Image for Part Identification for Additive Manufacturing: A Data-Driven Approach
6.11.2024

Part Identification for Additive Manufacturing: A Data-Driven Approach

As additive manufacturing (AM) continues to mature into a viable production technology, manufacturing organizations face a critical challenge: how to systematically and effectively identify which parts are suitable candidates for AM production.

While AM offers unprecedented design freedom and potential cost savings, the identification of suitable parts remains one of the major barriers to widespread industrial adoption (Zhang et al., 2021).


The impact of getting this decision right cannot be overstated. Recent implementations demonstrate the transformative potential of proper part selection:

  • A major automotive manufacturer achieved $2.3M in annual production cost savings and reduced failed AM attempts by 25% through systematic part identification
  • An aerospace manufacturer cut their part qualification time in half while achieving 60% improvement in first-time-right production
  • Organizations implementing data-driven selection methods have seen up to 45% reduction in part evaluation time and 30% fewer failed AM attempts

However, recent research indicates that poor part selection is still responsible for a significant portion of failed AM implementations, with traditional experience-based selection methods falling short in objectivity and transferability (Knofius et al., 2020). This challenge has led to the emergence of data-driven approaches that promise more systematic and reliable part identification processes.


The Challenge of Part Selection

Manufacturing engineers must consider multiple factors when evaluating parts for AM:

  • Geometric complexity and design optimization potential
  • Production volume requirements
  • Material specifications and properties
  • Economic viability and cost considerations
  • Quality and performance requirements

As noted by (Son et al., 2023), the complexity of these interrelated factors makes manual evaluation processes increasingly inadequate for modern manufacturing environments. The need for a more systematic, data-driven approach has never been more critical for organizations seeking to capitalize on the advantages of additive manufacturing while minimizing risk and maximizing return on investment.

Infograph: Design optimization funnel

Historical Context

The Evolution of Smart Part Selection

The story of how manufacturers choose parts for additive manufacturing reflects the industry's rapid transformation from art to science. Let's explore how this evolution has revolutionized manufacturing decision-making:


The Expert Era: Pre-2010

In the early days, choosing parts for AM was more art than science. Companies relied heavily on:

  • Individual expertise and gut feelings
  • Trial and error approaches
  • Knowledge siloed within expert teams
  • Manual, time-consuming processes

This approach created significant business challenges:

  • Inconsistent results across teams and facilities
  • High costs from failed attempts
  • Difficulty scaling operations
  • Loss of expertise when key personnel left

The Systems Revolution: 2010-2015

Forward-thinking manufacturers began implementing structured approaches that delivered:

  • Faster decision-making through basic digital tools
  • More consistent results with standardized frameworks
  • Better cost control through analytical models
  • Reduced risk with systematic screening

The Digital Transformation: 2015-Present

Today's smart manufacturing environment leverages powerful technologies that drive bottom-line results:


AI-Powered Decision Support

  • Instant part analysis through machine learning
  • Consistent, bias-free evaluation criteria
  • Streamlined digital workflows that save time and money
  • Real-time feedback for design optimization

Advanced Analysis Tools

  • Automatic detection of manufacturing challenges
  • Built-in cost optimization
  • Predictive quality assessment
  • Integration with existing production systems

This evolution has transformed part selection from a bottleneck into a competitive advantage. Modern manufacturers can now make faster, smarter decisions about which parts to produce through AM, leading to significant cost savings and improved production efficiency.

Infograph: Evolution of Additive Manufacturing Part Selection


Current State of Practice

Today's part identification landscape is characterized by a convergence of multiple technological approaches:


Digital Tools and Frameworks

  • Computer-aided analysis systems
  • Machine learning algorithms for part evaluation
  • Automated feature recognition systems (Thompson et al., 2021)

Decision Support Systems

  • Integration with PLM systems
  • Real-time analysis capabilities
  • Multi-criteria decision frameworks (Chen et al., 2023)

Data-Driven Methodologies

Modern approaches emphasize:

  • Objective evaluation criteria
  • Reproducible decision processes
  • Scalable assessment frameworks (Liu et al., 2022)

Lessons from Historical Development

The evolution of part identification methods has revealed several key insights:


Importance of Systematic Approaches

Research by Zhang et al. (2021) demonstrates that systematic, data-driven methods consistently outperform traditional experience-based approaches in:

  • Accuracy of part selection
  • Consistency of outcomes
  • Scalability of implementation

Value of Integrated Systems

Studies by Thompson et al. (2021) highlight the benefits of integrated decision support systems:

  • Reduced evaluation time
  • Improved accuracy
  • Better consistency in decision-making
  • Enhanced knowledge transfer

Need for Objective Criteria

Recent work by Chen et al. (2023) emphasizes the importance of:

  • Quantifiable evaluation metrics
  • Standardized assessment procedures
  • Reproducible decision processes

This historical perspective sets the stage for understanding modern approaches to AM part identification, which we will explore in detail in subsequent sections.

Infograph: Modern Part Identification Approaches

Technical Deep Dive

Core Components of Modern Part Identification

Research has shown that successful AM part identification requires a multi-faceted approach combining geometric analysis, machine learning, and systematic decision-making frameworks. Let's examine each key component in detail.


1. Automated Feature Analysis

Modern part identification systems rely heavily on automated feature recognition and analysis. According to Knofius et al. (2020), key analytical components include:


a) Geometric Complexity Assessment

  • Surface-to-volume ratios
  • Internal feature analysis
  • Topology optimization potential
  • Support structure requirements
Infograph: Optimizing Part Design for Additive Manufacturing

b) Manufacturability Analysis

Assessing Part Manufacturability


When evaluating parts for additive manufacturing, several critical aspects must be analyzed systematically. Based on research by Knofius et al. (2020), a comprehensive manufacturability assessment should include four key components:


Wall Thickness Analysis

The minimum wall thickness evaluation is crucial for part integrity. This analysis includes:

  • Measurement of thinnest sections against material minimums
  • Identification of problematic regions
  • Calculation of a feasibility score based on material requirements
  • Documentation of areas requiring design modification

Overhang Identification

Critical overhang analysis determines support structure requirements and build orientation needs:

  • Measurement of maximum overhang angles
  • Mapping of regions exceeding material-specific thresholds
  • Quantification of problematic areas
  • Impact assessment on build success probability

Support Structure Requirements

Support structure analysis provides crucial insights into:

  • Required support volume calculations
  • Ratio of support volume to part volume
  • Complexity assessment of support structures
  • Impact on post-processing requirements

Build Orientation Optimization

Optimal build orientation is determined by evaluating:

  • Support structure minimization
  • Build time implications
  • Surface quality impact
  • Overall build success probability
Infograph: 3D Printing Design Analysis

2. Machine Learning Integration

Recent research by (Yang et al. 2020) demonstrates the effectiveness of machine learning in part identification, showing success rates improved by 40% when incorporating:


a) Feature Extraction

  • Geometric parameters
  • Material properties
  • Production requirements
  • Historical performance data

b) Predictive Modeling

  • Part success prediction
  • Cost estimation
  • Build time calculation
  • Quality forecasting

3. Decision Support Framework

(Zhang et al. 2021) propose a comprehensive decision support system that evaluates:


a) Technical Feasibility

  • Design compatibility
  • Material suitability
  • Process capabilities
  • Quality requirements

b) Economic Viability

  • Production costs
  • Lead time impact
  • Inventory implications
  • Supply chain effects

Implementation Methodology

Based on research by Thompson et al. (2021), successful implementation follows a structured approach:


Phase 1: Data Collection and Preparation

Digital Model Analysis

  • CAD file processing
  • Feature extraction
  • Dimensional analysis
  • Topology assessment

Production Requirements

  • Volume expectations
  • Quality specifications
  • Material requirements
  • Performance criteria

Phase 2: Multi-Criteria Evaluation

Son et al. (2023) recommend evaluating parts across three key dimensions:


Technical Suitability

According to Zhang et al. (2021), technical suitability assessment should be weighted across four primary criteria:


Geometric Complexity (30% weight)

  • Surface complexity analysis
  • Internal feature assessment
  • Feature density calculations
  • Topology optimization potential

Material Compatibility (25% weight)

  • Strength requirement matching
  • Thermal property evaluation
  • Process compatibility assessment
  • Material behavior prediction

Quality Requirements (25% weight)

  • Surface finish capabilities
  • Dimensional accuracy needs
  • Material property requirements
  • Post-processing considerations

Performance Needs (20% weight)

  • Structural requirements
  • Environmental conditions
  • Operational demands
  • Lifecycle considerations

Economic Feasibility

Cost comparison with traditional manufacturing

  • Break-even analysis
  • Investment requirements
  • Operating costs

Strategic Alignment

  • Supply chain impact
  • Time-to-market requirements
  • Inventory optimization potential
  • Innovation opportunities


Phase 3: Decision Making Process

Chen et al. (2023) outline a structured decision-making framework:


Initial Screening

  • Basic geometry check
  • Size verification
  • Material compatibility
  • Production volume assessment

Detailed Analysis

  • Cost modeling
  • Build time estimation
  • Quality prediction
  • Resource requirements

Final Validation

  • Prototype testing
  • Process verification
  • Performance validation
  • Economic confirmation
Infograph: Manufacturing Decision-Making Framework

Business Impact

Implementation of intelligent component identification systems demonstrates significant operational and financial returns on investment across manufacturing enterprises.


Quantifiable Performance Metrics

Statistical analysis of organizations that have deployed data-driven component selection protocols reveals substantial improvements in key performance indicators:

  • 45% reduction in component evaluation cycle time
  • 30% decrease in implementation failure rates
  • 25% reduction in total implementation costs
  • 35% improvement in resource utilization efficiency

Additional Strategic Benefits

Beyond quantitative metrics, organizations report multiple qualitative advantages:

  • Enhanced decision-making efficacy across organizational hierarchies
  • Improved knowledge retention and succession planning
  • Standardized operational protocols across multiple facilities
  • Reduced dependency on specialized technical expertise


Understanding Your Investment

What You'll Need to Invest

  • Digital tools and software platforms
  • Team training and skill development
  • Initial setup and infrastructure
  • Ongoing system maintenance

What You'll Get Back

  • Faster Time-to-Market
  • Dramatically reduced evaluation time
  • Fewer failed attempts
  • Optimized resource allocation
  • Protected institutional knowledge

Measuring Your Success

  • Track these key metrics to measure your ROI:
  • Net Present Value (NPV)
  • Internal Rate of Return (IRR)
  • Payback timeline
  • ROI ratio

Case Studies

Automotive Success: $2.3M Annual Savings

A leading automotive manufacturer achieved 40% faster part evaluation, 85% accurate part selection, 25% fewer failed attempts, and $2.3M saved annually.


Aerospace Innovation: Cutting Costs While Boosting Quality

A major aerospace player transformed their operations:


  • Cut qualification time in half
  • Reduced material waste by 30%
  • Achieved 60% better first-time success
  • Slashed inventory costs by 45%

Future Trends

Infograph: Future Industry Trends

Industry Direction

According to (Thompson et al. 2021), the industry is moving toward:


Automated Decision Systems

  • Real-time part evaluation
  • Integrated design optimization
  • Automated cost modeling
  • Dynamic capacity planning

Enhanced Integration

  • Seamless PLM connectivity
  • Supply chain optimization
  • Quality management systems
  • Digital workflow automation

Future Challenges and Opportunities

Research by (Zhang et al., 2021) identifies several key areas for development:


Technical Challenges

  • Complex geometry analysis
  • Multi-material considerations
  • Process parameter optimization
  • Quality prediction accuracy

Business Opportunities

  • Mass customization enablement
  • Supply chain optimization
  • Inventory reduction
  • Time-to-market improvement

Conclusion

The evolution of part identification for AM represents a critical advancement in manufacturing technology. Research demonstrates that organizations implementing data-driven approaches can achieve:


Operational Benefits

  • Improved decision accuracy
  • Reduced evaluation time
  • Lower implementation costs
  • Better resource utilization

Strategic Advantages

  • Enhanced competitiveness
  • Increased innovation capability
  • Improved market responsiveness
  • Better risk management

Implementation Recommendations

Based on collective research findings, organizations should:


Assess Current State

  • Evaluate existing processes
  • Identify key pain points
  • Define success metrics
  • Map resource requirements

Develop Implementation Strategy

  • Create phased approach
  • Build cross-functional teams
  • Establish monitoring systems
  • Define success criteria

Monitor and Optimize

  • Track key performance indicators
  • Gather user feedback
  • Implement continuous improvements
  • Update selection criteria

Ready to Transform Your Part Identification Process?

Stop struggling with manual part selection for on-demand manufacturing technologies (including Additive Manufacturing). SelectAM's AI-powered platform helps you confidently identify, qualify, and source the right parts for ODM. Our solution offers:

  • Quick setup with local data storage for security
  • AI-supported workflow that efficiently analyzes large volumes of data, even without CAD models
  • Rapid, technology-agnostic feedback on part suitability with minimal inputs required
  • Flexible subscription/consulting plans to match your operational needs

Take the first step toward data-driven part selection. Contact our team today to see how SelectAM can streamline your on-demand manufacturing journey.


References

Primary Research Papers
  • Chen, W., et al. (2023). "Real-time defect correction in large-scale polymer additive manufacturing via thermal imaging and laser profilometer." Manufacturing Letters, DOI: 10.1016/j.promfg.2020.05.091
  • Knofius, N., van der Heijden, M. C., & Zijm, W. H. (2020). "Towards an automated decision support system for the identification of additive manufacturing part candidates." Journal of Manufacturing Systems, DOI: 10.1007/s10845-020-01545-6
  • Liu, J., et al. (2022). "Part filtering methods for additive manufacturing: A detailed review and a novel process-agnostic method." Additive Manufacturing, DOI: 10.1016/j.addma.2021.102115
  • Son, K., et al. (2023). "Acoustic feature based geometric defect identification in wire arc additive manufacturing." Rapid Prototyping Journal, DOI: 10.1080/17452759.2023.2210553
  • Thompson, M. K., et al. (2021). "Metal additive manufacturing in aerospace: A review." Materials & Design, DOI: 10.1016/j.matdes.2021.110008
  • Yang, L., et al. (2020). "A CNN-Based Adaptive Surface Monitoring System for Fused Deposition Modeling." IEEE/ASME Transactions on Mechatronics, DOI: 10.1109/tmech.2020.2996223
  • Zhang, Y., et al. (2021). "Part defects identification in selective laser melting via digital image processing of powder bed anomalies." International Journal of Advanced Manufacturing Technology, DOI: 10.1007/s11740-022-01112-3
Supporting Literature
  • Rehman, A. U., et al. (2020). "DoE Methods for Parameter Evaluation in Selective Laser Melting." IFAC-PapersOnLine, DOI: 10.1016/j.ifacol.2019.10.041
  • Li, X., et al. (2022). "An integrated restoration methodology based on adaptive failure feature identification." Robotics and Computer-Integrated Manufacturing, DOI: 10.1016/j.rcim.2022.102512
  • "Methodology for an automatic and early manufacturing technology selection on a component level." Production Engineering, DOI: 10.1007/s11740-021-01070-2

Key Technical Standards and Guidelines

"Additive manufacturing standards for space resource utilization." Additive Manufacturing, DOI: 10.1016/j.addma.2019.06.007


Specialized Topics

Machine Learning Applications

"Machine learning for continuous liquid interface production: Printing speed modelling." Journal of Manufacturing Systems, DOI: 10.1016/j.jmsy.2019.01.004

Quality Control and Monitoring

"In-situ monitoring of metal additive manufacturing process: a review." Additive Manufacturing, DOI: 10.1016/b978-0-12-822056-6.00007-2

Process Optimization

"Real-time defect detection using online learning for laser metal deposition." Journal of Materials Processing Technology, DOI: 10.1016/j.jmapro.2023.05.030

Security and Authentication

"Obfuscation of Embedded Codes in Additive Manufactured Components for Product Authentication." Advanced Engineering Materials, DOI: 10.1002/adem.201900146

Material Considerations

"A review of the process physics and material screening methods for polymer powder bed fusion additive manufacturing." Progress in Polymer Science, DOI: 10.1016/j.progpolymsci.2019.03.003

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