CLADDING TECHNOLOGY SHANXI CO., LTDFuzzy Comprehensive Evaluation of Strip Cladding Forming Quality Based on Matlab-FIS
Overview and Motivation
Strip cladding, also known as submerged arc strip cladding (SASC), is a high-efficiency weld overlay technique widely adopted in the fabrication of large-scale bimetal products such as hydrogenation reactor shells, heat exchanger tubesheets, and storage tank linings. The forming quality of the cladding layer is governed by a complex interplay of process parameters including current, voltage, travel speed, strip feed rate, gas flow, and flux composition. Because the quality assessment involves multiple, often semi-quantitative criteria, traditional deterministic evaluation methods frequently fail to capture the inherent uncertainty and fuzziness in real-world manufacturing conditions. The literature under review addresses this gap by constructing a Fuzzy Inference System (FIS) in Matlab to perform a comprehensive, multi-criteria quality evaluation of strip cladding forming.
Core Methodology and Technical Framework
The study adopts a three-stage fuzzy evaluation architecture: fuzzification of input parameters, fuzzy rule-based inference, and defuzzification to produce a composite quality score. The input variables typically include geometric dimensions (cladding layer thickness, width, and surface profile), metallurgical quality indicators (bond strength, dilution rate, hardness uniformity), and surface integrity parameters (weld bead appearance, porosity level, and undercut severity). Each input variable is mapped to linguistic membership functions such as "excellent," "good," "acceptable," and "poor," with triangular or trapezoidal membership functions defined over normalized ranges.
The rule base is constructed using the Mamdani inference model, where expert knowledge from welding engineers is encoded as IF-THEN rules. For example, a rule might state: "IF dilution rate is high AND bond strength is low, THEN forming quality is poor." The system then aggregates these rules using the max-min composition method and applies the centroid method for defuzzification. The resulting composite score provides a single, interpretable quality index that can be used for real-time process monitoring and quality control decision-making.
| Evaluation Parameter | Membership Function Type | Weight in FIS | Typical Acceptance Range |
|---|---|---|---|
| Cladding thickness deviation | Triangular | 0.15 | ±0.5 mm |
| Dilution rate | Trapezoidal | 0.20 | < 15% (for SS/CS) |
| Bond strength | Triangular | 0.25 | > 250 MPa |
| Surface porosity level | Trapezoidal | 0.10 | < 1% area fraction |
| Hardness uniformity (CV%) | Triangular | 0.15 | < 10% |
| Weld bead profile quality | Trapezoidal | 0.15 | No undercut, smooth transition |
Engineering Practice Insights
From a practical standpoint, this fuzzy evaluation approach offers significant advantages over conventional pass/fail inspection criteria. In strip cladding operations, the dilution rate between the cladding strip and the base carbon steel substrate is a critical parameter that directly affects the corrosion resistance and mechanical properties of the final clad product. A dilution rate exceeding 15% for a 304 stainless steel strip on a carbon steel base can lead to sensitization and loss of corrosion resistance in the heat-affected zone. The fuzzy system allows for graded assessment rather than binary classification, which is more aligned with the reality of production environments where minor deviations are common and often acceptable.
The integration of this methodology with real-time process monitoring systems could enable closed-loop control of strip cladding operations. For instance, if the FIS score drops below a threshold during production, the system could trigger an automatic adjustment of travel speed or current to maintain quality. This represents a meaningful step toward intelligent quality control in weld overlay manufacturing, although the successful implementation requires careful calibration of the membership functions and rule base against actual production data.
Key Reflections and Limitations
The primary limitation of the Matlab-FIS approach lies in the subjectivity of defining membership functions and rule bases. Different experts may construct slightly different FIS models, leading to variations in evaluation outcomes. Additionally, the model does not inherently account for spatial variations across the cladding surface; it treats the entire cladding layer as a homogeneous entity. In practice, the edge regions of a strip cladding run often exhibit different dilution and bonding characteristics compared to the central region. Future work should consider integrating spatial mapping techniques with the fuzzy evaluation framework to provide location-specific quality assessments.
Another consideration is the validation of the FIS model against destructive testing results. The study should ideally demonstrate that the fuzzy scores correlate well with measured bond strength, intergranular corrosion resistance, and other standard-based acceptance criteria defined in codes such as ASME VIII Div.1 and NB/T 47002. Without such validation, the model's credibility in a regulatory or code-compliance context remains limited.
Summary
The Matlab-FIS-based fuzzy comprehensive evaluation method provides a structured, multi-criteria framework for assessing strip cladding forming quality that transcends the limitations of traditional binary inspection approaches. By encoding expert knowledge into fuzzy rules and leveraging the computational capabilities of Matlab, the method enables graded, holistic quality assessment that better reflects the inherent uncertainty in weld overlay manufacturing. For engineers involved in bimetal product fabrication, this approach offers a practical tool for quality control that can be integrated into production workflows, provided that the membership functions and rule bases are carefully calibrated and validated against empirical test data.