Ergonomic redesign of an assembly workstation for heat exchanger assembly at Alfa Laval
Bibliographic record
Abstract
Work-related musculoskeletal disorders (WMSDs) represent one of the main risks in production environments, affecting both workers’ well-being and companies’ productivity. This project focuses on ergonomic improvements on a bolt assembly workstation at the company Alfa Laval. Currently, real cases of lumbar disc injuries have been reported at this workstation due to awkward postures, handling of heavy loads, and repetitive movements. The main objective of the project was to analyse ergonomic conditions of the workstation and propose a redesign that would improve workers’ well-being and increase the efficiency of the process, thereby improving occupational health and safety, as well as productivity. To achieve this, evaluations were carried out using the methods Arm Force Field (AFF), Lower Back Compression (LBC), and Rapid Entire Body Assessment (REBA), to identify the main risks present in the task. In addition, user-centred methodologies were applied, such as brainstorming sessions, direct observation in the real environment, concept generation, CAD modelling, and simulations with the Digital Human Modelling (DHM) tool IPS IMMA. This entire process made it possible to gather relevant information, define the specifications and needs of the workers, and validate improvement proposals, always with the participation of the workers, engineers and managers at Alfa Laval. The result was a redesign proposal based on ergonomic analyses and field observations, which could significantly reduce the risk of musculoskeletal injuries, improve the work environment, and increase productivity. This project demonstrates how user-centred design, and the use of ergonomic tools are key to preventing risks and improving workstation ergonomics in industrial environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".