Product State

In the manufacturing process, the goal is to transform raw materials into final products. This is achieved by the application of various value-adding processes, in a bid to deliver the desired product to the consumer. The concept of adding value to the product implies that the product undergoes physical transformation. Thus, the particular operation in every manufacturing process is to execute a physical change on the product. Ostensibly, the term “product state” is used in several fields. For example, in physics, it is applied in the area of quantum systems (Wuest, 2015). In engineering, the term is accepted, although researchers use it to describe different circumstances. For utilization in the manufacturing process, the term lacks sufficient definition. Nevertheless, understanding the motivation behind these different definitions, along  with the differences in definitions, provides the basis for defining the industrial product state. In this regard, Wuest explains that one of the most commonly used definitions is the state of aggregation. Largely, this applies the simplified classification of materials as either solid, liquid or gases. This definition can help forge an understanding of the two universal aspects of most state definition, as well as  defining the product state. Of importance to note is the fact that the state is dependent on time. For example, at point A (t=0) the state of the product can be in liquid state and at point B (t=1) it changes to a solid state.

Another aspect is in the field of ICT. Herein, the term is used as a descriptive character of the state. For instance, the term is used to describe the constraint of an object during its lifecycle, where the state is active only when the constraint is true. The other approach is in the application of state with finite-state machines. In this case, the original state (t=0),coupled with the input, defines the new state (t=1) (Wuest, 2015). Apart from the two concepts used in defining product state in a manufacturing process, the state transition model is another closely-related one. Largely, the state transition model describes an existing state, in addition to the methods used to transform the original state into a new one. The model is applied in various domains, such as medicine to understand the changes in tumor growth. Along with that, the descriptive character of the state is involved in thermodynamics. Herein, the term is used to describe a situation where all variables in a system can be allocated overt numerical values, dubbed state variables. The number of these variables is important when determining the state, and depends on both the inner structures and the density of the system. Thus, the number of variables is highly dependent on the prevailing conditions. 

Based on the aspects mentioned above, product state in a manufacturing process denotes a product at a specific state or, rather, after it has undergone various state characteristics. In this case, the state characteristics are definable and ascertainable external factors that influence the change of a product state in a manufacturing program. During machining or corrosion, for instance, the state of the product descriptive character changes from point A (t=0) to point B (t=1) (Wuest, 2015). To assuage an understanding of the manufacturing process would be a consideration of the straightening of steel subsequent to treatment. Herein, the geometrical change of the steel bars occurs for many reasons. These include continued processing or easing transportation for the consumer. While the purpose is to avert any bending of the steel, residual stress can be triggered. This, in turn, engenders other problems during the resultant manufacturing process. Therefore, it is important to understand the state characteristics and their effects on both the production process and the quality of a product.

For the most part, product state as a concept provides a realistic description of the current state of a product, mainly in the manufacturing process. Every production process strives to ensure that the final product acquires all the ideal product state characteristics, otherwise dubbed “the good state” (Wuest, 2015). Failure to obtain these ideal state characteristics will result in a bad state. However, the idea of classifying everything in either black or white matter is not holistic. This owes to the imminence of shades of gray. This usually occurs where a product fails to imbibe predetermined characteristic from one point, but proceeds to acquire the rest of the characteristics at subsequent points. This only further complicates the manufacturing processing entirety, particularly when it comes to understanding and defining checkpoints. Overall, the practice is a never-ending struggle, and acquiring sufficient data from the manufacturing process can ameliorate the choice of checkpoints. This, in turn, facilitates the achievement of product quality, thereby meeting consumer demands and expectations.

ReferencesWuest, T. (2015). Approach to identify product and process state drivers in manufacturing systems using supervised machine learning. Springer Theses. Heidelberg,Berlin: Springer.