دانلود کتاب هوش مصنوعی و پیشرفت‌های مبتنی بر داده در صنعت ۴.۰، جلد اول

Industrial artificial intelligence is rapidly evolving, driven by an unprecedented explosion of diverse data modalities. In today’s smart factories and Industry 4.0 environments, vast streams of data—from machine logs and sensor readings to high-resolution imagery and acoustic signals—are fueling a new wave of data-centric innovation [1]. Organizations now collect heterogeneous datasets—ranging from natural language documents [2] and medical images [3] to sensor readings [4] and satellite imagery—to monitor, optimize, and transform industrial processes on an unprecedented scale. This surge in data availability has sparked a fundamental shift toward a data-centric AI paradigm, where enhancing data quality and coverage becomes the primary driver of performance Zha et al. [5]. Unlike traditional model-centric approaches, data-centric strategies focus on meticulously curating multi-modal datasets, refining annotations, and ensuring that the data reflect the complexity of real-world industrial environments, thereby bolstering model learning. Recent breakthroughs in deep learning are further accelerated by the rich, multisensor data now accessible in industrial settings—spanning high-resolution RGB images, LiDAR scans, hyperspectral imagery, and audio streams captured from various production sources Paheding et al. [6]. As a result, modern AI systems are beginning to leverage this wealth of multi-modal information to achieve superior generalization and even exhibit emergent capabilities once thought beyond reach. For example, large-scale multimodal models that integrate vision and language are now capable of generating detailed narratives about production processes or solving complex operational tasks without explicit instruction Baltrušaitis et al. [7]. In parallel, advancements in natural language processing (NLP), driven by scaling both models and data in large language models (LLMs), are unlocking novel behaviors, such as few-shot reasoning and chain-of-thought problem solving, which are increasingly relevant to industrial applications Xu et al. [8]. Contribution 1 introduces an innovative intelligent sensor software that combines robust machine learning with interactive heat map visualization to optimize resistance spot welding for steel reinforcement. Contribution 2 presents an artificial neural network (ANN) model that accurately predicts process performance. Contribution 3 introduces an innovative U-shaped network that integrates a Space-To-Depth module and a refined attention mechanism to effectively address class imbalance and achieve precise segmentation of micro and fine scratch defects on metal surfaces. These developments highlight how the diversity and volume of data are transforming AI techniques, establishing a robust foundation for the impactful deployment of industrial artificial intelligence across modern manufacturing and beyond. The convergence of powerful AI methods with Industry 4.0 is driving transformative changes across a wide range of application domains Rai et al. [9]. Computer vision and NLP algorithms, in particular, are now deeply embedded in sectors such as healthcare Hirsch et al. [10], manufacturing Ahmad and Rahimi [11], and cybersecurity Zolanvari et al. [12]. In healthcare, deep learning models can analyze medical images (e.g., radiographs, MRI scans, pathology slides) with accuracy approaching or even exceeding expert human performance Shen et al. [13], thus aiding in diagnostics and treatment planning. Likewise, NLP techniques are used to mine clinical texts and biomedical literature Juhn and Liu [14], enabling predictive analytics and decision support in clinical workflows. These developments reflect a broader trend in which deep learning has fundamentally reshaped how we tackle tasks in vision, language, and healthcare analytics, among other fields (Paheding et al. [6], Abramson et al. [15]).