Journal of Product Design, Quality Engineering & Technology

Abstract

This report aims to place a part in a rotating indexing plate of a machine without any human assistance. In this report the part is a torque prevention nut. This report includes design and analysis of bowl feeder and magazine for conduction of nut up to the placing system. Two possible placing methods were identified. The first method includes use of a flexible strip fixed to a rigid support and a ram fixed to the power press. The second method consists of a pneumatic cylinder, 5X2 DCV, micro-controller (Arduino board), hosing and associated equipment.

Keywords: Bowl feeder, Placing systems, Flexible strip, Pneumatic cylinder

Abstract

Generative design, driven by Artificial Intelligence (AI), is transforming the way engineers and designers approach product development. Unlike traditional design methodologies, AI-driven generative product design leverages computational intelligence, machine learning, and optimization algorithms to automatically generate innovative design alternatives that meet specified performance, cost, and manufacturability constraints. This paper provides a comprehensive review of AI-based generative design, covering its methodologies, applications in mechanical, industrial, and consumer product design, as well as its challenges and future prospects. Furthermore, it explores integration with additive manufacturing, sustainable design practices, and collaborative design environments. Case studies and recent advances demonstrate the transformative potential of AI-driven generative design in reducing design cycles, enhancing creativity, and improving overall product performance.

Keywords: Generative Design, AI in Product Design, Machine Learning, Optimization, Additive Manufacturing, Computational Creativity

Abstract

Quality Assurance (QA) has traditionally relied on rule-based inspections, statistical quality control, and manual auditing processes. While effective in stable production environments, conventional QA systems often struggle to handle increasing product complexity, high variability, and rapid production cycles associated with Industry 4.0. Artificial Intelligence (AI) has emerged as a powerful enabler that augments quality assurance systems by introducing data-driven decision-making, predictive capabilities, and adaptive learning mechanisms. AI-augmented Quality Assurance systems integrate machine learning, computer vision, natural language processing, and predictive analytics to enhance defect detection, process monitoring, root cause analysis, and continuous improvement. This paper presents a comprehensive review of AI-augmented QA systems, covering their architecture, core technologies, applications across industries, benefits, limitations, and future research directions. Emphasis is placed on how AI complements rather than replaces human quality engineers, enabling proactive and intelligent quality management. The paper also discusses implementation challenges related to data quality, explainability, workforce skills, and ethical concerns. AI-augmented QA is positioned as a critical component of smart manufacturing and digital transformation strategies.

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

Abstract

Corrugated cardboard boxes manufacture in large volumes for packaging applications and it has high demand due to strength and structural stability. While studying the different industrial boxes, we note that the deformation of the boxes and the strength are weak in the load bearing capacity. In this paper all deformations and strength will be calculated by reducing the clearance and changing the size of corrugated box. This is to discover using an optimization procedure to reduce the area and specific weight of corrugated cardboard using FEA (Finite element analysis). The present study focused on optimizing the corrugated cardboard box.

Keywords:  Corrugated sheet box, finite element analysis, packaging, strength, craft paper

Abstract

Testing and verification are critical activities across software engineering, hardware design, cyber-physical systems, and product development lifecycles. Traditional testing approaches rely heavily on manually designed test cases, rule-based automation, and exhaustive verification strategies that are often time-consuming, expensive, and unable to cope with growing system complexity. In recent years, Artificial Intelligence (AI) has emerged as a transformative enabler for testing and verification automation. AI-enhanced methods leverage machine learning, deep learning, natural language processing, and search-based optimization to improve test generation, fault detection, coverage analysis, and verification efficiency. This paper presents a comprehensive review of AI-enhanced testing and verification automation, discussing its evolution, core techniques, applications across industries, benefits, challenges, and future research directions. The review highlights how AI-driven testing improves scalability, reduces human effort, and enhances reliability, while also acknowledging limitations such as data dependency, explainability issues, and trust concerns. The paper aims to serve as a reference for researchers and practitioners working in product design, quality engineering, and advanced technology domains.

Keywords: AI-driven testing, verification automation, machine learning, software quality, intelligent test generation, digital systems

Abstract

As they insist for energy is increasing people look to new technologies to make fuel consumption more efficient. People have adopted engines such as electric motor, hybrid engines and hybrid fuel cells, opposite piston engines and opposite cylinder engines. A combination of these 2 engines, opposite piston and opposite cylinder engines, will be powerful, lighter, smaller, and more efficient while delivering the same amount of power. An OPOC engine doesn’t need a valve train like that of standard four stroke engine, it doesn’t require the contributing timing parts, thereby eliminating the extra cost that the typical four stroke engine brings with it. These reports elaborate the essential aspects for the development of an OPOC engine and also describe the mechanism of an OPOC engine and the various applications of an OPOC engine.

Keywords: Energy, Consumption, OPOC, Efficient, Powerful

Abstract

The Inventory proportionality is the goal of demand-driven inventory management. The primary optimal outcome is to have the same number of days worth of inventory on hand across all products so that the time of run out of all products would be simultaneous. The secondary goal of inventory proportionality is inventory minimization. By integrating accurate demand forecasting with inventory management, replenishment inventories can be scheduled to arrive just in time to replenish the product destined to runout first, while at the same time balancing out the inventory supply of all products to make their inventories more proportional, and thereby closer to achieving the primary goal. Accurate demand forecasting also allows the desired inventory proportions to be dynamic by determining expected sales out into the future; this allows for inventory to be in proportion to expected short-term sales or consumption rather than to past averages, a much more accurate and optimal outcome.

Keywords: Inventory System, Canteen

Abstract

Manual rack and pinion steering systems are commonly used in small cars like nano and maruti800. The main aim of this paper is to create comparison between manual rack and pinion steering system and power steering system. The comparison of steering system factors like compactness, maintenance, power consumption.

Electric Power Steering for automobiles is primarily an energy saving scheme. Electric Power Steering (EPS) is a combination of electric and mechanical system, which reduces steering effort on wheel by applying the power from an electric motor to the steering system. A EPS system has been established, including full vehicle mechanical system, EPS mechanical system, and EPS electric control system. Electrical power steering will become most efficient, safe and reliable power steering system.

Keywords: Rack and Pinion steering, EPS, Mechanical system

Abstract

Spring-back is a very common and critical phenomenon in sheet metal forming operations, which is caused by the elastic redistribution of the internal stresses after the removal of deforming forces. Spring-back compensation is absolutely essential for the accurate geometry of sheet metal components. This paper reviews the various parameters affecting spring back such as punch angle, grain direction of sheet metal material, die opening, ratio of die radius to sheet thickness, sheet thickness, punch radius, punch height, coining force, pre stressed condition of strip etc.

Keywords: Springback; bending; punch angle; grain direction; die opening etc; Review

Abstract

Spring-back is a very common and critical phenomenon in sheet metal forming operations, which is caused by the elastic redistribution of the internal stresses after the removal of deforming forces. Spring-back compensation is absolutely essential for the accurate geometry of sheet metal components. This paper reviews the various parameters affecting spring back such as punch angle, grain direction of sheet metal material, die opening, ratio of die radius to sheet thickness, sheet thickness, punch radius, punch height, coining force, pre stressed condition of strip etc.

Keywords: Springback; bending; punch angle; grain direction; die opening etc; Review

Abstract

Engineering design workflows are becoming increasingly complex due to growing system requirements, multidisciplinary integration, and shortened development cycles. Traditional computer-aided design and simulation tools, while powerful, remain largely passive and require continuous human supervision. Recently, agentic artificial intelligence (AI) has emerged as a promising paradigm where autonomous or semi-autonomous AI agents actively participate in design decision-making, optimization, validation, and iteration. Unlike conventional AI models that respond to isolated prompts, agentic AI systems exhibit goal-oriented behavior, memory, tool usage, and self-reflection capabilities across the entire design lifecycle. This paper presents a comprehensive review of agentic AI applied to end-to-end engineering design workflows, covering conceptual design, modeling, simulation, optimization, verification, and lifecycle management. Key architectures, coordination mechanisms, human–AI collaboration models, and industrial case studies are discussed. The paper also highlights challenges such as trust, validation, scalability, and ethical considerations. Finally, future research directions are outlined, emphasizing the role of agentic AI in transforming engineering design from tool-driven to intent-driven workflows.

Keywords: Agentic AI, Engineering Design Automation, Multi-Agent Systems, End-to-End Design, Autonomous Engineering, AI-assisted Design

Abstract

The evolution of product engineering has been closely tied to the development of advanced materials that enhance performance, reduce weight, and improve sustainability. Advanced materials—including composites, ceramics, polymers, and metallic alloys—have revolutionized various engineering domains such as aerospace, automotive, electronics, and biomedical sectors. This paper presents a comprehensive review of the latest trends in advanced materials, their properties, and applications in product engineering. Special attention is given to nanomaterials, smart materials, and additive manufacturing techniques, which enable designers to achieve superior structural, mechanical, and functional performance. The review also highlights the challenges in material selection, cost implications, and future research directions aimed at integrating advanced materials into next-generation products.

Keywords: Advanced materials, product engineering, composites, nanomaterials, additive manufacturing, smart materials, material selection.

Author: Shubham S. Deshmukh, Vijaykumar S. Ghorade

Abstract: The solar cooling technologies applied in construction. There are three cooling technologies: absorption, adsorption and DEC –Desiccant Evaporative Cooling applied coupled with solar systems. What the stress is placed on are cooling systems of a high installed capacity of several hundred kW. Large scale solar cooling systems are not used in Poland. However, modern public buildings, especially offices with huge glazed facades very often require more energy for cooling than space heating. Solar cooling seems to be prospective technology to reduce energy consumption used by traditional air conditioning units. The development of renewable energy is on the rise worldwide because of the growing demand on energy, high oil prices, and concerns of environmental impacts. In recent years, progress on solarpowered air conditioning has increased as nowadays, air conditioning system is almost a must in every building if we want to have a good indoor comfort inside the building. Therefore, this type of system focuses in the design and construction of a direct current (DC) air conditioning system integrated with photovoltaic (PV) system which consists of PV panels, solar charger, inverter and batteries. The air conditioning system can be operated on solar and can be used in non-electrified areas. As we all known, solar energy is cost effective, renewable and environmentally friendly.

Authors: Akshay Rajesh Chaudhari, Vijaykumar S. Ghorade 

Abstract: Safety and security of life are the two most successful and booming words in the field of transport and manufacturing. The world has emerged from being a just simple form of day to day life to being aeon of mean and daring machines. Thus the safety of the people both inside and outside the vehicle is of prime concern in the car manufacturing industry. The term “Automotive Night Vision†refers to a number of systems that helps to increase driver’s awareness when it’s dark out. Thus system extends partition of driver beyond the limited reach of the headlights through the use of thermo-graphic cameras in front light heads up display and other technologies since automotive night vision can alert drivers to presence of potential hazards before they become visible, thus system can help to prevent accidents.

Authors: Sagar N. Garje, Kapil T. Patil

Abstract: For every industrial product we need packing box to store and transport from one place to another place, in that Corrugated sheet box are produced in large volume for packaging purpose an application which place high demand on its strength and structural stability. While studying boxes, it was observed that the deformation of the boxes and strength are weak in load bearing capacity. In this paper by reducing the clearance and change in dimension of actual corrugated sheet box, all the deformation and strength will be calculated. Reducing area and specific weight of corrugated board will be found using optimization procedure and FEA. The present study focused on the optimum design of the corrugated sheet box.

Author: Faisal Hussain

Abstract: This paper presents the design of an eco-friendly human powered vehicle with a compounded electric drive system. The focus has been laid on the simplicity in design, high performance, easy maintenance and safety at very reasonable prices. Most of the components have been chosen keeping in mind the easy availability and reliability. This paper provides the details of components used, and performance parameters taken into consideration while designing the vehicle. Due to rapid industrialization and development of the economy the expectation of the customer and their ability and willingness to pay for the product has changed drastically. Everyday new products are being launched and new niche markets are developing. To meet out this change and expectation and to maintain the value of the product the companies has to undergo continues process of product upgradation incorporating both technical and cosmetic changes. Major problem for the designer of the automobile is to retain its utility and continue to enhance the value, so that the product acceptability and prefer ability to competitor’s product is maintained.

Abstract

Predictive Engineering Analytics (PEA) and Performance Forecasting (PF) have emerged as transformative tools in modern engineering, combining data-driven techniques with classical engineering principles to enhance system reliability, operational efficiency, and lifecycle management. This paper reviews the state-of-the-art methodologies in PEA and PF, highlighting machine learning, digital twins, and physics-informed modeling approaches. Key applications in aerospace, manufacturing, energy, and automotive sectors are discussed, emphasizing the role of predictive analytics in minimizing downtime, optimizing maintenance schedules, and reducing operational costs. Challenges such as data quality, model interpretability, and integration with legacy systems are addressed. The paper also presents comparative analyses, illustrating how predictive analytics frameworks can guide decision-making and improve overall system performance.

Keywords: Predictive Engineering Analytics, Performance Forecasting, Machine Learning, Digital Twin, Reliability, Maintenance Optimization, Physics-Informed Modeling

Abstract

In an era characterized by rapid technological advancement and dynamic market demands, modular and upgradable product architectures have emerged as critical strategies for fostering product flexibility, sustainability, and lifecycle management. These architectures allow products to evolve over time, reduce waste, enhance customer satisfaction, and facilitate cost-effective production and maintenance. This paper provides a comprehensive review of modular and upgradable product architectures, exploring their design principles, types, benefits, challenges, and implementation strategies. A comparative analysis of traditional and modular systems is presented, alongside recent trends in industrial and consumer applications. The study emphasizes the role of modularity in promoting innovation, enabling platform-based design, and supporting sustainability goals. Recommendations for future research and practical adoption are also outlined.

Keywords: Modular architecture, Upgradable products, Product design, Sustainability, Lifecycle management, Platform strategy

Authors: Rajendra M. Galagali, R. G. Tikotkar

Abstract: Austempered Ductile Iron (ADI) is emerging as an alternative material to forged steel in many applications. ADI possesses remarkable wear resistance properties with reasonable toughness and ductility combined in one. In the present investigation, ADI was obtained by solutionizing ductile iron at 870°C for 90 minutes and austempering at 345°C for 150 minutes. The present work aims at predicting the wear behaviour of ADI by using Artificial Neural Network (ANN). A feed forward back propagation algorithm was used for the analysis. Experiments were planned using Design of experiments by Taguchi method. L9 orthogonal array was selected and experiments were conducted as per the run order generated by Taguchi for the control factors at three levels. Applied load of 19.62, 29.43, 39.24N were selected for study at speeds of 1.047, 2.095, 3.142m/s for sliding distances of 1257, 2514 and 3770m. Finally well optimized and trained neural network with LM training algorithm is used to predict the wear properties as a function of testing conditions, according to the input data sets. The results show that the predicted data are perfectly acceptable when compared to the actual experimental results. Hence a well trained artificial neural network system is expected to be very useful for estimating the weight loss of ADI under dry sliding wear test conditions at atmospheric temperature.

Authors: Pavana Kumara, G. K.Purohit

Abstract: Burnishing, being cold working, chip less post-machining, finishing process, uses a soft and hard ball or roller to deform peaks onto the valleys plastically. In current work, an attempt has been made to make use of abrasive particles in between roller burnishing tool and workpiece, to study its effect on surface properties such as surface roughness and microhardness. The experimental results of AAB (abrasives assisted burnishing) compared with dry burnishing process results to find the effectiveness of the abrasive particles. Results showed that both the cases increased hardness and surface roughness for both the materials.

 ABSTRACT

The proliferation of connected devices, commonly referred to as the Internet of Things (IoT), has transformed the landscape of technology, integrating smart systems into daily life. While these devices offer enhanced convenience, efficiency, and functionality, they also present unprecedented security challenges. Cyberattacks targeting connected devices can result in data breaches, financial losses, privacy violations, and compromise of critical infrastructure. This paper explores the importance of security-focused product design as a proactive approach to mitigate potential vulnerabilities in connected devices. It delves into best practices for secure hardware and software development, threat modeling, risk assessment, encryption techniques, and user-centric security measures. By integrating security principles at the design stage, manufacturers can ensure robust protection, user trust, and long-term device resilience. The paper also discusses regulatory standards, real-world case studies, and emerging trends in security-centric product engineering, emphasizing a holistic approach to safeguarding connected systems.

KEYWORDS: Connected devices, IoT security, threat modeling, secure design principles, encryption, risk assessment, user-centric security

ABSTRACT
Consumer product design has evolved beyond functionality and aesthetics to encompass psychological principles that influence consumer behavior, perception, and decision-making. This paper explores the key psychological factors that shape product appeal, including perception, motivation, emotional design, cognitive load, social influence, memory, and psychological ownership. By analyzing how these factors interact with consumer experiences, the study highlights the importance of integrating emotional, cognitive, and social elements into product design. The paper also discusses applications across industries such as electronics, automotive, fashion, and food, as well as challenges like cultural differences, evolving trends, and balancing aesthetics with usability. Future research avenues, including neuromarketing, AI-driven personalization, and sustainable design, are considered to enhance product innovation and consumer engagement. Understanding psychological factors in design enables brands to create products that resonate deeply with consumers, foster loyalty, and provide meaningful experiences.

KEYWORDS: Consumer psychology, Product design, Emotional design, Perception and cognition, Psychological ownership, Motivation and decision-making

Abstract

The increasing demand for personalized products has driven firms to seek innovative strategies that balance variety with cost efficiency. Modular product design has emerged as a pivotal approach to achieving mass customization, enabling companies to offer highly customized products without significantly increasing production costs. This paper explores the principles of modular design, its role in mass customization, and its impact on product development, manufacturing, and supply chain management. It further examines different modularization strategies, their implementation challenges, and benefits through case studies and illustrative examples. The paper also discusses the integration of digital technologies, such as CAD systems and AI-driven configuration tools, in enhancing modular design efficiency. Findings suggest that modular product design not only fosters customer satisfaction but also improves operational efficiency and market competitiveness.

Keywords: Modular Product Design, Mass Customization, Product Architecture, Manufacturing Flexibility, Customer-Centric Design

Authors: Dattatraya K. Nannaware, Kaveri. S. Kadam

Abstract: In gear test rig all the gears will be mounted on a plate which may be fixed or stationary as per the requirement of the measurement. In order to check the combined tooth error different types of gear testing machines are used. Various machines have its ability to check specified parameters only. Highly precise machine required special installation and space. For the purpose of checking gear in machine shop while performing machine required such an arrangement which is robust and quick one. This purpose can be solved using gear test rig. This test rig can be used in shop floor as it requires less space and operator can use it as per need without wasting much time. The test rig can be developed for different parameter as per measurement requirement. There are various test rigs which can be used for that particular designed condition.

Author: Bhosale Vijaykumar Nagnath

Abstract: When you can identify and quantify hidden process defects, you can eliminate them and move those wasted dollars to the bottom line and to investment in new opportunities to grow your business. By knowing which factors affect your process outputs and cause problems, you can take the steps to improve them. Gaining and using that knowledge is the goal of Six Sigma. Like peeling an onion, Six Sigma uncovers the layers of process variables and defects that you need to understand and control to eliminate the wasted time, effort, and materials that add to your costs but don’t add value for your customers. It’s a problem-solving technology, but it’s also a management methodology that ties process improvement directly to lowered costs, improved customer satisfaction, and higher returns on your investment of time and money in your business. Six Sigma is far more than a “quality†fad. The proof Hundreds of companies are implementing Six Sigma as you are reading this book. They are lowering their costs, improving customer satisfaction, and increasing their profit. In other words, they are getting great results. Now you can as well! ain a high-level understanding of the tools methods and application of Six Sigma Learn the basic vocabulary of Six Sigma Understand the roles and responsibilities of Six Sigma practitioners. Identify the benefits of Six Sigma as a business improvement methodology.

Abstract

Real-Time Simulation Analytics (RTSA) is emerging as a transformative approach in modern engineering, manufacturing, healthcare, and data-driven industries. By integrating real-time data with predictive simulation models, organizations can make immediate, informed decisions, optimize processes, and enhance system performance. This paper reviews the theoretical foundations of RTSA, its architecture, enabling technologies, and practical applications across industries. Key challenges, such as data latency, computational complexity, and scalability, are discussed alongside strategies for overcoming these obstacles. Case studies from manufacturing, energy, healthcare, and smart city domains demonstrate the efficacy of RTSA. Finally, the paper explores future directions and research opportunities, highlighting the potential of RTSA in AI-driven autonomous systems.

Keywords: Real-Time Simulation Analytics, Predictive Modeling, Digital Twins, Big Data, Real-Time Decision Making, Industry 4.0, Smart Systems