Artificial intelligence has become essential to any kind of systems in any field. To say some diverse examples, it might be applied to detect fire [1], to detect breast cancer on screening mammograms [2], to segment and classify beans [3] or to identify industrial superheat in aluminum electrolysis [4]. The use of Intelligent Systems improves processes so important currently like sustainability by increasing energy awareness [5] or optimization of production by tuning of combined cycle plant control [6].

Quality is nowadays a demanding requirement in all industries since traditionally it has been assumed as a key issue so as to produce a market success [7]. In this sense, highly technological solutions have been applied in order to obtain products of higher quality such as steel industry [8] or manufacturing [9].

The main goal for designing a visual inspection system is based on the fact that manual processes are carried out by an operator and involve some drawbacks like subjectivity or time-demanding, among others. In particular, manufacturing processes have adopted new techniques based on artificial intelligence improving the whole procedure.

Industrial inspection in the manufacturing industry must be quick and reliable in order to guarantee established quality standards but keeping a competitive cost. In [10], Deep Learning is applied so as to improve the visual inspection in the Printing Industry 4.0 by doing it automatically and, therefore, reducing its cost.

This paper focuses on the quality maintenance of machined pieces. Traditionally, the quality evaluation of the workpiece was carried out visually by the operator using their experience. This procedure presents several disadvantages such as subjectivity or the selection of acceptance criteria.

The use of stylus devices is another approach since they measure by contact but they are not always appropriate: the obtained accuracy sometimes is not enough as it depends on the tip radius and its setup is complex in certain situations [11]. For that reason, many works that employs computer vision techniques have been proposed in order to increase the efficiency and facilitate the set-up [12]. However, analysis of surface roughness is still a relevance problem as it is shown in recent publications that employes techniques as convolutional neural networks [11]. In addition to this, in aerospace industry and other industrial sectors, it is crucial the inspection and verification of drilled or milled inner surfaces. In this sense, Rubio et al. [13] present a 3D inspection system for drill holes analysis in aeronautical surfaces that locates the defect position. Inner surface inspection presents more complexity due to the access limitations.

This paper presents an intelligent diagnostic system based on the use of an industrial boroscope and a microscope camera which provides fast information about the status of inner and outer parts. Based on computer vision, the system is able to extract information that determines if the quality satisfies the expected standards by analyzing the roughness and texture of the surface. This novel system provides a new perspective of internal and external inspection of technological equipment and machined components, locating damages or defects of inner and outer parts.

Unlike the method presented at [14] where a single classifier was employed, the proposed system relies on a collection of individual classifiers (weak learners) that together form a stronger learner that usually outperforms the results from the weak learners [15].

The paper is organized as follows. Section 2 comments the last papers published in this field. In Section 3 an explanation of the proposed inspection method and the feature extraction stage is presented. Section 4 gives details of the considered machine learning methods and Section 5 shows the experiments that validate the proposed method. Finally, Section 6 gathers the conclusions and future works.
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