We extract technical articles, manufacturing parameters, fiber properties, and machinery specifications from TextileLearner. Delivered as clean JSON, CSV, or Parquet to S3 or PostgreSQL.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Technical Articles objects from textilelearner.net. All fields typed and schema-versioned.
"article_id": "txl_8492", "title": "Process Flow Chart of Spinning", "primary_category": "Spinning", "author_name": "Mazharul Islam Kiron", "publish_date": "2025-08-14", "abstract_text": "Spinning is the twisting technique where the fiber is drawn out, twisted, and wound onto a bobbin.", "image_urls": "['https://textilelearner.net/wp-content/uploads/spinning-flow.jpg']"
| # | article_id | url | title | primary_category | sub_category | author_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Manufacturing Processes objects from textilelearner.net. All fields typed and schema-versioned.
"process_name": "Reactive Dyeing", "department": "Wet Processing", "machine_type": "Winch Dyeing Machine", "input_material": "Cotton Fabric", "temperature_c": 60.5, "chemical_agents": "['Soda Ash', 'Glauber Salt', 'Reactive Dye']", "cycle_time_mins": 120
| # | process_id | process_name | department | machine_type | input_material | output_material |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fiber Properties objects from textilelearner.net. All fields typed and schema-versioned.
"fiber_name": "Polyester", "classification": "Synthetic", "tensile_strength_g_den": 5.5, "elongation_pct": 20.0, "moisture_regain_pct": 0.4, "density_g_cm3": 1.38, "dye_affinity": "Disperse Dyes"
| # | fiber_name | classification | origin | tensile_strength_g_den | elongation_pct | moisture_regain_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Machinery Specs objects from textilelearner.net. All fields typed and schema-versioned.
"machine_name": "Ring Spinning Frame", "application_area": "Yarn Manufacturing", "production_capacity_kg_hr": 25.0, "power_consumption_kw": 45.0, "operating_speed_rpm": 22000, "automation_level": "Semi-Automatic", "maintenance_interval_hrs": 500
| # | machine_name | manufacturer_type | application_area | production_capacity_kg_hr | power_consumption_kw | dimensions_m |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Author Profiles objects from textilelearner.net. All fields typed and schema-versioned.
"author_name": "Mazharul Islam Kiron", "credentials": "B.Sc. in Textile Engineering", "university_affiliation": "Bangladesh University of Textiles", "total_articles_published": 142, "specialisation_areas": "['Apparel Merchandising', 'Wet Processing']", "linkedin_url": "https://linkedin.com/in/example"
| # | author_id | author_name | credentials | university_affiliation | total_articles_published | specialisation_areas |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
TextileLearner contains decades of unstructured engineering data. We parse articles, extract technical tables, normalise chemical formulas, and map categories into structured relational formats.
Capture title, author, publish date, category taxonomy, and full body text stripped of HTML bloat and advertisements.
Extract and structure HTML tables containing machinery specifications, process parameters, and fiber property matrices.
Identify and isolate dyeing formulations, chemical agents, and concentration percentages from wet processing articles.
Categorise records accurately across Spinning, Weaving, Knitting, Dyeing, and Garment Manufacturing sub-disciplines.
Extract URLs for process flow charts, machinery diagrams, and fabric structure images, linked directly to the parent article.
Aggregate author credentials, university affiliations, and publication history to build academic and industry expert profiles.
Identify ordered lists and step-by-step manufacturing instructions, structuring them into sequential JSON arrays.
Monitor category feeds and author pages to extract new publications and updated articles on a weekly or daily schedule.
Standardise units of measurement (e.g. converting temperatures to Celsius, weights to Kilograms) for consistent downstream analysis.
Brief in. Clean data out.
Select target categories, authors, or specific data types like machinery specs or dyeing formulas.
We configure crawlers to navigate WordPress pagination, parse unstructured text, and extract technical tables.
Schema validation ensures units of measurement and chemical formulas are accurately captured without truncation.
Clean JSON, CSV, or Parquet delivered to your S3 bucket or PostgreSQL database.
Extracting data from a content-heavy portal requires advanced parsing techniques to turn paragraphs into parameters.
Much of the valuable data exists in paragraph form. We use custom parsing logic to identify and extract process parameters, temperatures, and cycle times embedded within standard text blocks.
Technical specifications are often presented in complex, nested HTML tables. Our pipeline flattens these structures, standardises headers, and converts them into clean key-value pairs.
We map the entire site taxonomy, ensuring articles are correctly tagged with their primary and secondary disciplines, from fiber science to apparel merchandising.
We remove in-content advertisements, related post widgets, and author bios from the main text body, delivering only the core technical content.
Textile engineering uses various units (denier, tex, centigrade, fahrenheit). We standardise these metrics during extraction to ensure your database remains consistent and queryable.
AI teams ingest structured textile articles and process parameters to fine-tune industry-specific language models.
Production engineers extract process cycle times and efficiency metrics to benchmark their own facility operations.
Universities aggregate literature on specific fiber properties or dyeing techniques for material science studies.
Equipment manufacturers analyse published machine specifications to understand competitive capabilities and market standards.
Chemical suppliers extract dyeing and finishing formulations to optimise their product offerings for textile mills.
Industry analysts track publication trends across sub-categories to identify emerging technologies in technical textiles.
"TextileLearner holds decades of applied manufacturing knowledge, but extracting technical parameters from blog posts requires precise, domain-aware parsing."
Converting a content portal into a relational database is a complex parsing challenge. DataFlirt handles the extraction, table normalisation, and unit standardisation, delivering clean engineering datasets so your team can focus on material science and process optimisation rather than writing web scrapers.
Everything supported by our textilelearner.net scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles the deep crawling of taxonomies and pagination, while custom Python parsing libraries handle the extraction of tables and unstructured text blocks.
Post-extraction routines run in Pandas to standardise units, flatten nested table structures, and clean text strings before loading to the database.
Scheduled runs are managed via Apache Airflow on Kubernetes, ensuring new articles are captured and processed without manual intervention.
Data delivered to where your team already works — no new tooling required.
About textilelearner.net scraping, legality, and pipeline operations.
Ask us directly →Yes. Our parsers are designed to handle the varied HTML table structures used on TextileLearner, converting specs and formulations into clean, queryable key-value pairs.
By default, we provide the direct URLs to process flow charts and machinery diagrams. If required, we can configure the pipeline to download and store the image assets in your S3 bucket.
For a static archive extraction, it is a one-off run. For continuous monitoring, we typically schedule pipelines to check category feeds weekly or daily for new publications.
Yes. We can apply custom normalisation rules during the extraction phase to ensure all temperatures, weights, and speeds conform to your preferred metric system.
We extract the text and lists associated with dyeing and finishing processes. Complex chemical structures in image format will be captured as image URLs.
We capture the direct URL to the PDF document. Full text extraction from linked PDFs requires an additional OCR or PDF parsing module, which can be scoped upon request.
We recommend JSON for complex articles, as it allows us to nest multiple tables (e.g. machine specs and process parameters) under a single article record.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you are building an industry-specific LLM or benchmarking manufacturing processes, we build the pipeline to deliver clean data. Tell us your requirements.
