
Las herramientas de IA en línea están transformando rápidamente la ingeniería mecánica al aumentar las capacidades humanas en diseño, análisis, fabricacióny mantenimiento. Estos sistemas de IA pueden procesar grandes cantidades de datos, identificar patrones complejos y generar soluciones novedosas mucho más rápido que los métodos tradicionales. Por ejemplo, la IA puede ayudarle a optimizar el rendimiento y la fabricabilidad de los diseños, acelerar simulaciones complejas, predecir las propiedades de los materiales y automatizar una amplia gama de tareas analíticas.
The prompts provided below will for example help on generative design, accelerate simulations (FEA/CFD), help on predictive maintenance where AI analyzes sensor data from machinery to forecast potential failures, enabling proactive servicing and minimizing downtime, help on material selection and much more.
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- Análisis de causa raíz
- Ingeniería mecánica
AI Prompt to ACR comparativo para fallos repetitivos
- Mejora continua, Medidas correctoras, Diseño para la fabricación (DfM), Análisis de fallos, Manufactura esbelta, Ingeniería Mecánica, Mejora de procesos, Gestión de calidad, Análisis de causa raíz
Analiza descripciones textuales de múltiples informes de incidentes de un fallo repetitivo en un sistema mecánico. El objetivo es identificar patrones comunes, posibles causas raíz compartidas y factores diferenciadores entre incidentes que ayuden a resolver problemas persistentes. El resultado es un análisis comparativo con formato markdown.
Salida:
- Markdown
- no requiere Internet en directo
- Campos: {nombre_del_sistema_o_del_componente} {descripción_común_del_fallo} {texto_de_informes_de_incidentes_de_fallos_múltiples}
Act as a Senior Reliability Engineer conducting a Root Cause Analysis (RCA) on REPETITIVE failures.
Your TASK is to analyze the information provided in `{multiple_failure_incident_reports_text}` concerning recurring instances of '`{common_failure_description}`' affecting the `{system_or_component_name}`.
The goal is to identify common patterns
potential shared root causes
and any significant differentiating factors or unique conditions across the incidents.
The `{multiple_failure_incident_reports_text}` is a single block of text where each incident report is clearly demarcated (e.g.
by '---INCIDENT REPORT X START---' and '---INCIDENT REPORT X END---'
or user ensures separation). Each report may contain details like date
operator
specific symptoms
environmental conditions
immediate actions taken
and initial findings.
**COMPARATIVE ROOT CAUSE ANALYSIS REPORT (MUST be Markdown format):**
**1. Overview of Repetitive Failure:**
* **System/Component**: `{system_or_component_name}`
* **Common Failure Mode**: `{common_failure_description}`
* **Number of Incident Reports Analyzed**: [AI to count based on demarcations in `{multiple_failure_incident_reports_text}`]
**2. Data Extraction and Tabulation (Conceptual - AI to perform this internally):**
* For each incident report
extract key information such as:
* Incident ID/Date
* Specific symptoms observed (beyond the `{common_failure_description}`)
* Operating conditions at time of failure (load
speed
temperature
etc.)
* Environmental conditions
* Maintenance history just prior
* Operator actions or comments
* Any parts replaced or immediate fixes tried.
* *(AI should internally process this information to find patterns. A table won't be in the final output unless it's a summary table
but the AI's logic should be based on this kind of structured comparison.)*
**3. Identification of Common Patterns and Themes Across Incidents:**
* **Symptomology**: Are there consistent preceding symptoms or secondary effects noted across multiple reports before or during the `{common_failure_description}`?
* **Operating Conditions**: Do failures tend to occur under specific loads
speeds
temperatures
or during particular phases of operation (startup
shutdown
steady-state)?
* **Environmental Factors**: Is there a correlation with specific environmental conditions (e.g.
high humidity
dusty environment
specific time of day/year)?
* **Maintenance Activities**: Do failures cluster after certain maintenance activities
or if maintenance is overdue?
* **Component Batch/Supplier (if mentioned in reports)**: Is there any indication of issues related to specific batches or suppliers of the `{system_or_component_name}` or its sub-parts?
* **Human Factors**: Any patterns related to operator experience
shift changes
or specific procedures being followed/not followed?
**4. Identification of Differentiating Factors and Unique Conditions:**
* Are there any incidents that stand out as different in terms
of symptoms
conditions
or severity?
* What unique factors were present in these outlier incidents?
* Could these differences point to multiple root causes or aggravating factors for the `{common_failure_description}`?
**5. Hypothesis Generation for Potential Shared Root Cause(s):**
Based on the common patterns
propose 2-3 primary hypotheses for the underlying root cause(s) of the repetitive '`{common_failure_description}`'. For each hypothesis:
* **Hypothesis Statement**: (e.g.
'Material fatigue due to cyclic loading under X condition'
'Inadequate lubrication leading to premature wear'
'Sensor malfunction providing incorrect feedback to control system').
* **Supporting Evidence from Reports**: Briefly list the common patterns from section 3 that support this hypothesis.
**6. Recommended Next Steps for Investigation / Verification:**
* What specific data collection
tests
or analyses should be performed to confirm or refute the proposed hypotheses? Examples:
* `Detailed metallurgical analysis of failed components from multiple incidents.`
* `Targeted inspection of [specific sub-component] across all similar units.`
* `Review of design specifications vs. actual operating conditions.`
* `Interviews with operators and maintenance staff involved in the incidents.`
* `Monitoring specific parameters (e.g.
vibration
temperature) that might be precursors.`
**7. Interim Containment or Mitigation Actions (if obvious from analysis):**
* Are there any immediate actions that could be taken to potentially reduce the frequency or severity of the failures while the full RCA is ongoing
based on the patterns identified?
**IMPORTANT**: The analysis should focus on synthesizing information from MULTIPLE reports to find trends that might not be obvious from a single incident. The AI should clearly articulate the logic connecting observed patterns to potential root causes.
- Lo mejor para: Ayudar a los ingenieros mecánicos a diagnosticar fallos recurrentes del sistema mediante el análisis comparativo de múltiples informes de incidencias para identificar patrones comunes y posibles causas raíz compartidas.
- Optimización del diseño experimental
- Ingeniería mecánica
AI Prompt to Creador de la lista de validación de datos experimentales
- Ingeniería Mecánica, Seguro de calidad, Control de calidad, Gestión de calidad, Análisis estadístico, Métodos de ensayo, Validación, Verificación
Esta solicitud pide a la IA que genere una lista de comprobación detallada para validar la calidad y la integridad de los datos experimentales de ingeniería mecánica basándose en la descripción del experimento y el tipo de datos proporcionados por el usuario.
Salida:
- Markdown
- no requiere Internet en directo
- Campos: {experiment_description} {data_type}
Create a comprehensive checklist for validating the quality and integrity of experimental data in mechanical engineering. The experiment description is: {experiment_description}. The type of data collected is: {data_type}. The checklist should cover data collection methods, calibration, error sources, data consistency, and documentation practices. Format the checklist in markdown with numbered items and subpoints. Highlight critical validation steps.
- Lo mejor para: Lo mejor para garantizar una recopilación y un análisis de datos experimentales fiables y de alta calidad
- Asistencia para propuestas de subvención y redacción científica
- Ingeniería mecánica
AI Prompt to Estructura de la revisión bibliográfica para la introducción
- Fabricación aditiva, Mejora continua, Diseño para fabricación aditiva (DfAM), Innovación, Ingeniería Mecánica, Gestión de calidad, Prácticas de sostenibilidad
Ayuda a estructurar la revisión bibliográfica para la sección de introducción de un trabajo de investigación identificando los temas clave de los resúmenes proporcionados y sugiriendo un flujo lógico para establecer la brecha de investigación para un tema de ingeniería mecánica. El resultado es un esquema en formato markdown y una guía narrativa.
Salida:
- Markdown
- no requiere Internet en directo
- Campos: {título_del_tema_de_investigación} {list_of_key_abstracts_or_papers_text} {principal_falta_de_investigación_o_pregunta}
Act as a Research Methodology Advisor specializing in scientific writing for Mechanical Engineering.
Your TASK is to help structure the literature review part of an introduction section for a research paper on '`{research_topic_title}`'.
You will be given a `{list_of_key_abstracts_or_papers_text}` (a block of text containing several abstracts or summaries of key papers) and the `{main_research_gap_or_question}` the author intends to address.
Your goal is to propose a logical flow and thematic organization for the literature review that effectively leads to the stated research gap/question.
**PROPOSED LITERATURE REVIEW STRUCTURE (MUST be Markdown format):**
**Research Topic**: `{research_topic_title}`
**Stated Research Gap/Question**: `{main_research_gap_or_question}`
**I. Broad Context and Motivation (1-2 paragraphs)**
* **Guidance**: Start by establishing the general importance and relevance of the broader field related to `{research_topic_title}`.
* **Content to draw from `{list_of_key_abstracts_or_papers_text}`**: Identify abstracts that provide this wider context or highlight the significance of the area.
* **Example Phrasing**: "The field of [Broader Field of `{research_topic_title}`] has garnered significant attention due to its implications for..."
**II. Key Themes/Sub-areas from Existing Literature (organized thematically
3-5 paragraphs typically)**
* **Guidance**: Analyze the `{list_of_key_abstracts_or_papers_text}` to identify recurring themes
established findings
common methodologies
or different approaches related to `{research_topic_title}`. Group papers by these themes.
* **For each Theme/Sub-area X**:
* **A. Introduce Theme X**: Briefly state what this theme covers.
* **B. Summarize Key Contributions**: Discuss what important studies (from the provided list) have found regarding Theme X. Mention specific authors or papers if they are seminal (e.g.
"Smith et al. (Year) demonstrated...
while Jones (Year) focused on...").
* **C. Highlight Consistencies or Contradictions**: Note if findings are generally in agreement or if there are conflicting results or debates within this theme.
* **Example Themes (AI to derive from abstracts)**: Based on typical mechanical engineering topics
themes could be "Material Development for [Application]"
"Advancements in [Specific Manufacturing Process]"
"Computational Modeling of [Phenomenon]"
"Experimental Validation of [Theory/Model]"
"Limitations of Current [Technology/Approach]".
**III. Identification of a Specific Gap or Unresolved Issues (1-2 paragraphs)**
* **Guidance**: Transition from the summary of existing work to pinpointing specific limitations
unanswered questions
or underexplored areas that emerge from the reviewed literature. This section directly sets the stage for the `{main_research_gap_or_question}`.
* **Content to draw from `{list_of_key_abstracts_or_papers_text}`**: Look for phrases in abstracts like "further research is needed..."
"limitations of this study include..."
or areas where fewer studies exist.
* **Example Phasing**: "Despite these advancements
several aspects remain underexplored..." or "A critical review of the literature reveals a gap in understanding..."
**IV. Statement of Current Work and How It Addresses the Gap (1 paragraph)**
* **Guidance**: Clearly state the `{main_research_gap_or_question}` that YOUR proposed paper will address.
* Briefly outline how your paper aims to fill this gap or answer this question
linking it to the shortcomings identified in section III.
* **Example Phasing**: "Therefore
the present study aims to address this gap by investigating [your specific objective related to `{main_research_gap_or_question}`] through [your brief method]..."
**Logical Flow Summary**:
* `General Importance -> Specific Area Review (Thematic) -> Limitations/Gaps in Specific Area -> How Current Paper Fills a Specific Gap.`
**IMPORTANT**: The AI should analyze the provided `{list_of_key_abstracts_or_papers_text}` to suggest plausible themes. The structure should provide a compelling narrative that justifies the need for the research addressing the `{main_research_gap_or_question}`.
- Lo mejor para: Ayudar a los ingenieros mecánicos a estructurar la revisión bibliográfica en las introducciones de los trabajos de investigación organizando temáticamente la información de los resúmenes existentes y conduciendo lógicamente a la brecha de investigación.
- Modelización predictiva
- Ingeniería mecánica
AI Prompt to Creador de modelos de predicción de propiedades de materiales
- Aprendizaje automático, Materiales, Ingeniería Mecánica, Propiedades mecánicas, Algoritmos de mantenimiento predictivo, Control de calidad, Gestión de calidad, Análisis estadístico
Este aviso guía a la IA para construir un modelo predictivo de las propiedades mecánicas de los materiales basado en datos históricos de pruebas proporcionados por el usuario en formato CSV. Incluye los pasos de selección, entrenamiento y validación del modelo.
Salida:
- Pitón
- no requiere Internet en directo
- Campos: {csv_material_data} {target_property}
Using the following CSV data of mechanical material test results: {csv_material_data}, build a predictive model to estimate the target property: {target_property}. Follow these steps: 1) Preprocess the data (handle missing values, normalize features) 2) Select suitable modeling techniques (e.g., regression, machine learning) 3) Train the model and validate it with cross-validation 4) Output performance metrics (R², RMSE) 5) Provide the final model code snippet in Python. Respond only with the Python code and brief comments.
- Lo mejor para: Lo mejor para crear modelos basados en datos para prever el comportamiento de los materiales
- Modelización predictiva
- Ingeniería mecánica
AI Prompt to Herramienta de previsión del rendimiento del sistema
- Evaluación de Impacto Ambiental, Tecnologías medioambientales, Aprendizaje automático, Algoritmos de mantenimiento predictivo, Gestión de calidad, Análisis estadístico, Control estadístico de procesos (CEP), Diseño del sistema
Esta solicitud pide a la IA que pronostique el rendimiento futuro de un sistema mecánico basándose en datos operativos históricos y factores ambientales proporcionados en formato JSON. La IA genera una previsión de series temporales con intervalos de confianza.
Salida:
- JSON
- no requiere Internet en directo
- Campos: {historical_data_json} {factores_ambientales_json}
Given the historical operational data: {historical_data_json} and environmental factors data: {environmental_factors_json}, forecast the mechanical system's performance over the next 12 months. Use appropriate time series forecasting methods and provide confidence intervals for predictions. Structure the output as a JSON object with keys: 'month', 'predicted_performance', 'confidence_interval_lower', and 'confidence_interval_upper'. Include brief comments on model choice and assumptions.
- Lo mejor para: Lo mejor para anticipar el comportamiento del sistema mecánico en condiciones variables.
- Modelización predictiva
- Ingeniería mecánica
AI Prompt to Modelo de estimación de la probabilidad de fallo
- Análisis de fallos, Análisis de modos de fallo y efectos (FMEA), Mantenimiento, Ingeniería Mecánica, Algoritmos de mantenimiento predictivo, Análisis de riesgos, Gestión de riesgos, Análisis estadístico
Este mensaje indica a la IA que desarrolle un modelo predictivo que estime la probabilidad de fallo de los componentes mecánicos basándose en las características de entrada y en los datos históricos de fallos proporcionados en formato CSV. Incluye una explicación del modelo e instrucciones de uso.
Salida:
- Pitón
- no requiere Internet en directo
- Campos: {csv_failure_data} {component_features}
Using the provided CSV dataset of historical failures: {csv_failure_data} and the list of component features: {component_features}, build a predictive model estimating failure probability of mechanical components. Steps: 1) Data preprocessing 2) Feature importance analysis 3) Model training (e.g., logistic regression, random forest) 4) Model evaluation 5) Provide Python code with comments explaining usage. Return only the code and brief explanations.
- Lo mejor para: Lo mejor para predecir la fiabilidad de los componentes y programar el mantenimiento
- Modelización predictiva
- Ingeniería mecánica
AI Prompt to Biomechanical Response Prediction for Materials
- Biomateriales, Diseño para fabricación aditiva (DfAM), Método de los elementos finitos (MEF), Ciencia de los materiales, Ingeniería Mecánica, Propiedades mecánicas, Algoritmos de mantenimiento predictivo, Ingeniería estructural
This prompt requests the AI to predict biomechanical responses of materials under specified loading conditions. The user inputs material properties and load parameters, and the AI outputs a detailed response model.
Salida:
- LaTeX
- no requiere Internet en directo
- Fields: {material_properties} {load_conditions}
Predict the biomechanical response of a material with the following properties: {material_properties}, subjected to load conditions: {load_conditions}. Include stress-strain behavior, deformation, and failure criteria. Present the response model using LaTeX formatted equations and explanations. Highlight assumptions and boundary conditions clearly.
- Best for: Best for modeling mechanical behavior of materials under biomechanical loads
- Análisis de causa raíz
- Ingeniería mecánica
AI Prompt to Failure Root Cause Hypothesis Generator
- Mejora continua, Análisis de fallos, Análisis de modos de fallo y efectos (FMEA), Manufactura esbelta, Técnicas de resolución de problemas, Mejora de procesos, Gestión de calidad, Análisis de causa raíz, Seis Sigma
This prompt directs the AI to generate plausible root cause hypotheses for a mechanical failure event based on a detailed failure description and observed symptoms provided by the user.
Salida:
- Texto
- no requiere Internet en directo
- Fields: {failure_description} {observed_symptoms}
Analyze the following mechanical failure description: {failure_description}, along with observed symptoms: {observed_symptoms}. Generate a list of 5 plausible root cause hypotheses ranked by likelihood. For each hypothesis, provide supporting rationale and suggest diagnostic tests or inspections to confirm or rule out the cause. Format the output as a numbered list with clear headings.
- Best for: Best for initial investigation and narrowing down failure causes
- Análisis de causa raíz
- Ingeniería mecánica
AI Prompt to Fault Tree Analysis Builder
- Análisis de fallos, Análisis de modos de fallo y efectos (FMEA), Análisis del árbol de fallos (FTA), Ingeniería Mecánica, Mejora de procesos, Control de calidad, Gestión de calidad, Análisis de riesgos, Gestión de riesgos
This prompt requests the AI to construct a fault tree analysis diagram in text format for a given mechanical system failure event. The user provides the failure event description and components involved.
Salida:
- Markdown
- no requiere Internet en directo
- Fields: {failure_event} {system_components}
Construct a fault tree analysis for the mechanical failure event described as: {failure_event}. Consider the following system components: {system_components}. Present the fault tree in markdown using indentation and bullet points to represent logical AND/OR gates and failure paths. Include explanations of each branch and possible root causes. Use uppercase for failure events and lowercase for components.
- Best for: Best for visualizing failure propagation and dependencies in mechanical systems
- Análisis de causa raíz
- Ingeniería mecánica
AI Prompt to Failure Mode Prioritization Matrix
- Mejora continua, Medidas correctoras, Análisis de fallos, Análisis de modos de fallo y efectos (FMEA), Mejora de procesos, Control de calidad, Gestión de calidad, Análisis de riesgos, Gestión de riesgos
This prompt asks the AI to create a failure mode prioritization matrix based on a CSV input of failure modes, their severity, occurrence, and detection ratings. It helps prioritize root causes for mechanical failures.
Salida:
- CSV
- no requiere Internet en directo
- Fields: {csv_failure_modes}
Using the following CSV data of failure modes with columns: Failure_Mode, Severity, Occurrence, Detection: {csv_failure_modes}, calculate Risk Priority Numbers (RPN) for each mode. Sort the failure modes by decreasing RPN and generate a prioritization matrix. Output a CSV with columns: Failure_Mode, Severity, Occurrence, Detection, RPN, Priority_Rank. Provide a brief summary explaining the top 3 prioritized failure modes and recommendations for mitigation.
- Best for: Best for quantitatively prioritizing failure investigations and corrective actions
¿Estamos asumiendo que la IA siempre puede generar las mejores indicaciones en ingeniería mecánica? ¿Cómo se generan?
¿Hará la IA innecesarios a los ingenieros humanos?
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