Manufacturing Assembly Operations Research Articles

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.

An LOPCOW-OPTBIAS-based Integrated Approach for Cobot Selection in Manufacturing Assembly Operations

An LOPCOW-OPTBIAS-based Integrated Approach for Cobot Selection in Manufacturing Assembly Operations

Specifying task allocation in automotive wire harness assembly stations for Human-Robot Collaboration

Wire harness assembly is normally a manual assembly process that poses ergonomic challenges. As a consequence of the rapidly expanding electrification of vehicles and transportation systems, the demand for wire harnesses can be expected to grow radically, further increasing assembly operator challenges. Thus, automating this assembly process is highly prioritised by production engineers. The rapid development of industrial robot technology has enabled more human-robot collaboration possibilities, simplifying the automation of wire harness process tasks. However, successful automation applications involving humans require efficient and safe allocation of tasks between humans and technology. Unfortunately, present assembly system design methods may be obsolete and insufficient in light of the capabilities of emerging automation technologies such as collaborative robots. This paper presents a design and specification methodology for human-centred manufacturing systems and focuses on collaborative assembly operations in complex production systems. A case study on human-robot collaboration provides an application example from a wire-harness collaborative assembly process. The proposed design methodology combines hierarchical task analysis with assessments of cognitive and physical Levels of Automation (LoAc and LoAp). The assessments are then followed by evaluations of the Levels of human-robot Collaboration (LoC) and the Levels of operator Skill requirements (LoSr) respectively. A task allocation matrix supports the identification of possible combinations of automation and collaboration solutions for a human-centred and collaborative wire harness assembly process. System designers and integrators may utilise the design and specification methodology to identify the potential and extent of human-robot collaboration in collaborative manufacturing assembly operations.

Intelligent assembly operations monitoring with the ability to detect non-value-added activities as out-of-distribution (OOD) instances

Recognition and localization of actions in manufacturing assembly operations improves productivity and product quality by identifying bottlenecks and assembly errors. In our previous work, we developed an approach that can recognize and localize the assembly standard operating procedures (SOP) steps in real-time using vision cameras. In this work, we augment the previous study with the ability to detect objects corresponding to the step being performed. Additionally, identifying non-value-added (NVA) activities in an assembly operation is challenging, hence, in this study, we propose an approach to detecting NVA activities by considering the out-of-distribution for deep learning models.

High Performance Work Strategies: Empowerment Or Repression For The Working Class?

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.6in 0pt 0.5in;"><span style="font-family: "Times New Roman","serif"; font-size: 10pt; mso-bidi-font-style: italic;">A brief overview of work organization from pre-industrial times to today is presented.<span style="mso-spacerun: yes;">  </span>The importance of lean production and just-in-time inventory (JIT) as high performance work organization techniques in manufacturing assembly operations is highlighted as important business strategies for firms competing in the global auto industry.<span style="mso-spacerun: yes;">  </span>Lean production and JIT strategies, when properly implemented, positively impact the need for manufacturing flexibility and customer demands for high quality and short delivery time.<span style="mso-spacerun: yes;">  </span>However, there is growing concern that these strategies are having an unintended and negative impact on worker well being.<span style="mso-spacerun: yes;">  </span>Recent empirical work on the lean production and JIT in auto assembly plants is presented in light of its impact on workers.<span style="mso-spacerun: yes;">  </span>In addition, an assessment is made as to whether these strategies empower or repress members of today's working class. <span style="mso-tab-count: 1;">             </span></span></p>

Case Study: Pacific Green Industries (Fiji) Limited: Pacific Palmwood Furnitures Green Approach

Pacific Green is a green furniture company producing up-market furniture made from senile coconut trees. This paper explores the unique characteristics of this company and its success in world markets. Originally operating only in Fiji, the company now sources finished wood from Fiji and carries out its manufacturing assembly operations in China. It has showrooms across the world. The paper also highlights issues which need to be further explored, including concerns from the fast growing coconut oil skin care industry in Fiji about continued availability of high quality coconut oil, since senile trees still produce at a reduced but reasonable rate and no formal replanting program is in place. Coconut oil is also being considered as a viable source of biofuel, placing further pressure on future coconut availability.

A methodology for systemic‐structural analysis and design of manual‐based manufacturing operations

AbstractThe main objective of this study was to develop a method for systemic‐structural analysis and design of manufacturing assembly operations based on the activity theory. The “activity” was defined as behavior distinctively specific to workers, associated with mobilizations and realization of conscious manufacturing goals. The fundamental units of analysis of activity are actions that are both motor and mental. Activity is considered as a complicated structure of actions that are logically ordered in space and time. Each action is comprised of different operations. The building blocks of motor actions are motions. The building blocks of cognitive actions are mental operations. Activity integrates not merely cognitive and behavioral components, but the energy components as well. The proposed methodology is illustrated using an example of two manufacturing assembly operations. © 2001 John Wiley and Sons, Inc.

Assembly-like queues with finite capacity: Bounds, asymptotics and approximations

In this paper we study a queueing model of assembly-like manufacturing operations. This study was motivated by an examination of a circuit pack testing procedure in an AT & T factory. However, the model may be representative of many manufacturing assembly operations. We assume that customers fromn classes arrive according to independent Poisson processes with the same arrival rate into a single-server queueing station where the service times are exponentially distributed. The service discipline requires that service be rendered simultaneously to a group of customers consisting of exactly one member from each class. The server is idle if there are not enough customers to form a group. There is a separate waiting area for customers belonging to the same class and the size of the waiting area is the same for all classes. Customers who arrive to find the waiting area for their class full, are lost. Performance measures of interest include blocking probability, throughput, mean queue length and mean sojourn time. Since the state space for this queueing system could be large, exact answers for even reasonable values of the parameters may not be easy to obtain. We have therefore focused on two approaches. First, we find upper and lower bounds for the mean sojourn time. From these bounds we obtain the asymptotic solutions as the arrival rate (waiting room, service rate) approaches zero (infinity). Second, for moderate values of these parameters we suggest an approximate solution method. We compare the results of our approximation against simulation results and report good correspondence.