Implementation of Lean Tools to Improve Mass Production of a Laser Cladding Process
Bibliographic record
Abstract
Laser cladding is one of the state of the art technologies in metal coating that is found advantageous over conventional welding and repairing technologies due to its innovative microstructural, metallurgical, and strength characteristics. Although various studies have been focused on implementing lean tools in different fields, there is a lack of such implementation on the cladding process. This study aims to use these tools in a laser cladding process and is intended to address the areas requiring improvement of the deadtime, with a prime focus on minimizing the waste and increasing overall equipment effectiveness. After analyzing the entire flow of the process through Value Stream Mapping, the laser cladding machine was recognized to be the bottleneck. As such, efforts were focused on minimizing the non-value-added time attached to this process. Further, the study identified the major root causes, including limited process standardization, absence of maintenance schedules, unnecessary motion, inefficient machine design, and health and safety risks. To address these major causes, a course of action was developed using Lean tools such as Ishikawa diagram, Standard Operating Procedure, Cleaning, Lubrication, Inspection, Tightening sheets, and Single-Minute Exchange of Die. These recommendations discuss improvements involving the reduction in the cycle time and increased productivity while also reducing safety risks due to high exposure to powder and temperature. It was observed that the deadtime was decreased by 41.6%, whereas the productivity increased by 20.9% after implementing the proposed recommendations on the shop floor. Furthermore, the Single-Minute Exchange of Die methodology is drafted to develop an efficient machine design that can increase productivity to about 66.6% after successful implementation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".