Author: PhD. Raúl Aguilar-Elena (GPRL, Universidad Internacional de Valencia)
Date: 2026-05-12

# ORISMA: Mapping Occupational Risk Evidence in Metal Additive Manufacturing

#### PhD. Raúl Aguilar-Elena (GPRL, Universidad Internacional de Valencia)

#### Ana Delgado-García (Universidad de Salamanca)

#### 2026-05-12

# 1 Introduction

Metal additive manufacturing (AM) – commonly known as 3D metal printing – represents one of the most rapidly expanding advanced manufacturing technologies of the 21st century. Its occupational health implications are still poorly understood, partly because the scientific literature is growing faster than the capacity of occupational health professionals to synthesise and apply it.

`orisma` (Occupational Risk Integrated Systematic Mapping and Analysis) addresses this gap by automating the complete pipeline from raw bibliographic exports to actionable risk intelligence – in a single function call.

This vignette demonstrates the full ORISMA workflow using a systematic corpus of 184 records (114 unique after deduplication) retrieved from Web of Science and Scopus on the topic of occupational health and safety in metal additive manufacturing (2015-2026).

## 1.1 What ORISMA does

ORISMA automates seven analytical steps:

1. Ingestion – reads RIS/BibTeX/CSV files from multiple databases
2. Deduplication – three-step pipeline (DOI + title + fuzzy matching)
3. Risk extraction – dictionary-based classification into 58 normative categories
4. Bibliometric analysis – computes WRDI, RCS, and MGP indicators
5. Dimension detection – automatic discovery of normative blocks (A-F)
6. Preventive intelligence – Abstract Sufficiency Score (ASS) and bridge article detection
7. Report generation – bilingual HTML reports, risk sheets, extraction matrices

## 1.2 Installation

    # From CRAN
    install.packages("orisma")

    # Development version
    remotes::install_github("Aguilar-Elena/orisma")

# 2 The three-line workflow

The entire analysis pipeline runs in three lines of R:

    library(orisma)

    refs   <- orm_load("path/to/ris/files/")
    result <- orm_run(refs, topic = "Metal additive manufacturing - OHS")
    orm_report(result, lang = "en", out_dir = "my_outputs/")

The remainder of this vignette unpacks what happens inside each step and interprets the results from the metal AM corpus.

# 3 Step 1: Loading and deduplication

## 3.1 Loading references

`orm_load()` reads all RIS, BibTeX, and CSV files from a folder, detects the source database automatically, and returns a standardised `orisma_refs` object.

    library(orisma)

    refs <- orm_load(
      path = "path/to/ris/files/",
      lang = "en"
    )

In the metal AM corpus, two files were loaded:

- `Wos.ris` – 73 records from Web of Science
- `scopusris.ris` – 111 records from Scopus
- Total: 184 records identified

## 3.2 Deduplication

`orm_dedup()` runs a three-step deduplication pipeline:

1. Exact DOI match – records with identical DOIs are deduplicated
2. Normalised title match – titles are lowercased, stripped of punctuation, and matched exactly
3. Fuzzy match – remaining potential duplicates are identified using Levenshtein distance with a configurable threshold (default: 0.90 similarity)

    # Deduplication runs automatically inside orm_run()
    # but can be called independently:
    deduped <- orm_dedup(refs, fuzzy_threshold = 0.90, verbose = TRUE)

Result for the metal AM corpus:

Step | Duplicates removed | Records remaining
--- | --- | ---
Exact DOI | 70 | 114
Normalised title | 0 | 114
Fuzzy match | 0 | 114

70 duplicates (38.0%) were removed, yielding 114 unique records. The high overlap between WoS and Scopus confirms the importance of multi-database deduplication in systematic reviews of this topic.

# 4 Step 2: Risk category extraction

## 4.1 The ORISMA dictionary

Risk categories are extracted using a built-in normative dictionary of 58 categories organised in six blocks anchored in ISO 45001:2018, INSST, NIOSH, and EU-OSHA:

    dict <- orm_dict()
    orm_dict_categories(dict)

Block | Categories | Normative anchors
--- | --- | ---
A – Safety at work | 18 | INSST / ISO 45001
B – Industrial hygiene | 8 | INSST / NIOSH
C – Ergonomics | 8 | INSST / ISO 45001
D – Psychosociology | 11 | INSST / ISO 45001
E – Biological hazards | 5 | EU-OSHA / NIOSH
F – Emerging technologies | 8 | EU-OSHA 2024-2026

The dictionary can be extended for any domain:

    # Add terms to an existing category
    dict <- orm_dict_add_terms(dict, "nanomaterials",
                               c("nano-aerosol", "NOAA particle"))

    # Add a completely new category
    dict <- orm_dict_add_category(dict,
      key      = "laser_safety",
      label_en = "Laser safety in AM processes",
      label_es = "Seguridad laser en procesos AM",
      terms    = c("laser safety", "laser hazard", "laser exposure")
    )

## 4.2 Extraction results

    mx <- orm_extract(deduped, dict = dict,
                      fields  = c("title", "abstract", "keywords"),
                      verbose = TRUE)
    print(mx)

22 of 58 categories were detected in the metal AM corpus. The top five by frequency:

Category | N records | %
--- | --- | ---
Additive manufacturing and 3D printing | 113 | 99.1%
Exposure to hazardous chemical agents | 61 | 53.5%
Nanomaterials and nanotechnology | 28 | 24.6%
Exposure to carcinogens, mutagens, reprotoxics (CMR) | 15 | 13.2%
Exposure to ionising radiation | 10 | 8.8%

Note that “Additive manufacturing and 3D printing” (Block F) dominates the corpus – this is the technological context of the corpus, not a risk category per se. ORISMA’s `orm_priority()` function automatically separates context categories from risk categories.

# 5 Step 3: The three original ORISMA indicators

## 5.1 Worker-Risk Disconnection Index (WRDI)

The WRDI measures the proportion of studies characterising an occupational risk without including direct worker exposure data. It is computed at both the category level and globally.

\[WRDI_c = 1 - \frac{N_{workers,c}}{N_{total,c}}\]

where \(N_{workers,c}\) is the number of studies in category \(c\) that include worker exposure terms (e.g. “worker exposure”, “breathing zone”, “personal sampling”, “field study”) and \(N_{total,c}\) is the total number of studies.

    result <- orm_run(refs, topic = "Metal additive manufacturing - OHS")
    cat("Global WRDI:", result$WRDI_global, "\n")

Global WRDI = 0.6053 for the metal AM corpus. This means that 60.5% of studies characterise occupational risks without including direct worker exposure data. The literature is predominantly technical – characterising emissions, particles, and processes in controlled settings – without measuring what workers actually breathe or absorb.

WRDI by category reveals important heterogeneity:

Category | WRDI | Interpretation
--- | --- | ---
Indoor air quality | 0.22 | Best connected to workers
Exposure to ionising radiation | 0.30 | Good worker data
Exposure to hazardous chemical agents | 0.34 | Moderate
Nanomaterials and nanotechnology | 0.39 | Moderate
Biological hazard - Bacteria | 1.00 | No worker data at all
Artificial intelligence and autonomous systems | 1.00 | No worker data

## 5.2 Risk Category Saturation Index (RCS)

The RCS measures the relative dominance of each risk category compared to a hypothetical uniform distribution:

\[RCS_c = \frac{N_c \cdot K}{N_{total}}\]

where \(K\) = 58 categories. RCS > 1 indicates over-representation; RCS < 1 indicates under-representation.

    ind <- result$indicators[result$indicators$n_records > 0, ]
    ind[order(-ind$RCS), c("label", "n_records", "RCS")]

The metal AM corpus shows extreme saturation:

- Additive manufacturing and 3D printing: RCS = 24.1 – massively over-represented (the dominant topic of the corpus)
- Exposure to hazardous chemical agents: RCS = 13.0 – strongly over-represented
- Most other categories: RCS < 1 – under-represented or absent

This pattern confirms that the literature has concentrated on chemical emissions while largely ignoring safety, ergonomic, psychosocial, and biological risks.

## 5.3 Material-Gap Profile (MGP)

The MGP identifies materials that are hazardous but understudied:

\[MGP_m = \frac{hazard\_proxy_m}{coverage_m}\]

    print(result$MGP)

Material | Coverage | Hazard proxy | MGP
--- | --- | --- | ---
Titanium | 2.9% | 1.33 | 45.3
Aluminium | 8.8% | 1.11 | 12.6
Nickel/Inconel | 8.8% | 0.89 | 10.1
Steel | 17.6% | 1.33 | 7.6
Others/Mixed | 61.8% | 0.89 | 1.4

Titanium has the highest MGP (45.3). Despite its known carcinogenic and respiratory hazard potential, it appears in only 2.9% of corpus studies. This represents the most critical material gap in the field.

# 6 Step 4: Automatic dimension detection

`orm_autodim()` automatically discovers the normative blocks present in the corpus without any user configuration. In “blocks” mode (default), it uses the six normative blocks (A-F) as dimensions:

    dims <- orm_autodim(result$mx, method = "blocks", verbose = TRUE)
    print(dims)

For the metal AM corpus, all six blocks are present, with Block F (Emerging technologies) dominating (n=113) followed by Block B (Industrial hygiene, n=66).

`orm_dim_matrix()` then builds a risk category x normative block cross-matrix, visualised as a hierarchical clustered heatmap:

    mat <- orm_dim_matrix(result, dims,
                          out_dir = "outputs/plots/",
                          lang    = "en")

The heatmap reveals that most studies addressing chemical agents (Block B) also address the AM technology context (Block F), while safety risks (Block A) are largely absent from the literature.

# 7 Step 5: Preventive intelligence indicators

## 7.1 Abstract Sufficiency Score (ASS)

The ASS is a cumulative 0-5 index of preventive informativeness:

    mx  <- orm_ass(result$mx, verbose = TRUE)
    orm_ass_plot(mx, lang = "en")

Distribution for the metal AM corpus (N=114):

Level | N | % | Meaning
--- | --- | --- | ---
0 | 28 | 24.6% | Non-informative for OHS
1 | 12 | 10.5% | Mentions hazard, no workplace context
2 | 27 | 23.7% | Occupational context present
3 | 24 | 21.1% | Exposure measurement reported
4 | 20 | 17.5% | Worker exposure with result
5 | 3 | 2.6% | Complete: exposure + population + method + prevention

Mean ASS = 2.04/5. Only 3 abstracts (2.6%) contain complete preventive information. Nearly one quarter (24.6%) contain no OHS-useful information at all. This quantifies the practical gap that practitioners face when consulting the primary literature.

## 7.2 Bridge article detection

    mx <- orm_bridge(result$mx, verbose = TRUE)

Bridge classification:

Type | N | %
--- | --- | ---
Strong bridge (score 4-5) | 22 | 19.3%
Partial bridge (score 3) | 4 | 3.5%
Technical study | 88 | 77.2%

77.2% of studies are purely technical – they do not simultaneously address workers, exposure measurement, AND preventive recommendations. Only 22 studies (19.3%) qualify as strong bridges between technical science and applied OHS prevention.

## 7.3 Priority reading ranking

    ranking <- orm_ranking(result$mx, top_n = 10, lang = "en")
    print(ranking)

The top-ranked article (Additive Manufacturing for Occupational Hygiene, Stefaniak et al. 2021) achieves a perfect bridge score of 5/5 and ASS of 5/5, with a combined priority score of 21. This is the single most valuable article for a practitioner entering this field.

# 8 Step 6: Practitioner outputs

## 8.1 Risk sheet

The `orm_risk_sheet()` function generates a structured, actionable risk sheet for OHS practitioners. It is regulation-neutral (applicable globally) and includes the traffic-light priority classification:

    orm_risk_sheet(result,
      topic           = "Metal additive manufacturing",
      search_strategy = "Systematic search in WoS and Scopus (2015-2026).",
      out_dir         = "outputs/",
      lang            = "en"
    )

The risk sheet:

- Separates context categories (RCS > 15, dominant topic) from actual risk categories
- Classifies each risk as RED (critical gap), AMBER (attention), GREEN (reasonable coverage), or GREY (insufficient evidence)
- Includes a full methodological section with WRDI definition, extraction method, deduplication statistics, and limitations

## 8.2 Guided extraction matrix

For systematic reviewers, `orm_extraction_matrix()` generates a pre-filled CSV template ready for full-text review:

    orm_extraction_matrix(result$mx, result,
      top_n   = 20,
      out_dir = "outputs/",
      lang    = "en"
    )

The matrix pre-fills bibliographic data, ORISMA scores, auto-detected technology, and risk categories. Reviewers only need to complete the PDF-dependent fields (study design, population, exposure level, main result, preventive recommendations).

# 9 Step 7: Validation

ORISMA’s automatic classification should be validated against manual review before publication. `orm_validate()` supports this:

    # Step 1: Generate validation sample
    val_path <- orm_validate(result$mx,
      n_sample = 30,
      out_dir  = "outputs/validation/",
      lang     = "en"
    )

    # Step 2: Open the CSV, fill in manual_* columns (0 or 1)
    # Step 3: Compute Cohen's Kappa
    kappa_results <- orm_validate(result$mx,
      validation_file = val_path,
      out_dir         = "outputs/validation/",
      lang            = "en"
    )
    print(kappa_results)

A Kappa >= 0.7 is considered acceptable for publication in high-impact OHS journals.

# 10 Summary of results

The ORISMA analysis of 114 unique studies on occupational health in metal additive manufacturing reveals:

1. High technical-preventive disconnection (WRDI = 0.61): 60.5% of studies lack direct worker exposure data. The evidence base is predominantly technical.

2. Extreme category saturation: Chemical agents (RCS = 13.0) and the AM technology context (RCS = 24.1) dominate the literature. Safety, ergonomic, and psychosocial risks are virtually absent.

3. Critical material gap: Titanium (MGP = 45.3) is the most hazardous and least-studied material. A priority target for future research.

4. Low abstract informativeness (mean ASS = 2.04/5): Most abstracts describe technical processes without conveying worker exposure data. Only 2.6% of abstracts are fully informative for OHS practitioners.

5. Few bridge articles (22/114, 19.3%): Only one in five studies connects technical characterisation with real worker data and preventive recommendations.

These findings demonstrate both the utility of ORISMA for rapid evidence mapping and the significant methodological gap in the occupational health literature on metal AM.

# 11 Session information

    sessionInfo()

# 12 References

Aguilar-Elena, R. & Delgado-Garcia, A. (2025). Mapping the Safety Landscape of Emerging Technologies: A Bibliometric Analysis of Occupational Risks in Metal Additive Manufacturing. [Under review]

International Organization for Standardization. (2018). ISO 45001:2018 Occupational health and safety management systems – Requirements with guidance for use. ISO.

National Institute for Safety and Health at Work (INSST). (2023). Clasificacion de los accidentes de trabajo por forma/contacto. Ministerio de Trabajo y Economia Social, Spain.

National Institute for Occupational Safety and Health (NIOSH). (2023). NIOSH Pocket Guide to Chemical Hazards. CDC/NIOSH.

European Agency for Safety and Health at Work (EU-OSHA). (2023). Foresight on new and emerging occupational safety and health risks. EU-OSHA.