code without understanding Their computing fundamentals will be easily displaced in industry. Conversely, Informatics graduates who understand in-depth theory and know how to control AI as a tool will become Super-Developers capable of completing projects five times faster than before.

Dr. -Ing. Salman, ST., MSc.
Teaching staff at the Department of Mechanical Engineering, Mataram University

Design and Development of Experimental Test Rig as a Means of Experimental Research for Scientific Publication

Dr. Ing. Salman ST MSc — Thu, 30 Jul 2026 04:21:37 +0000

Research Test Rig Design
Experimental research in mechanical engineering requires a test system (experimental test rig) capable of accurately, safely, economically, and easily controlled, representing real-world phenomena on a laboratory scale. One of the main challenges faced by researchers, particularly in universities, is the limited availability of commercial test equipment, which is generally very expensive, bulky, and difficult to modify to meet research needs. Therefore, developing an independent experimental test rig is a highly relevant alternative because it allows researchers to design a system tailored to their research objectives while simultaneously generating scientific contributions with novelty.
The first stage in test rig development is identifying the research problem to be studied. At this stage, researchers must determine the mechanical phenomena to be observed, such as vibrations due to unbalance, shaft misalignment, bearing damage, mechanical looseness, resonance, or other dynamic phenomena. Determining the research objectives serves as the basis for establishing the technical specifications of the equipment to be built, ensuring that all selected components truly support the achievement of the research objectives.
Next, a comprehensive literature review is conducted of various previous studies on experimental rigs with similar characteristics. Literature reviews are used to identify commonly used system configurations, sensor types, data acquisition methods, test parameters, and signal analysis methods. This stage is also crucial for identifying gaps in previous research, ensuring that the developed test rig design offers specific advantages, such as lower cost, a more modular system, the ability to withstand various types of damage, or integration with microcontroller-based monitoring systems.
Based on the literature review, researchers then develop a test rig design concept. This concept includes the mechanical configuration, power transmission system, bearing positions, shaft dimensions, rotor mounting method, and sensor mounting locations. At this stage, several design alternatives are typically created using CAD software such as FreeCAD, SolidWorks, Autodesk Inventor, or similar software. Three-dimensional modeling aims to evaluate dimensions, ease of assembly, sensor mounting access, and the possibility of interference between components.
The next stage is the selection of key components. The electric motor is selected based on the required rotational range during the experiment. The shaft is designed to have adequate stiffness while still being able to demonstrate dynamic characteristics under certain disturbances. Bearings are selected according to the load capacity and shaft diameter. A coupling is used to connect the motor to the shaft and reduce minor misalignments during operation. The frame is constructed of sufficiently rigid material to prevent it from dominating the system's vibration characteristics. Furthermore, the sensor mount is designed to provide good contact between the sensor and the structure, resulting in high-quality vibration data.
The fabrication phase begins after all engineering drawings are complete. This process includes cutting materials, turning shafts, drilling, welding frames, manufacturing bearing mounts, and assembling all mechanical components. During the fabrication process, dimensions are checked using precision measuring instruments to ensure compliance with the design drawings. Fabrication accuracy is crucial because dimensional deviations can affect the system's dynamic characteristics.
After assembly is complete, the instrumentation system is integrated. Accelerometer sensors are installed at predetermined measurement points, for example, on the bearing housing in the horizontal, vertical, or axial directions. The sensors are connected to a data acquisition system using a microcontroller or data acquisition (DAQ) system. For laboratory-scale research, the use of microcontrollers such as Arduino or ESP32 can be an economical solution capable of generating data with adequate sampling frequency. In addition to vibration sensors, the system can also be equipped with rotation sensors (tachometers), temperature sensors, or electric current sensors to obtain more comprehensive information about operating conditions.
The next stage is calibration of the measurement system. Calibration is performed to ensure that the sensors provide accurate and consistent measurement results. In addition, initial commissioning tests were conducted to ensure all mechanical components were functioning properly, the motor was capable of reaching the desired speed, and there were no mechanical faults that could affect the experimental results.
After the system was declared fit for use, the test rig's performance was validated. Testing began under normal, undisturbed conditions as a baseline. Subsequently, specific disturbances were introduced, such as adding an eccentric mass to produce unbalance, shifting the clutch position to produce misalignment, loosening bolts to simulate mechanical looseness, or modifying bearings to produce specific damage characteristics. Each condition was measured multiple times to ensure repeatability and reproducibility of the data.
The experimental data was then processed using signal analysis methods. Time domain analysis was used to evaluate changes in amplitude, RMS, peak value, crest factor, skewness, and kurtosis. The signal was then converted to the frequency domain using the Fast Fourier Transform (FFT) to identify the dominant frequencies. The analysis was continued using the Welch-based Power Spectral Density (PSD) method to obtain a more stable spectral energy distribution and reduce the variance of the spectrum estimates. The combination of time and frequency domain analysis allows for a more comprehensive identification of the characteristics of each damage condition.
The final stage is evaluating the performance of the experimental test rig. This evaluation is conducted by comparing the resulting signal characteristics against theory and previous research. If the dominant frequency, spectrum pattern, and amplitude changes match the theoretical characteristics of each damage condition, then the test rig can be declared successful in representing the phenomenon under study. Furthermore, an analysis of the equipment's strengths and limitations is conducted, such as operating speed range, configuration flexibility, ease of maintenance, manufacturing costs, and potential for future development.
Novelty Value for Publication
For test rig development to be worthy of publication in a reputable scientific journal, research must not only describe the equipment's manufacturing process but also demonstrate a clear scientific contribution. This contribution can include a low-cost test rig design with good measurement performance, a modular system that allows simulation of various types of damage, the integration of low-cost sensors with high data quality, a real-time data acquisition method, or the development of a new signal analysis method. Researchers must also present experimental validation demonstrating that the test rig is capable of producing vibration characteristics consistent with theory and previous research results.
Recommended Paper Structure
Scientific articles on experimental test rigs generally consist of five main sections. The introduction explains the importance of the research and existing research gaps. The methods section describes the design process, fabrication, instrumentation, experimental procedures, and data analysis methods. The results and discussion section presents the test rig's performance, test results, vibration characteristic analysis, and comparisons with previous research. The conclusion section summarizes the test rig's ability to represent the studied phenomena and its scientific contributions. With a systematic presentation and supported by valid experimental data, research on the development of experimental test rigs has a good chance of being published in accredited national journals or reputable international journals.

Author
Dr. -Ing. Salman, ST., MSc.
Lecturer in the Department of Mechanical Engineering, University of Mataram

Perpetual Motion Machines: A Myth But Still Full of Curiosity

Dr. Ing. Salman ST MSc — Sun, 21 Jun 2026 09:48:45 +0000

Perpetual energy usually refers to the idea of a device that can continuously produce energy without requiring an external energy supply. This concept is often associated with a perpetual motion machine, a machine that can run forever and even produce more energy than it consumes.
However, based on the current understanding of modern physics, a perpetual motion machine has never been successfully built and is considered impossible according to the laws of thermodynamics.

History of the Perpetual Energy Concept
Since the Middle Ages, many inventors have attempted to create machines that can rotate or run indefinitely. Some famous designs include:
A wheel with an unbalanced weight, a water wheel using permanent magnets, a system of wheels and fluids that claimed to be self-pumping, and a gravity machine that utilized mass transfer. Many of these inventions captured public attention because they promised an unlimited source of energy. However, after scientific testing, all of them failed to produce clean energy continuously.

Classification of Perpetual Motion Machines
Perpetual Motion Machines: The First Type
This machine claimed to produce more energy than it input.
Examples include generators that produce electricity without a power source. Motors that continuously rotate and produce electricity without losing energy. Such machines violate the First Law of Thermodynamics, namely the law of conservation of energy.
Mathematically:

Illustration of a magnetic perpetual-motion concept intended to achieve continuous rotation through magnetic interactions.

Energy Output > Energy Input

This is impossible because energy cannot be created from nothing.
Perpetual Motion Machine Type Two
This machine attempts to convert all heat energy into mechanical work without any energy loss. This machine violates the Second Law of Thermodynamics, which states that every real process always experiences an increase in entropy and a loss of usable energy. For example, a machine that takes heat from the surrounding air and converts it all into electricity without any waste heat.
Perpetual Motion Machine Type Three
This machine aims to eliminate all friction and resistance so that it can move forever. In practice, there is always friction. There is always air loss, and there is always material loss.
Therefore, no real system is completely free of energy loss.

Why Can't a Perpetual Motion Machine Be Built?
Friction in bearings, shafts, gears, and other mechanical components. Friction converts mechanical energy into heat. Furthermore, objects moving through the air experience drag. Kinetic energy is slowly converted to heat in the surrounding air. Electrical losses in generators and electric motors include copper losses, iron core losses, hysteresis losses, and eddy current losses. The best efficiency of modern generators is generally around 95–99%, but never 100%.

Examples of Common Perpetual Energy Claims
Permanent Magnet Generator
Many videos on the internet show generators that use only permanent magnets. The claimed principle is that the magnets attract the rotor.
The rotor continues to rotate.
The generator produces electricity continuously. The problem is that magnets can provide force, but they do not produce unlimited energy. To maintain rotation and produce output power, energy from another source is still required. A magnetic perpetual energy system is a concept that attempts to utilize the attractive and repulsive forces of permanent magnets to maintain continuous motion without requiring an external power source. In many proposed designs, magnets are strategically arranged around a rotating wheel or rotor so that magnetic interactions generate torque and keep the rotor spinning. The underlying idea is that the magnetic forces provide a constant driving effect capable of overcoming friction and other energy losses.
Proponents of magnetic perpetual energy devices often suggest that a carefully designed magnetic configuration can create an imbalance of forces, resulting in sustained rotation and continuous energy production. Such systems are frequently referred to as free-energy machines because they are intended to operate indefinitely once started. Various designs have been proposed, including magnetic wheels, magnetic motors, and hybrid systems that combine magnets with mechanical linkages.
From a scientific perspective, however, permanent magnets do not create energy; they only exert forces that can transfer or store energy within a system. According to the laws of thermodynamics and the principle of energy conservation, a machine cannot continuously produce useful energy without an external energy input. Friction, air resistance, magnetic hysteresis, and other losses eventually dissipate the available mechanical energy, causing the motion to slow down and stop. Consequently, no magnetic perpetual motion machine has been demonstrated to operate indefinitely while delivering net energy output.
Nevertheless, magnetic perpetual energy concepts continue to attract interest among inventors, researchers, and enthusiasts because they provide an intriguing platform for studying magnetism, force interactions, rotational dynamics, and innovative mechanical designs.

Magnetic Motor
This motor uses an arrangement of magnets designed to keep the rotor constantly pushed. In a complete physical analysis, the magnetic force accelerating the rotor is balanced by the resisting force on other parts of the path.
As a result, the rotor does not generate net energy.
Self-Running Generator
Typically consists of an electric motor, generator, and battery. The generator is used to recharge the battery that powers the motor. The problem is that the total efficiency is always less than 100%. For example,

ηmotor = 90%
ηgenerator = 90%

then
ηtotal = 0.9×0.9=0.81

This means that only 81% of the energy is returned, so the system eventually stops.

Are There Any Systems That Almost Appear Perpetual?
Satellites in Orbit
The International Space Station can orbit Earth for years. However, it still requires orbital corrections. It uses energy from solar panels. It doesn't generate energy from nothing.
Precision Mechanical Clocks
Some clocks can run for years. For example, clocks obtain energy from temperature changes, air pressure changes, and environmental vibrations. However, the energy still comes from the environment.
Superconductors
At very low temperatures, electric current can flow with very little loss. However, the energy is not increased. It takes a large amount of energy to maintain cryogenic temperatures.

Technologies Often Mistaken for Perpetual Energy
Solar Energy
Solar panels appear to produce energy without fuel, but their energy source is the Sun.
Wind Energy
Wind turbines harness the kinetic energy of the atmosphere.
Water Energy
Hydroelectric power plants utilize the hydrological cycle driven by the Sun.
Geothermal Energy
Utilizes the Earth's internal heat.
Nuclear Energy
Nuclear reactors utilize the binding energy of atomic nuclei. Examples include the Chernobyl Nuclear Power Plant and the Fukushima Daiichi Nuclear Power Plant. The energy produced is enormous, but it still comes from nuclear fuel, not from a perpetual motion machine.

Current Scientific Perspective
For more than two centuries, thousands of perpetual motion machine designs have been proposed. None have passed independent scientific testing and produced more energy than they input. Therefore, the scientific and engineering communities currently consider a perpetual motion machine that produces energy from nothing to be impossible. Every real system always has energy losses. All energy sources used by humans come from identifiable physical sources, such as the Sun, gravity, chemical reactions, nuclear reactions, or geothermal heat.
For a mechanical engineering researcher, the concept of perpetual energy remains interesting to study because it helps understand the basic principles of energy conservation, machine dynamics, friction, vibration, mechanical efficiency, electromagnetism, and thermodynamics. In fact, many modern engineering advances stem from efforts to improve system efficiency, although they can never achieve a truly "eternal energy" state.

Adapted from various sources
Dr. -Ing. Salman, ST., MSc.
Lecturer in the Department of Mechanical Engineering, University of Mataram