Journal of Product Design, Quality Engineering & Technology

The rapid advancement of smart products powered by artificial intelligence, Internet of Things (IoT), machine learning, and data analytics has transformed modern lifestyles, industries, and consumer behavior. While these technologies offer enhanced convenience, personalization, and efficiency, they also introduce complex ethical challenges related to privacy, data security, transparency, inclusivity, sustainability, and user autonomy. Ethical considerations in smart product design have therefore emerged as a critical area of concern for designers, engineers, managers, and policymakers. This paper examines the key ethical dimensions associated with smart product design, focusing on responsible innovation, human-centered design, and trust-building mechanisms. The study highlights major ethical risks, explores design principles for ethical smart systems, and emphasizes the role of designers in embedding ethical values throughout the product lifecycle. Through conceptual analysis and illustrative examples, the paper aims to provide a structured understanding of how ethical responsibility can coexist with technological innovation in smart product development.

KEYWORDS: Smart Products, Ethical Design, User Privacy, Responsible Innovation, Product Design Engineering

Abstract

In recent years, product and system design has moved beyond purely functional and technical considerations to include emotional and psychological aspects of users. Emotional and affective design focuses on understanding how users feel when they interact with products, services, or systems, and how these feelings influence satisfaction, usability, and long-term acceptance. Among various affective design approaches, Kansei Engineering has emerged as a structured and widely adopted methodology that translates user emotions and impressions into concrete design parameters. This review paper presents a comprehensive discussion on emotional and affective design, with special emphasis on Kansei Engineering principles, methods, tools, and applications. The paper reviews the evolution of affective design, key theoretical foundations, data collection and analysis techniques, and integration with modern technologies such as artificial intelligence and digital product development. Case examples from consumer products, automotive design, and human–computer interaction are also discussed. The challenges, limitations, and future research directions in emotional design are highlighted. The study aims to provide researchers and practitioners with a consolidated understanding of affective design methodologies and their role in creating emotionally resonant products.

Keywords: Emotional design, Affective design, Kansei Engineering, User experience, Product design, Human-centered design

Abstract

Artificial Intelligence (AI) is increasingly influencing product engineering, enhancing design efficiency, predictive maintenance, and performance optimization. However, the adoption of AI in engineering products introduces complex ethical challenges related to accountability, transparency, bias, and social impact. This paper critically examines the ethical implications of AI applications in product engineering. Key areas analyzed include decision-making autonomy, data privacy, bias in AI algorithms, sustainability, and the potential displacement of human roles. The study integrates case studies from automotive, consumer electronics, and industrial automation sectors to illustrate practical concerns. By reviewing existing ethical frameworks and proposing best practices, this paper aims to guide engineers, designers, and policymakers toward responsible AI integration in product engineering.

Keywords: AI ethics, product engineering, responsible AI, bias, transparency, sustainability, human-AI collaboration.

Abstract

The rapid technological evolution in engineering and design fields has made it imperative to cultivate future-ready designers and engineers. Traditional educational systems often focus on theoretical knowledge and standardized curricula, which may not adequately prepare students for the complex, multidisciplinary, and fast-changing professional landscape. This paper explores modern educational frameworks aimed at producing adaptable, innovative, and technologically proficient professionals. Key components, including interdisciplinary learning, project-based education, digital literacy, and industry-academia collaboration, are analyzed. Furthermore, the role of emerging technologies, lifelong learning, and sustainability-focused design in shaping curricula is discussed. Case studies, frameworks, and practical guidelines are provided to aid educational institutions in implementing future-ready programs. The paper concludes with recommendations for policymakers and educators to foster a new generation of designers and engineers capable of addressing contemporary global challenges.

Keywords: Future-ready education, engineering curriculum, design thinking, interdisciplinary learning, digital literacy, lifelong learning, educational frameworks.

Author:- Kajol Pathak, Vineet Choudhary, Sagar Bansal

Abstract:- This article sought to use Desmet's explanation of Product Emotion (CPE) Model to the 'Evolution of Emotional Development (EED)' in order to clarify the link between design and emotion. This study comes up with a figure that illustrates the link between Versions in EED and Desmet's CPE. In Desmet's paradigm, Version 3 in EED may be split into social and interest emotions.

Users can get emotional satisfaction through interacting with other people (social emotions) or interacting with things (product emotions) (interest emotions).

Keywords:-  Emotional experience, emotional satisfaction, mate design, emotion, product design.

Abstract

Artificial Intelligence (AI) has become an important support tool in modern design processes, influencing decisions related to form generation, material selection, usability evaluation, and performance optimization. However, many AI-driven design systems operate as black boxes, offering limited insight into how and why specific decisions are produced. This lack of transparency reduces designer trust, makes validation difficult, and raises ethical and accountability concerns. Explainable Artificial Intelligence (XAI) aims to address these challenges by providing interpretable and understandable explanations for AI outputs. This paper presents a comprehensive review of Explainable AI in the context of design decision-making. It discusses the fundamentals of XAI, its relevance to design disciplines, commonly used explanation techniques, and application scenarios across product design, engineering design, and user experience design. The paper also highlights benefits, limitations, and future research directions of XAI-enabled design systems. The review shows that integrating XAI into design workflows can improve trust, collaboration, and informed decision-making, while supporting responsible and human-centered use of AI technologies.

Keywords: Explainable AI, design decisions, human-centered design, interpretable models, decision support systems

Author:- R. S. Kumaravelan, C. Sathish Gandhi, P. Ramesh, L.  Venkatesan

Abstract:- Model making, often known as prototyping, is an essential stage in the product design and development process that aids in the conception of a design. Rapid prototyping (RP) is a method of layer-by-layer material deposition that became popular in the early 1980s as computer-aided design and manufacturing (CAD/CAM) technology grew rapidly. The edges and surfaces of a complicated solid model, as well as their information, are utilised to define a product that is then CNC machined into a completed product. For different applications of RP models, this article offers a better platform for researchers, new learners, and product makers. As a result, it raises public knowledge of the rapidly evolving RP manufacturing technique in product design, development, and applications.

Keywords:-Prototyping, layer-by-layer, CAD/CAM, product design

Author:- Gunjan Tiwari

Abstract:-  For a new product modelling decision process, the current and future problems of a new product design, forecasting, and risk management launch strategy The purpose of this study is to propose and investigate the creation of a low-cost integrated CAD-CAPP-CAD/CAM product modelling system for the design and production of a suggested product. It's a connection between numerous design phases, such as functional, technological, and physical design. The drafting of a product to be maintained with the drafting software package is the first step in the modelling data generating process. The data is transmitted from the CAD drawing to be utilised as product models, and a CAPP software programme will then produce the operational parameters for the product's manufacture. These process data are sent to a CAM software programme, which generates the automated data processing processes. The function's last stage is to assist design and production activities, which may have garnered significant cost savings in terms of basic equipment and software.

Keywords:- product modelling; open sources CAD, Computer Numerical Control (CNC), modular design;, reason maintenance

 

Authors: Shivika, Renu, Arti Saxsena

Abstract: Stir casting is an appealing processing method for manufacturing MMCs among the various composite materials processing methods. It is reasonably priced and provides a diverse range of materials and processing options. The stir casting method includes introducing pre-treated or warmed ceramic particles into the spinning impeller's molten alloy vortex. In this technique, the distribution of the reinforcement material in the matrix should be homogeneous, and the wet ability between the molten matrix and reinforcement particles should be adjusted in order to produce the best characteristics of the metal matrix composite. This technique may achieve appropriate integration by carefully selecting process parameters such as pouring temperature, stirring speed, reinforcement preheating temperature, carrier gas usage, and so on. Controlling usually undesired characteristics such as porosity caused by gas entrapment during mixing and oxide inclusions is critical to the effectiveness of stir casting. The current research focuses on the design of various forms of mechanical stirrers and their effects on vortex generation and dispersion, as well as the usage of a carrier gas such as Argon.

Keywords: Stir Casting, Reinforcement Preheating Temperature, Mechanical Stirrers, Spinning Impeller

Abstract

Human error remains one of the most persistent contributors to accidents, quality failures, and inefficiencies across industrial systems. Traditional safety approaches often rely on training, procedures, and post-incident corrective actions, which may not adequately address the root causes of human error. Prevention in Design (PtD) offers a proactive approach by integrating safety, usability, and error prevention strategies directly into the design phase of products, processes, and systems. This review paper examines the role of PtD in reducing human error by focusing on design-based prevention rather than behavioral correction. The paper discusses theoretical foundations of human error, classifications of error types, and the evolution of PtD principles. Key design strategies such as error-proofing, ergonomics, human-centered design, and system simplification are analyzed. Industrial case examples from manufacturing, healthcare, construction, and transportation sectors are reviewed to demonstrate practical implementation. Challenges, limitations, and future research directions related to digital design tools and Industry 4.0 integration are also discussed. The study concludes that human error reduction through PtD is not only feasible but essential for achieving sustainable safety and quality performance in complex socio-technical systems.

Keywords: Human error, Prevention in Design, PtD, error-proofing, human-centered design, safety engineering

Authors: Jai Kishor, Narendra Kumar, Deepak Tyagi

Abstract: With the advancement of technology, the amount of effort required to perform any type of labour has been steadily reducing. By implementing superior designs, the work necessary to get the target output can be reduced effectively and inexpensively. Power screws are used to convert rotary motion into translatory motion. A screw jack is an example of a power screw in which a small force applied in a horizontal plane is used to raise or lower a large load. The principle on which it works is similar to that of an inclined plane. The ratio of the load applied to the effort applied is the mechanical advantage of a screw jack. A lead screw is used to operate the screw jack. The jack's height is modified by turning a lead screw, which can be done either manually or electrically. The jack (manually operated) that is still supplied as standard equipment with most new cars is familiar to most people. In the repair and maintenance of automobiles, it is frequently essential to raise the vehicle in order to change a tyre or gain access to the vehicle's undesirables. As a result, a number of car jacks have been invented to lift a vehicle from the ground. As a result, the screw jack is powered by electricity and driven by a D.C. motor to reduce human effort.

Keywords: Lead screw, screw jack, DC Motor

Author: Aadars.M. S.

Abstract: Fire hazards have been an issue in industrial as well as commercial sectors for years. Fire suppression systems equipped with water mist spray techniques are implemented in these buildings to avoid or prevent these property damages and human health hazards. But this water mist suppression system is less effective when collateral damage is considered. If we can reduce the fire suppression time, we will be able to decrease the loss. In order to enhance the performance of the fire suppression system, a new additive can be added to the system. So, this experiment deals with the calculation and comparison of fire suppression time of water mist and the mixture. A calculated proportion of the organic solvent mixture is added along with water mist. As we increase the proportion, the fire suppression time of the pool fire rapidly declines and then slightly increases at some point. Our aim is to find the point at which the fire suppression time is minimum for different organic mixtures. From this experiment, we can compare the fire suppression time of different organic solvents mixed with water mist.

Authors: Juhi Singh, Geetika Tyagi

Abstract: The aim of the Automatic Saw Cutting venture is to cut metals and woods without the need for human intervention. A self-weight is connected to the responding device to provide the required descending power for the hacksaw sharp edge to cut the work piece. When a piece is cut, a breaking point switch would automatically give a signal to stop the engine immediately, which the microcontroller would sense. If more pieces need to be removed, we repeat the process until the predetermined number of work-pieces has been cut.A pneumatic barrel or a seat savvy is utilized for holding the work-piece when cutting task is going on.

Keywords: Power hacksaws, designing, automatic saw cutting

Abstract

Human-centric and empathetic design methods have gained significant importance in recent years due to the increasing complexity of products, services, and socio-technical systems. Traditional design approaches often prioritize functionality, cost, and performance, while neglecting human emotions, cultural contexts, and lived experiences. This review paper explores the evolution, principles, and methodologies of human-centric and empathetic design, highlighting their role in improving usability, inclusivity, and user satisfaction. The paper discusses key tools such as empathy mapping, user journey mapping, participatory design, and co-creation practices. It also examines the integration of empathy into digital product design, healthcare systems, and social innovation. Challenges, limitations, and future research directions are presented to guide designers and researchers toward more responsible and meaningful design outcomes.

Keywords: Human-centric design, Empathetic design, User experience, Design thinking, Inclusive design

Abstract

Design creativity and user experience (UX) are important parts of modern product development. Traditional design methods mostly focus on aesthetics, usability and engineering performance, but recent research shows that understanding how the human brain works can strongly improve design outcomes. Neurocognitive approaches combine neuroscience, psychology and design thinking to study how users perceive, feel and interact with products. Brain responses, cognitive load, emotional reactions and decision making patterns all influence how a user experiences a design. Tools such as EEG, eye tracking, fMRI and biometric sensors help designers to understand hidden user reactions that surveys or interviews may fail to capture. This paper reviews the neurocognitive foundations of creativity, the role of brain processes in UX, and how these insights are applied in product design. It also discusses experimental tools, applications in digital interfaces and physical products, and future challenges. The integration of neurocognition into design practices provides more human-centered, emotionally engaging and innovative design solutions.

Keywords: Neurocognition, Design Creativity, User Experience, Brain-Computer Interaction, Cognitive Load, Emotional Design, EEG, Eye Tracking, Human-Centered Design

Abstract

Model-Driven Engineering (MDE) has emerged as a powerful approach to software and systems development, emphasizing abstraction through models as primary artifacts. While MDE enhances productivity and maintainability, ensuring the quality of both models and generated artifacts remains a critical challenge. Quality Engineering (QE) offers systematic practices, metrics, and validation techniques to guarantee product reliability, performance, and conformance to requirements. This paper reviews the integration of QE principles into MDE, exploring strategies for automated testing, model validation, metrics-based assessment, and continuous quality assurance. It also examines case studies from industry applications, identifies challenges, and proposes future research directions for enhancing model-driven quality assurance. The synthesis highlights that incorporating QE into MDE significantly improves software reliability, reduces defects, and facilitates maintainable, scalable systems.

Keywords: Model-Driven Engineering, Quality Engineering, Model Validation, Software Quality, Metrics, Continuous Quality Assurance

Abstract

Product design today is not only about performance, aesthetics, and cost effectiveness, but also about ethical responsibility, transparency, and social accountability. Designers and engineers are increasingly expected to consider how products influence users, society, and environment. Ethical product design ensures that products do not harm users physically, psychologically, or socially. Transparent design focuses on clear communication about how products function, how data is used, and what impact they create. Responsible product design extends toward sustainability, inclusivity, and long-term societal benefit. This paper reviews the core concepts, principles, and practical approaches to ethical, transparent, and responsible product design. It discusses issues like dark patterns, privacy concerns, inclusive design, sustainability practices, and regulatory frameworks. Case examples from digital, consumer, and industrial products are discussed to show how design decisions affect trust and user wellbeing. Finally, the paper outlines challenges and future directions for integrating ethics into product development processes.

Keywords: Ethical design, Responsible engineering, Transparency, Sustainable product design, Inclusive design, Privacy, Dark patterns, Product lifecycle, User trust

Abstract

Digital prototyping has emerged as a transformative approach in modern product development, offering a virtual platform to design, test, and optimize products before physical manufacturing. Integrating digital prototyping with defect reduction techniques enables engineers and designers to anticipate potential issues, minimize production errors, and improve product quality and reliability. This paper explores the principles, tools, methodologies, and applications of digital prototyping in various industries, with a particular focus on defect detection and reduction strategies. Case studies from automotive, aerospace, and electronics industries highlight the practical benefits and challenges of adopting digital prototyping. The research further examines the role of simulation, virtual testing, and predictive analytics in enhancing product quality. The findings suggest that organizations adopting these strategies achieve faster time-to-market, lower costs, and improved customer satisfaction.

Keywords: Digital prototyping, defect reduction, virtual testing, simulation, predictive analytics, product quality, CAD, CAE.

Abstract

Design management plays a very important role in connecting creativity with systematic engineering practices in modern product development. With fast changing customer needs and shorter product life cycles, organizations are increasingly adopting agile methods to manage design and development activities. Agile product development strategies allow teams to work in iterative cycles, encourage collaboration, and reduce time-to-market. This paper reviews the relationship between design management principles and agile methodologies in product engineering. It discusses how design thinking, cross-functional coordination, lean practices, and agile frameworks like Scrum and Kanban are applied in managing product design effectively. The study also explains the challenges faced while integrating design management with agile workflows and suggests practical solutions. Tables and conceptual models are included to show stages, tools, and comparisons. The paper concludes that a balanced integration of design management and agile practices improves innovation, flexibility, and product quality in competitive markets.

Keywords: Design Management, Agile Development, Product Design, Scrum, Kanban, Lean Design, Cross-functional Teams, Iterative Prototyping, Innovation Strategy, Quality Engineering

Abstract

Sustainable and circular product design has emerged as a critical approach to mitigating environmental impacts, promoting resource efficiency, and extending the lifecycle of products. This review examines the principles, methodologies, and challenges associated with sustainable and circular design, with an emphasis on material selection, life cycle assessment, and product end-of-life strategies. Key frameworks such as the Cradle-to-Cradle (C2C) model, Life Cycle Thinking (LCT), and Design for Disassembly (DfD) are discussed. The paper also explores industry case studies and technological enablers, including digital twin simulation and additive manufacturing, which support circularity. The review concludes by highlighting future research directions to integrate sustainability deeply into mainstream product development processes.

Keywords: Circular Economy, Sustainable Design, Life Cycle Assessment, Cradle-to-Cradle, Design for Disassembly, Resource Efficiency.

Abstract

The rapid advancement of automation and artificial intelligence (AI) has transformed traditional industrial inspection processes. Smart inspection systems powered by computer vision (CV) are emerging as pivotal tools for enhancing accuracy, efficiency, and reliability in quality control and monitoring tasks. This paper reviews the principles, methodologies, and applications of computer vision-based inspection systems in manufacturing, healthcare, infrastructure monitoring, and logistics. It also discusses challenges, including data acquisition, computational complexity, and real-time processing, while providing insights into emerging trends such as deep learning-based defect detection and edge AI integration. By synthesizing recent research, this paper highlights how smart inspection systems improve operational efficiency, reduce human error, and pave the way for fully automated inspection environments.

Keywords: Computer vision, smart inspection, industrial automation, deep learning, defect detection, quality control, real-time monitoring.

Abstract

The advent of Industry 4.0 has revolutionized manufacturing processes, giving rise to Quality 4.0—a paradigm shift in quality management that leverages Artificial Intelligence (AI) and Machine Learning (ML) for intelligent, predictive, and real-time quality control. Traditional quality management practices relying on manual inspection and statistical process control are increasingly inadequate in handling complex, high-speed production environments. This review explores the integration of AI and ML into quality control systems, covering predictive quality, real-time defect detection, anomaly prediction, and process optimization. Furthermore, it discusses the technological enablers, challenges, and future directions for Quality 4.0, emphasizing how smart quality management can enhance efficiency, reduce waste, and improve customer satisfaction.

Keywords: Quality 4.0, Industry 4.0, Artificial Intelligence, Machine Learning, Predictive Quality, Quality Control, Smart Manufacturing

Abstract

In modern industries, equipment reliability and operational efficiency are critical for competitive advantage. Predictive maintenance (PdM) and reliability engineering optimization have emerged as key strategies to enhance equipment uptime, reduce maintenance costs, and prevent catastrophic failures. PdM leverages advanced data analytics, machine learning, and condition monitoring to forecast potential failures before they occur, allowing proactive maintenance interventions. Reliability engineering complements this by optimizing system design, components’ lifespan, and maintenance schedules. This review paper explores the evolution of predictive maintenance strategies, advanced diagnostic tools, reliability-centered approaches, and optimization techniques that improve maintenance decision-making. Various case studies from manufacturing, aerospace, and energy sectors are discussed. The paper also highlights the integration of Industry 4.0 technologies such as IoT, big data, and artificial intelligence in predictive maintenance. Challenges, future directions, and practical implications for industrial applications are also presented.

Keywords: Predictive Maintenance, Reliability Engineering, Optimization, Condition Monitoring, Machine Learning, Industry 4.0

Abstract

Hyper-personalization is emerging as a crucial strategy for modern consumer product companies to drive engagement, loyalty, and revenue growth. By leveraging big data, artificial intelligence (AI), and advanced analytics, businesses can offer individualized experiences, recommendations, and products tailored to the preferences of each customer. This paper reviews the concept of hyper-personalization, its technological enablers, implementation strategies, and challenges. It also examines its impact on consumer behavior and business performance across different industries. Furthermore, the study explores emerging trends, such as AI-driven design, real-time personalization, and predictive consumer insights. Finally, the paper highlights key considerations for effective adoption, including ethical concerns, data privacy, and operational integration.

Keywords: Hyper-personalization, consumer behavior, AI-driven marketing, product customization, predictive analytics, data privacy.

Abstract

Human-centered and inclusive design practices prioritize the needs, abilities, and contexts of end-users throughout the design process. By emphasizing empathy, accessibility, and equity, these practices aim to produce solutions that are both functional and socially responsible. This paper provides a comprehensive review of the principles, methods, and challenges associated with human-centered and inclusive design. It explores frameworks for implementation, case studies across technology, healthcare, and urban environments, and the integration of participatory design techniques. Furthermore, the paper highlights emerging trends such as digital inclusivity, adaptive technologies, and AI-driven personalization. The review concludes by discussing gaps in current practice and proposing strategies to enhance inclusivity and usability in design.

Keywords: Human-centered design, inclusive design, accessibility, participatory design, user experience, equity