This chapter introduces the basic concepts of explainable artificial intelligence (XAI), with an emphasis on manufacturing applications. First, the definition of XAI is given. The three current development directions of XAI are also mentioned. Subsequently, the implementation process of XAI application is established, including seven steps. Then some past research and applications of XAI in manufacturing are reviewed, thereby concluding that the most commonly used fields in manufacturing include quality control, decision-making, and maintenance, with the purpose of explaining classification/estimation/optimization mechanisms and results, explaining the composition and aggregation of criteria, explaining the comparison and ranking of alternatives, and comparing the effects of inputs on the output, respectively. Difficulties in applying XAI in manufacturing are also discussed. In addition, some basic XAI techniques are also introduced, including locally interpretable model-agnostic interpretation (LIME), Shapley value (SHAP) analysis, local foil tree method, attention mechanisms, and random forest-based incremental interpretation (RFII).